From 505e027ea65512dbf193853579b9d110668e9bb7 Mon Sep 17 00:00:00 2001
From: ju40268 <ju40268@gmail.com>
Date: Mon, 20 Feb 2023 17:40:33 -0800
Subject: [PATCH 01/14] basic late fusion cooeprative mode

---
 .../core/application/coperception/__init__.py |   0
 opencda/core/common/cav_world.py              |   8 +-
 opencda/core/common/rsu_manager.py            |   5 +-
 opencda/core/common/vehicle_manager.py        |  15 +-
 .../sensing/perception/coperception_libs.py   | 197 +++++++++++++++++
 .../core/sensing/perception/o3d_lidar_libs.py |  62 +++++-
 .../sensing/perception/perception_manager.py  |  89 +++++++-
 opencda/customize/ml_libs/opencood_manager.py | 105 +++++++++
 .../scenario_testing/config_yaml/copy.yaml    | 205 ++++++++++++++++++
 .../config_yaml/v2xp_online_carla.yaml        | 205 ++++++++++++++++++
 opencda/scenario_testing/v2xp_online_carla.py |  96 ++++++++
 11 files changed, 971 insertions(+), 16 deletions(-)
 rename __init__.py => opencda/core/application/coperception/__init__.py (100%)
 create mode 100644 opencda/core/sensing/perception/coperception_libs.py
 create mode 100644 opencda/customize/ml_libs/opencood_manager.py
 create mode 100644 opencda/scenario_testing/config_yaml/copy.yaml
 create mode 100644 opencda/scenario_testing/config_yaml/v2xp_online_carla.yaml
 create mode 100644 opencda/scenario_testing/v2xp_online_carla.py

diff --git a/__init__.py b/opencda/core/application/coperception/__init__.py
similarity index 100%
rename from __init__.py
rename to opencda/core/application/coperception/__init__.py
diff --git a/opencda/core/common/cav_world.py b/opencda/core/common/cav_world.py
index 794bab8f..e03eab24 100644
--- a/opencda/core/common/cav_world.py
+++ b/opencda/core/common/cav_world.py
@@ -43,7 +43,7 @@ def __init__(self, apply_ml=False):
         self._platooning_dict = {}
         self._rsu_manager_dict = {}
         self.ml_manager = None
-
+        """
         if apply_ml:
             # we import in this way so the user don't need to install ml
             # packages unless they require to
@@ -51,7 +51,11 @@ def __init__(self, apply_ml=False):
                 "opencda.customize.ml_libs.ml_manager"), 'MLManager')
             # initialize the ml manager to load the DL/ML models into memory
             self.ml_manager = ml_manager()
-
+        """
+        if apply_ml:
+            ml_manager = getattr(importlib.import_module(
+                "opencda.customize.ml_libs.opencood_manager"), 'OpenCOODManager')
+            self.ml_manager = ml_manager()
         # this is used only when co-simulation activated.
         self.sumo2carla_ids = {}
 
diff --git a/opencda/core/common/rsu_manager.py b/opencda/core/common/rsu_manager.py
index 94f99508..64e26cb9 100644
--- a/opencda/core/common/rsu_manager.py
+++ b/opencda/core/common/rsu_manager.py
@@ -80,7 +80,10 @@ def __init__(
                                              sensing_config['localization'],
                                              self.carla_map)
         # perception module
-        self.perception_manager = PerceptionManager(vehicle=None,
+        self.perception_manager = PerceptionManager(v2x_manager=None,
+                                                    localization_manager=None,
+                                                    behavior_agent=None,
+                                                    vehicle=None,
                                                     config_yaml=sensing_config['perception'],
                                                     cav_world=cav_world,
                                                     carla_world=carla_world,
diff --git a/opencda/core/common/vehicle_manager.py b/opencda/core/common/vehicle_manager.py
index 99a15afa..b0f461e2 100644
--- a/opencda/core/common/vehicle_manager.py
+++ b/opencda/core/common/vehicle_manager.py
@@ -99,10 +99,6 @@ def __init__(
         # localization module
         self.localizer = LocalizationManager(
             vehicle, sensing_config['localization'], carla_map)
-        # perception module
-        self.perception_manager = PerceptionManager(
-            vehicle, sensing_config['perception'], cav_world,
-            data_dumping)
         # map manager
         self.map_manager = MapManager(vehicle,
                                       carla_map,
@@ -132,6 +128,17 @@ def __init__(
         else:
             self.data_dumper = None
 
+        # perception module
+        # move it down here to pass in the behavior manager & localization manager
+        self.perception_manager = PerceptionManager(
+            v2x_manager=self.v2x_manager,
+            localization_manager=self.localizer,
+            behavior_agent=self.agent,
+            vehicle=vehicle,
+            config_yaml=sensing_config['perception'],
+            cav_world=cav_world,
+            data_dump=data_dumping)
+
         cav_world.update_vehicle_manager(self)
 
     def set_destination(
diff --git a/opencda/core/sensing/perception/coperception_libs.py b/opencda/core/sensing/perception/coperception_libs.py
new file mode 100644
index 00000000..e5a68df6
--- /dev/null
+++ b/opencda/core/sensing/perception/coperception_libs.py
@@ -0,0 +1,197 @@
+import numpy as np
+
+from opencda.core.common.misc import get_speed
+import opencda.core.sensing.perception.sensor_transformation as st
+from opencood.utils.transformation_utils import x1_to_x2
+
+
+class CoperceptionLibs:
+    def __init__(self, lidar, rgb_camera, localization_manager, behavior_agent):
+        self.lidar = lidar
+        self.rgb_camera = rgb_camera
+        self.localizer = localization_manager
+        self.agent = behavior_agent
+        self.time_delay = self.calculate_time_delay()
+
+    @staticmethod
+    def matrix2list(matrix):
+        assert len(matrix.shape) == 2
+        return matrix.tolist()
+
+    def calculate_time_delay(self):
+        """
+        TODO: needs revision to reflect the delay
+        """
+        return 0
+
+    def load_vehicles(self, objects):
+        data = {}
+        if 'vehicles' in objects:
+            vehicle_list = objects['vehicles']
+            for v in vehicle_list:
+                veh_carla_id = v.carla_id
+                veh_pos = v.get_transform()
+                veh_bbx = v.bounding_box
+                veh_speed = get_speed(v)
+
+                data.update({
+                    veh_carla_id: {
+                        'bp_id': v.type_id,
+                        'color': v.color,
+                        "location": [veh_pos.location.x,
+                                     veh_pos.location.y,
+                                     veh_pos.location.z],
+                        "center": [veh_bbx.location.x,
+                                   veh_bbx.location.y,
+                                   veh_bbx.location.z],
+                        "angle": [veh_pos.rotation.roll,
+                                  veh_pos.rotation.yaw,
+                                  veh_pos.rotation.pitch],
+                        "extent": [veh_bbx.extent.x,
+                                   veh_bbx.extent.y,
+                                   veh_bbx.extent.z],
+                        "speed": veh_speed
+                }})
+
+        return {
+            'vehicles': data
+        }
+
+    def load_transformation_matrix(self, is_ego, data):
+        """
+        TODO: needs revision to reflect the cur/delay params
+        """
+        cur_params = data
+        delay_params = data
+        cur_ego_params = data
+        delay_ego_params = data
+
+        delay_cav_lidar_pose = delay_params['lidar_pose']
+        delay_ego_lidar_pose = delay_ego_params["lidar_pose"]
+        cur_ego_lidar_pose = cur_ego_params['lidar_pose']
+        cur_cav_lidar_pose = cur_params['lidar_pose']
+
+        """"
+        TODO: needs to take in the loc_error_flag
+        if cav_content['ego'] and self.loc_err_flag:
+            delay_cav_lidar_pose = self.add_loc_noise(delay_cav_lidar_pose,
+                                                      self.xyz_noise_std,
+                                                      self.ryp_noise_std)
+            cur_cav_lidar_pose = self.add_loc_noise(cur_cav_lidar_pose,
+                                                    self.xyz_noise_std,
+                                                    self.ryp_noise_std)
+        """
+        if is_ego:
+            transformation_matrix = x1_to_x2(delay_cav_lidar_pose, cur_ego_lidar_pose)
+            spatial_correction_matrix = np.eye(4)
+        else:
+            transformation_matrix = x1_to_x2(delay_cav_lidar_pose, delay_ego_lidar_pose)
+            spatial_correction_matrix = x1_to_x2(delay_ego_lidar_pose, cur_ego_lidar_pose)
+        gt_transformation_matrix = x1_to_x2(cur_cav_lidar_pose, cur_ego_lidar_pose)
+        return {
+            'transformation_matrix': transformation_matrix,
+            'gt_transformation_matrx': gt_transformation_matrix,
+            'spatial_correction_matrix': spatial_correction_matrix
+        }
+
+    def load_cur_lidar_pose(self):
+        lidar_transformation = self.lidar.sensor.get_transform()
+        return {
+            'lidar_pose': [
+                lidar_transformation.location.x,
+                lidar_transformation.location.y,
+                lidar_transformation.location.z,
+                lidar_transformation.rotation.roll,
+                lidar_transformation.rotation.yaw,
+                lidar_transformation.rotation.pitch
+            ]
+        }
+
+    def load_camera_data(self):
+        data = {}
+        for (i, camera) in enumerate(self.rgb_camera):
+            camera_param = {}
+            camera_transformation = camera.sensor.get_transform()
+            camera_param.update({'cords': [
+                camera_transformation.location.x,
+                camera_transformation.location.y,
+                camera_transformation.location.z,
+                camera_transformation.rotation.roll,
+                camera_transformation.rotation.yaw,
+                camera_transformation.rotation.pitch
+            ]})
+
+            # dump intrinsic matrix
+            camera_intrinsic = st.get_camera_intrinsic(camera.sensor)
+            camera_intrinsic = self.matrix2list(camera_intrinsic)
+            camera_param.update({'intrinsic': camera_intrinsic})
+
+            # dump extrinsic matrix lidar2camera
+            lidar2world = \
+                st.x_to_world_transformation(self.lidar.sensor.get_transform())
+            camera2world = \
+                st.x_to_world_transformation(camera.sensor.get_transform())
+
+            world2camera = np.linalg.inv(camera2world)
+            lidar2camera = np.dot(world2camera, lidar2world)
+            lidar2camera = self.matrix2list(lidar2camera)
+            camera_param.update({'extrinsic': lidar2camera})
+            data.update({'camera%d' % i: camera_param})
+
+        print(f"CoperceptionLibs.load_camera_data: {data}.")
+        return data
+
+    def load_ego_data(self):
+        data = {
+            'predicted_ego_pos': [],
+            'true_ego_pos': [],
+            'ego_speed': 0.0
+        }
+        if not self.localizer:
+            return data
+
+        predicted_ego_pos = self.localizer.get_ego_pos()
+        true_ego_pos = self.localizer.vehicle.get_transform() \
+            if hasattr(self.localizer, 'vehicle') \
+            else self.localizer.true_ego_pos
+        data = {
+            'predicted_ego_pos': [
+                predicted_ego_pos.location.x,
+                predicted_ego_pos.location.y,
+                predicted_ego_pos.location.z,
+                predicted_ego_pos.rotation.roll,
+                predicted_ego_pos.rotation.yaw,
+                predicted_ego_pos.rotation.pitch
+            ],
+            'true_ego_pos': [
+                true_ego_pos.location.x,
+                true_ego_pos.location.y,
+                true_ego_pos.location.z,
+                true_ego_pos.rotation.roll,
+                true_ego_pos.rotation.yaw,
+                true_ego_pos.rotation.pitch
+            ],
+            'ego_speed': float(self.localizer.get_ego_spd())
+        }
+        print(f"CoperceptionLibs.load_ego_data: {data}")
+        return data
+
+    def load_plan_trajectory(self):
+        data = {'RSU': True}
+        if self.agent is not None:
+            trajectory_deque = \
+                self.agent.get_local_planner().get_trajectory()
+            trajectory_list = []
+
+            for i in range(len(trajectory_deque)):
+                buffer = trajectory_deque.popleft()
+                x = buffer[0].location.x
+                y = buffer[0].location.y
+                speed = buffer[1]
+                trajectory_list.append([x, y, speed])
+
+            data = {
+                'plan_trajectory': trajectory_list,
+                'RSU': False
+            }
+        return data
diff --git a/opencda/core/sensing/perception/o3d_lidar_libs.py b/opencda/core/sensing/perception/o3d_lidar_libs.py
index 36253738..31986498 100644
--- a/opencda/core/sensing/perception/o3d_lidar_libs.py
+++ b/opencda/core/sensing/perception/o3d_lidar_libs.py
@@ -20,6 +20,8 @@
     is_vehicle_cococlass, ObstacleVehicle
 from opencda.core.sensing.perception.static_obstacle import StaticObstacle
 
+from opencood.visualization.vis_utils import bbx2oabb
+
 VIRIDIS = np.array(cm.get_cmap('plasma').colors)
 VID_RANGE = np.linspace(0.0, 1.0, VIRIDIS.shape[0])
 LABEL_COLORS = np.array([
@@ -98,8 +100,8 @@ def o3d_visualizer_init(actor_id):
     """
     vis = o3d.visualization.Visualizer()
     vis.create_window(window_name=str(actor_id),
-                      width=480,
-                      height=320,
+                      width=960,
+                      height=640,
                       left=480,
                       top=270)
     vis.get_render_option().background_color = [0.05, 0.05, 0.05]
@@ -158,6 +160,62 @@ def o3d_visualizer_show(vis, count, point_cloud, objects):
             vis.remove_geometry(aabb)
 
 
+def o3d_visualizer_show_coperception(vis, count, point_cloud, pred_bbx, objects):
+    """
+    Visualize the point cloud at runtime.
+
+    Parameters
+    ----------
+    vis : o3d.Visualizer
+        Visualization interface.
+
+    count : int
+        Current step since simulation started.
+
+    point_cloud : o3d.PointCloud
+        Open3d point cloud.
+
+    objects : dict
+        The dictionary containing objects.
+
+    Returns
+    -------
+
+    """
+
+    if count == 2:
+        vis.add_geometry(point_cloud)
+
+    oabbs_pred = bbx2oabb(pred_bbx, color=(1, 0, 0))
+    for ele in oabbs_pred:
+        vis.add_geometry(ele)
+
+    vis.update_geometry(point_cloud)
+
+    for key, object_list in objects.items():
+        # we only draw vehicles for now
+        if key != 'vehicles':
+            continue
+        for object_ in object_list:
+            aabb = object_.o3d_bbx
+            vis.add_geometry(aabb)
+
+    vis.poll_events()
+    vis.update_renderer()
+    # # This can fix Open3D jittering issues:
+    time.sleep(0.001)
+
+    for key, object_list in objects.items():
+        if key != 'vehicles':
+            continue
+        for object_ in object_list:
+            aabb = object_.o3d_bbx
+            vis.remove_geometry(aabb)
+
+    for ele in oabbs_pred:
+        vis.remove_geometry(ele)
+
+
 def o3d_camera_lidar_fusion(objects,
                             yolo_bbx,
                             lidar_3d,
diff --git a/opencda/core/sensing/perception/perception_manager.py b/opencda/core/sensing/perception/perception_manager.py
index ecc777a9..9b0100fd 100644
--- a/opencda/core/sensing/perception/perception_manager.py
+++ b/opencda/core/sensing/perception/perception_manager.py
@@ -8,7 +8,6 @@
 
 import weakref
 import sys
-import time
 
 import carla
 import cv2
@@ -23,7 +22,10 @@
 from opencda.core.sensing.perception.static_obstacle import TrafficLight
 from opencda.core.sensing.perception.o3d_lidar_libs import \
     o3d_visualizer_init, o3d_pointcloud_encode, o3d_visualizer_show, \
-    o3d_camera_lidar_fusion
+    o3d_camera_lidar_fusion, o3d_visualizer_show_coperception
+
+from opencda.core.sensing.perception.coperception_libs import CoperceptionLibs
+from collections import OrderedDict
 
 
 class CameraSensor:
@@ -374,7 +376,7 @@ class PerceptionManager:
         Open3d point cloud visualizer.
     """
 
-    def __init__(self, vehicle, config_yaml, cav_world,
+    def __init__(self, v2x_manager, localization_manager, behavior_agent, vehicle, config_yaml, cav_world,
                  data_dump=False, carla_world=None, infra_id=None):
         self.vehicle = vehicle
         self.carla_world = carla_world if carla_world is not None \
@@ -387,6 +389,11 @@ def __init__(self, vehicle, config_yaml, cav_world,
         self.lidar_visualize = config_yaml['lidar_visualize']
         self.global_position = config_yaml['global_position'] \
             if 'global_position' in config_yaml else None
+        self.cooperative = config_yaml['cooperative'] \
+            if 'cooperative' in config_yaml else False
+        self.v2x_manager = v2x_manager
+        self.localization_manager = localization_manager
+        self.behavior_agent = behavior_agent
 
         self.cav_world = weakref.ref(cav_world)()
         ml_manager = cav_world.ml_manager
@@ -444,6 +451,14 @@ def __init__(self, vehicle, config_yaml, cav_world,
         # the dictionary contains all objects
         self.objects = {}
 
+        # coperception libs
+        self.coperception_libs = CoperceptionLibs(
+            lidar=self.lidar,
+            rgb_camera=self.rgb_camera,
+            localization_manager=self.localization_manager,
+            behavior_agent=self.behavior_agent
+        )
+
     def dist(self, a):
         """
         A fast method to retrieve the obstacle distance the ego
@@ -478,19 +493,79 @@ def detect(self, ego_pos):
         """
         self.ego_pos = ego_pos
 
-        objects = {'vehicles': [],
-                   'traffic_lights': []}
+        objects = {
+            'vehicles': [],
+            'traffic_lights': []
+        }
 
         if not self.activate:
             objects = self.deactivate_mode(objects)
 
         else:
-            objects = self.activate_mode(objects)
-
+            if self.cooperative:
+                objects = self.coperception_mode(objects)
+            else:
+                objects = self.activate_mode(objects)
         self.count += 1
 
         return objects
 
+    def coperception_mode(self, objects):
+        """
+            Use OpenCOOD to detect objects
+        """
+        data = OrderedDict()
+        cav_id = str(self.id)
+        data[cav_id] = OrderedDict()
+        # TODO: subject to changes later on?
+        data[cav_id]['ego'] = True  # assume itself is ego
+        data[cav_id]['time_delay'] = self.coperception_libs.time_delay
+        # gather calculated data
+        data[cav_id]['params'] = {}
+        camera_data = self.coperception_libs.load_camera_data()
+        ego_data = self.coperception_libs.load_ego_data()
+        plan_trajectory_data = self.coperception_libs.load_plan_trajectory()
+        lidar_pose_data = self.coperception_libs.load_cur_lidar_pose()
+        vehicles = self.coperception_libs.load_vehicles(objects)
+        # update dic
+        data[cav_id]['params'].update(plan_trajectory_data)
+        data[cav_id]['params'].update(camera_data)
+        data[cav_id]['params'].update(ego_data)
+        data[cav_id]['params'].update(lidar_pose_data)
+        data[cav_id]['params'].update(vehicles)
+
+        transformation_matrix = self.coperception_libs.load_transformation_matrix(data[cav_id]['ego'],
+                                                                                  data[cav_id]['params'])
+        data[cav_id]['params'].update(transformation_matrix)
+
+        if self.lidar.data is None: return objects
+
+        data[cav_id].update({'lidar_np': self.lidar.data})
+
+        reformat_data_dict = self.ml_manager.opencood_dataset.get_item_test(data)
+        output_dict = self.ml_manager.opencood_dataset.collate_batch_test(
+            [reformat_data_dict])  # should have batch size dim
+        batch_data = self.ml_manager.to_device(output_dict)
+        pred_box_tensor, pred_score, gt_box_tensor = self.ml_manager.inference(batch_data)
+        # self.ml_manager.show_vis(pred_box_tensor, gt_box_tensor, batch_data)
+
+        if self.lidar_visualize:
+            while self.lidar.data is None:
+                continue
+            o3d_pointcloud_encode(self.lidar.data, self.lidar.o3d_pointcloud)
+            o3d_visualizer_show_coperception(
+                self.o3d_vis,
+                self.count,
+                self.lidar.o3d_pointcloud,
+                pred_box_tensor,
+                # gt_box_tensor,
+                objects)
+        # add traffic light
+        objects = self.retrieve_traffic_lights(objects)
+        self.objects = objects
+
+        return objects
+
     def activate_mode(self, objects):
         """
         Use Yolov5 + Lidar fusion to detect objects.
diff --git a/opencda/customize/ml_libs/opencood_manager.py b/opencda/customize/ml_libs/opencood_manager.py
new file mode 100644
index 00000000..dba30f20
--- /dev/null
+++ b/opencda/customize/ml_libs/opencood_manager.py
@@ -0,0 +1,105 @@
+# -*- coding: utf-8 -*-
+import os
+import torch
+import argparse
+import opencood.hypes_yaml.yaml_utils as yaml_utils
+from opencood.visualization import vis_utils
+from opencood.tools import train_utils, inference_utils
+from opencood.utils import eval_utils
+from opencood.data_utils.datasets import build_dataset
+from opencood.data_utils.pre_processor import build_preprocessor
+from opencood.data_utils.post_processor import build_postprocessor
+from opencood.data_utils.datasets.late_fusion_dataset import LateFusionDataset
+from opencood.data_utils.datasets.early_fusion_dataset import EarlyFusionDataset
+from opencood.data_utils.datasets.intermediate_fusion_dataset import IntermediateFusionDataset
+
+model_mapping = {
+    'early': 'opencood/logs/pointpillar_early_fusion.yaml',
+    'late': 'opencood/logs/pointpillar_late_fusion',
+    'intermediate': 'opencood/logs/pointpillar_intermediate_fusion.yaml',
+}
+
+DATASET_DICT = {
+    'late': LateFusionDataset,
+    'early': EarlyFusionDataset,
+    'intermediate': IntermediateFusionDataset,
+}
+
+
+class OpenCOODManager(object):
+    def __init__(self, fusion_method='late'):
+        assert fusion_method in ['late', 'early', 'intermediate']
+        self.fusion_method = fusion_method
+        self.opt = argparse.Namespace(model_dir=model_mapping[fusion_method])
+        hypes = yaml_utils.load_yaml(None, self.opt)
+        self.model = train_utils.create_model(hypes)
+        if torch.cuda.is_available():
+            self.model.cuda()
+        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+        saved_path = model_mapping[fusion_method]
+        _, model = train_utils.load_saved_model(saved_path, self.model)
+        model.eval()
+
+        self.opencood_dataset = build_dataset(hypes, visualize=True, train=False)
+
+        # Create the dictionary for evaluation
+        self.result_stat = {0.3: {'tp': [], 'fp': [], 'gt': 0},
+                            0.5: {'tp': [], 'fp': [], 'gt': 0},
+                            0.7: {'tp': [], 'fp': [], 'gt': 0}}
+        self.vis_counter = 0
+
+    def to_device(self, data):
+        return train_utils.to_device(data, self.device)
+
+    def inference(self, batch_data):
+        if self.fusion_method == 'late':
+            pred_box_tensor, pred_score, gt_box_tensor = \
+                inference_utils.inference_late_fusion(batch_data,
+                                                      self.model,
+                                                      self.opencood_dataset)
+        elif self.fusion_method == 'early':
+            pred_box_tensor, pred_score, gt_box_tensor = \
+                inference_utils.inference_early_fusion(batch_data,
+                                                       self.model,
+                                                       self.opencood_dataset)
+        elif self.fusion_method == 'intermediate':
+            pred_box_tensor, pred_score, gt_box_tensor = \
+                inference_utils.inference_intermediate_fusion(batch_data,
+                                                              self.model,
+                                                              self.opencood_dataset)
+        else:
+            raise NotImplementedError('Only early, late and intermediate'
+                                      'fusion is supported.')
+
+        eval_utils.caluclate_tp_fp(pred_box_tensor,
+                                   pred_score,
+                                   gt_box_tensor,
+                                   self.result_stat,
+                                   0.3)
+        eval_utils.caluclate_tp_fp(pred_box_tensor,
+                                   pred_score,
+                                   gt_box_tensor,
+                                   self.result_stat,
+                                   0.5)
+        eval_utils.caluclate_tp_fp(pred_box_tensor,
+                                   pred_score,
+                                   gt_box_tensor,
+                                   self.result_stat,
+                                   0.7)
+
+        return pred_box_tensor, pred_score, gt_box_tensor
+
+    def show_vis(self, pred_box_tensor, gt_box_tensor, batch_data):
+        vis_save_path = os.path.join(self.opt.model_dir, 'vis')
+        if not os.path.exists(vis_save_path):
+            os.makedirs(vis_save_path)
+        vis_save_path = os.path.join(vis_save_path, '%05d.png' % self.vis_counter)
+
+        vis_utils.visualize_single_sample_output_gt(pred_box_tensor,
+                                               gt_box_tensor,
+                                               batch_data['ego'][
+                                                   'origin_lidar'],
+                                               True,
+                                               vis_save_path,
+                                               mode='constant')
+        self.vis_counter += 1
diff --git a/opencda/scenario_testing/config_yaml/copy.yaml b/opencda/scenario_testing/config_yaml/copy.yaml
new file mode 100644
index 00000000..bd9096d6
--- /dev/null
+++ b/opencda/scenario_testing/config_yaml/copy.yaml
@@ -0,0 +1,205 @@
+description: |-
+  Copyright 2021 <UCLA Mobility Lab>
+  Author: Runsheng Xu <rxx3386@ucla.edu>
+  Content: This is the scenario testing configuration file for single vehicle perception/localization/behavior/control
+           testing in high speed in the customized 2lanefree simple version.
+
+
+# define carla simulation setting
+world:
+  sync_mode: true
+  client_port: 2000
+  fixed_delta_seconds: &delta 0.05
+  seed: 22
+  weather:
+    sun_altitude_angle: 15 # 90 is the midday and -90 is the midnight
+    cloudiness: 0 # 0 is the clean sky and 100 is the thickest cloud
+    precipitation: 0 # rain, 100 is the heaviest rain
+    precipitation_deposits: 0 # Determines the creation of puddles. Values range from 0 to 100, being 0 none at all and 100 a road completely capped with water.
+    wind_intensity: 0 # it will influence the rain
+    fog_density: 0 # fog thickness, 100 is the largest
+    fog_distance: 0  # Fog start distance. Values range from 0 to infinite.
+    fog_falloff: 0 # Density of the fog (as in specific mass) from 0 to infinity. The bigger the value, the more dense and heavy it will be, and the fog will reach smaller heights
+    wetness: 0
+
+# Define settings for multi-class blueprint spawning
+# Comment out this chunk of code or set use_multi_class_bp to be False if you don't want to spawn multi-class actors
+blueprint:
+  use_multi_class_bp: true
+  bp_meta_path: "opencda/assets/blueprint_meta/bbx_stats_0912.json" # Define the path for loading the blueprint metadata for defining the class of each blueprint
+  # Define blueprint type sample probabilities
+  bp_class_sample_prob:
+    car: 0.5
+    truck: 0.1
+    bus: 0.1
+    bicycle: 0.1
+    motorcycle: 0.1
+
+# Define the basic parameters of the rsu
+rsu_base: &rsu_base
+  sensing:
+    perception:
+      activate: false # when not activated, objects positions will be retrieved from server directly
+      cooperative: false
+      camera_visualize: 1 # how many camera images need to be visualized. 0 means no visualization for camera
+      camera_num: 4 # how many cameras are mounted on the vehicle. Maximum 3(frontal, left and right cameras)
+      lidar_visualize: true # whether to visualize lidar points using open3d
+      lidar: # lidar sensor configuration, check CARLA sensor reference for more details
+        channels: 32
+        range: 120
+        points_per_second: 1000000
+        rotation_frequency: 20 # the simulation is 20 fps
+        upper_fov: 2
+        lower_fov: -25
+        dropoff_general_rate: 0.3
+        dropoff_intensity_limit: 0.7
+        dropoff_zero_intensity: 0.4
+        noise_stddev: 0.02
+    localization:
+      activate: true # when not activated, ego position will be retrieved from server directly
+      dt: *delta # used for kalman filter
+      gnss: # gnss sensor configuration
+        noise_alt_stddev: 0.05
+        noise_lat_stddev: 3e-6
+        noise_lon_stddev: 3e-6
+
+# Define the basic parameters of the vehicles
+vehicle_base: &vehicle_base
+  sensing: &base_sensing
+    perception: &base_perception
+      activate: false # when not activated, objects positions will be retrieved from server directly
+      cooperative: true # cooperative perception
+      camera_visualize: 1 # how many camera images need to be visualized. 0 means no visualization for camera
+      camera_num: 4 # how many cameras are mounted on the vehicle. Maximum 3(frontal, left and right cameras)
+      lidar_visualize: true # whether to visualize lidar points using open3d
+      lidar: # lidar sensor configuration, check CARLA sensor reference for more details
+        channels: 32
+        range: 120
+        points_per_second: 1000000
+        rotation_frequency: 20 # the simulation is 20 fps
+        upper_fov: 2
+        lower_fov: -25
+        dropoff_general_rate: 0.3
+        dropoff_intensity_limit: 0.7
+        dropoff_zero_intensity: 0.4
+        noise_stddev: 0.02
+    localization: &base_localize
+      activate: true # when not activated, ego position will be retrieved from server directly
+      cooperative: false # cooperative perception
+      dt: *delta # used for kalman filter
+      gnss: # gnss sensor configuration
+        noise_alt_stddev: 0.005
+        noise_lat_stddev: 1e-6
+        noise_lon_stddev: 1e-6
+        heading_direction_stddev: 0.1 # degree
+        speed_stddev: 0.2
+      debug_helper: &loc_debug_helper
+        show_animation: false # whether to show real-time trajectory plotting
+        x_scale: 1.0 # used to multiply with the x coordinate to make the error on x axis clearer
+        y_scale: 100.0 # used to multiply with the y coordinate to make the error on y axis clearer
+  map_manager: &base_map_manager
+    pixels_per_meter: 2
+    raster_size: [ 224, 224 ]
+    lane_sample_resolution: 0.1
+    visualize: false
+    activate: false
+  behavior: &base_behavior
+    max_speed: 30 # maximum speed, km/h
+    tailgate_speed: 34 # when a vehicles needs to be close to another vehicle asap
+    speed_lim_dist: 3 # max_speed - speed_lim_dist = target speed
+    speed_decrease: 15 # used in car following mode to decrease speed for distance keeping
+    safety_time: 4 # ttc safety thresholding for decreasing speed
+    emergency_param: 0.4 # used to identify whether a emergency stop needed
+    ignore_traffic_light: true # whether to ignore traffic light
+    overtake_allowed: false # whether overtake allowed, typically false for platoon leader
+    collision_time_ahead: 1.5 # used for collision checking
+    overtake_counter_recover: 35 # the vehicle can not do another overtake during next certain steps
+    sample_resolution: 4.5 # the unit distance between two adjacent waypoints in meter
+    local_planner: &base_local_planner # trajectory planning related
+      buffer_size: 12 # waypoint buffer size
+      trajectory_update_freq: 15 # used to control trajectory points updating frequency
+      waypoint_update_freq: 9 # used to control waypoint updating frequency
+      trajectory_dt: 0.20 # for every dt seconds, we sample a trajectory point from the trajectory path as next goal state
+      min_dist: 3 # used to pop out the waypoints too close to current location
+      debug: false # whether to draw future/history waypoints
+      debug_trajectory: false # whether to draw the trajectory points and path
+  controller: &base_controller
+    type: pid_controller # this has to be exactly the same name as the controller py file
+    args:
+      lat:
+        k_p: 0.75
+        k_d: 0.02
+        k_i: 0.4
+      lon:
+        k_p: 0.37
+        k_d: 0.024
+        k_i: 0.032
+      dynamic: false # whether use dynamic pid setting
+      dt: *delta # this should be equal to your simulation time-step
+      max_brake: 1.0
+      max_throttle: 1.0
+      max_steering: 0.3
+  v2x: &base_v2x # communication related
+    enabled: true
+    communication_range: 35
+    loc_noise: 0.0
+    yaw_noise: 0.0
+    speed_noise: 0.0
+    lag: 0
+  cooperative:
+    options:
+      - lidar
+      # - camera uncomment camera if collecting the raw rgb camera image
+
+
+# define the background traffic control by carla
+carla_traffic_manager:
+  sync_mode: true # has to be same as the world setting
+  global_distance: 5 # the minimum distance in meters that vehicles have to keep with the rest
+  # Sets the difference the vehicle's intended speed and its current speed limit.
+  #  Carla default speed is 30 km/h, so -100 represents 60 km/h,
+  # and 20 represents 24 km/h
+  global_speed_perc: -10
+  set_osm_mode: true # Enables or disables the OSM mode.
+  auto_lane_change: true
+  random: true # whether to random select vehicles' color and model
+  ignore_lights_percentage: 100 # whether set the traffic ignore traffic lights
+  vehicle_list: ~  # a number or a list
+  # Used only when vehicle_list is a number.
+  #  x_min, x_max, y_min, y_max, x_step, y_step, vehicle_num
+  range:
+    - [-11, 12, 100, 200, 3.5, 15, 3]
+
+
+# define scenario. In this scenario, a 4-vehicle platoon already exists.
+scenario:
+  rsu_list:
+    - <<: *rsu_base
+      spawn_position: [12.00, 192.31, 3.0]
+      id: -1
+
+  single_cav_list:
+    - <<: *vehicle_base
+      spawn_position: [12.00, 192.31, 0.3, 0, -90, 0]
+      destination: [67.12, 150.2, 1.0]
+      v2x:
+        <<: *base_v2x
+        communication_range: 45
+      behavior:
+        <<: *base_behavior
+        local_planner:
+          <<: *base_local_planner
+          debug_trajectory: false
+          debug: false
+    - <<: *vehicle_base
+      spawn_position: [-5.51, 160.24, 0.3, 0, 90, 0]
+      destination: [-2.43, 263, 0.3]
+      v2x:
+        <<: *base_v2x
+        communication_range: 45
+      behavior:
+        <<: *base_behavior
+        local_planner:
+          <<: *base_local_planner
+          debug_trajectory: false
+          debug: false
\ No newline at end of file
diff --git a/opencda/scenario_testing/config_yaml/v2xp_online_carla.yaml b/opencda/scenario_testing/config_yaml/v2xp_online_carla.yaml
new file mode 100644
index 00000000..9c13b071
--- /dev/null
+++ b/opencda/scenario_testing/config_yaml/v2xp_online_carla.yaml
@@ -0,0 +1,205 @@
+description: |-
+  Copyright 2021 <UCLA Mobility Lab>
+  Author: Runsheng Xu <rxx3386@ucla.edu>
+  Content: This is the scenario testing configuration file for single vehicle perception/localization/behavior/control
+           testing in high speed in the customized 2lanefree simple version.
+
+
+# define carla simulation setting
+world:
+  sync_mode: true
+  client_port: 2000
+  fixed_delta_seconds: &delta 0.05
+  seed: 22
+  weather:
+    sun_altitude_angle: 15 # 90 is the midday and -90 is the midnight
+    cloudiness: 0 # 0 is the clean sky and 100 is the thickest cloud
+    precipitation: 0 # rain, 100 is the heaviest rain
+    precipitation_deposits: 0 # Determines the creation of puddles. Values range from 0 to 100, being 0 none at all and 100 a road completely capped with water.
+    wind_intensity: 0 # it will influence the rain
+    fog_density: 0 # fog thickness, 100 is the largest
+    fog_distance: 0  # Fog start distance. Values range from 0 to infinite.
+    fog_falloff: 0 # Density of the fog (as in specific mass) from 0 to infinity. The bigger the value, the more dense and heavy it will be, and the fog will reach smaller heights
+    wetness: 0
+
+# Define settings for multi-class blueprint spawning
+# Comment out this chunk of code or set use_multi_class_bp to be False if you don't want to spawn multi-class actors
+blueprint:
+  use_multi_class_bp: true
+  bp_meta_path: "opencda/assets/blueprint_meta/bbx_stats_0912.json" # Define the path for loading the blueprint metadata for defining the class of each blueprint
+  # Define blueprint type sample probabilities
+  bp_class_sample_prob:
+    car: 0.5
+    truck: 0.1
+    bus: 0.1
+    bicycle: 0.1
+    motorcycle: 0.1
+
+# Define the basic parameters of the rsu
+rsu_base: &rsu_base
+  sensing:
+    perception:
+      activate: true # when not activated, objects positions will be retrieved from server directly
+      cooperative: true
+      camera_visualize: 1 # how many camera images need to be visualized. 0 means no visualization for camera
+      camera_num: 4 # how many cameras are mounted on the vehicle. Maximum 3(frontal, left and right cameras)
+      lidar_visualize: true # whether to visualize lidar points using open3d
+      lidar: # lidar sensor configuration, check CARLA sensor reference for more details
+        channels: 32
+        range: 120
+        points_per_second: 1000000
+        rotation_frequency: 20 # the simulation is 20 fps
+        upper_fov: 2
+        lower_fov: -25
+        dropoff_general_rate: 0.3
+        dropoff_intensity_limit: 0.7
+        dropoff_zero_intensity: 0.4
+        noise_stddev: 0.02
+    localization:
+      activate: false # when not activated, ego position will be retrieved from server directly
+      dt: *delta # used for kalman filter
+      gnss: # gnss sensor configuration
+        noise_alt_stddev: 0.05
+        noise_lat_stddev: 3e-6
+        noise_lon_stddev: 3e-6
+
+# Define the basic parameters of the vehicles
+vehicle_base: &vehicle_base
+  sensing: &base_sensing
+    perception: &base_perception
+      activate: true # when not activated, objects positions will be retrieved from server directly
+      cooperative: true # cooperative perception
+      camera_visualize: 1 # how many camera images need to be visualized. 0 means no visualization for camera
+      camera_num: 4 # how many cameras are mounted on the vehicle. Maximum 3(frontal, left and right cameras)
+      lidar_visualize: true # whether to visualize lidar points using open3d
+      lidar: # lidar sensor configuration, check CARLA sensor reference for more details
+        channels: 32
+        range: 120
+        points_per_second: 1000000
+        rotation_frequency: 20 # the simulation is 20 fps
+        upper_fov: 2
+        lower_fov: -25
+        dropoff_general_rate: 0.3
+        dropoff_intensity_limit: 0.7
+        dropoff_zero_intensity: 0.4
+        noise_stddev: 0.02
+    localization: &base_localize
+      activate: false # when not activated, ego position will be retrieved from server directly
+      cooperative: false # cooperative perception
+      dt: *delta # used for kalman filter
+      gnss: # gnss sensor configuration
+        noise_alt_stddev: 0.005
+        noise_lat_stddev: 1e-6
+        noise_lon_stddev: 1e-6
+        heading_direction_stddev: 0.1 # degree
+        speed_stddev: 0.2
+      debug_helper: &loc_debug_helper
+        show_animation: false # whether to show real-time trajectory plotting
+        x_scale: 1.0 # used to multiply with the x coordinate to make the error on x axis clearer
+        y_scale: 100.0 # used to multiply with the y coordinate to make the error on y axis clearer
+  map_manager: &base_map_manager
+    pixels_per_meter: 2
+    raster_size: [ 224, 224 ]
+    lane_sample_resolution: 0.1
+    visualize: false
+    activate: false
+  behavior: &base_behavior
+    max_speed: 30 # maximum speed, km/h
+    tailgate_speed: 34 # when a vehicles needs to be close to another vehicle asap
+    speed_lim_dist: 3 # max_speed - speed_lim_dist = target speed
+    speed_decrease: 15 # used in car following mode to decrease speed for distance keeping
+    safety_time: 4 # ttc safety thresholding for decreasing speed
+    emergency_param: 0.4 # used to identify whether a emergency stop needed
+    ignore_traffic_light: true # whether to ignore traffic light
+    overtake_allowed: false # whether overtake allowed, typically false for platoon leader
+    collision_time_ahead: 1.5 # used for collision checking
+    overtake_counter_recover: 35 # the vehicle can not do another overtake during next certain steps
+    sample_resolution: 4.5 # the unit distance between two adjacent waypoints in meter
+    local_planner: &base_local_planner # trajectory planning related
+      buffer_size: 12 # waypoint buffer size
+      trajectory_update_freq: 15 # used to control trajectory points updating frequency
+      waypoint_update_freq: 9 # used to control waypoint updating frequency
+      trajectory_dt: 0.20 # for every dt seconds, we sample a trajectory point from the trajectory path as next goal state
+      min_dist: 3 # used to pop out the waypoints too close to current location
+      debug: false # whether to draw future/history waypoints
+      debug_trajectory: false # whether to draw the trajectory points and path
+  controller: &base_controller
+    type: pid_controller # this has to be exactly the same name as the controller py file
+    args:
+      lat:
+        k_p: 0.75
+        k_d: 0.02
+        k_i: 0.4
+      lon:
+        k_p: 0.37
+        k_d: 0.024
+        k_i: 0.032
+      dynamic: false # whether use dynamic pid setting
+      dt: *delta # this should be equal to your simulation time-step
+      max_brake: 1.0
+      max_throttle: 1.0
+      max_steering: 0.3
+  v2x: &base_v2x # communication related
+    enabled: true
+    communication_range: 35
+    loc_noise: 0.0
+    yaw_noise: 0.0
+    speed_noise: 0.0
+    lag: 0
+  cooperative:
+    options:
+      - lidar
+      # - camera uncomment camera if collecting the raw rgb camera image
+
+
+# define the background traffic control by carla
+carla_traffic_manager:
+  sync_mode: true # has to be same as the world setting
+  global_distance: 5 # the minimum distance in meters that vehicles have to keep with the rest
+  # Sets the difference the vehicle's intended speed and its current speed limit.
+  #  Carla default speed is 30 km/h, so -100 represents 60 km/h,
+  # and 20 represents 24 km/h
+  global_speed_perc: -10
+  set_osm_mode: true # Enables or disables the OSM mode.
+  auto_lane_change: true
+  random: true # whether to random select vehicles' color and model
+  ignore_lights_percentage: 100 # whether set the traffic ignore traffic lights
+  vehicle_list: ~  # a number or a list
+  # Used only when vehicle_list is a number.
+  #  x_min, x_max, y_min, y_max, x_step, y_step, vehicle_num
+  range:
+    - [-11, 12, 100, 200, 3.5, 15, 10]
+
+
+# define scenario. In this scenario, a 4-vehicle platoon already exists.
+scenario:
+  rsu_list:
+    - <<: *rsu_base
+      spawn_position: [12.00, 192.31, 3.0]
+      id: -1
+
+  single_cav_list:
+    - <<: *vehicle_base
+      spawn_position: [12.00, 192.31, 0.3, 0, -90, 0]
+      destination: [67.12, 150.2, 1.0]
+      v2x:
+        <<: *base_v2x
+        communication_range: 45
+      behavior:
+        <<: *base_behavior
+        local_planner:
+          <<: *base_local_planner
+          debug_trajectory: false
+          debug: false
+    - <<: *vehicle_base
+      spawn_position: [-5.51, 160.24, 0.3, 0, 90, 0]
+      destination: [-2.43, 263, 0.3]
+      v2x:
+        <<: *base_v2x
+        communication_range: 45
+      behavior:
+        <<: *base_behavior
+        local_planner:
+          <<: *base_local_planner
+          debug_trajectory: false
+          debug: false
\ No newline at end of file
diff --git a/opencda/scenario_testing/v2xp_online_carla.py b/opencda/scenario_testing/v2xp_online_carla.py
new file mode 100644
index 00000000..75fc93a0
--- /dev/null
+++ b/opencda/scenario_testing/v2xp_online_carla.py
@@ -0,0 +1,96 @@
+# -*- coding: utf-8 -*-
+"""
+Scenario testing: merging vehicle joining a platoon in the
+customized 2-lane freeway simplified map sorely with carla
+"""
+# Author: Runsheng Xu <rxx3386@ucla.edu>
+# License: TDG-Attribution-NonCommercial-NoDistrib
+import os
+
+import carla
+
+import opencda.scenario_testing.utils.sim_api as sim_api
+from opencda.core.common.cav_world import CavWorld
+from opencda.scenario_testing.evaluations.evaluate_manager import \
+    EvaluationManager
+from opencda.scenario_testing.utils.yaml_utils import load_yaml, save_yaml
+
+
+def run_scenario(opt, config_yaml):
+    try:
+        scenario_params = load_yaml(config_yaml)
+
+        # create CAV world
+        cav_world = CavWorld(opt.apply_ml)
+
+        # create scenario manager
+        scenario_manager = sim_api.ScenarioManager(scenario_params,
+                                                   opt.apply_ml,
+                                                   opt.version,
+                                                   town='Town06',
+                                                   cav_world=cav_world)
+
+        if opt.record:
+            scenario_manager.client. \
+                start_recorder("v2xp_online_carla.log", True)
+
+        single_cav_list = \
+            scenario_manager.create_vehicle_manager(application=['single', 'cooperative'],
+                                                    data_dump=False)
+        rsu_list = \
+            scenario_manager.create_rsu_manager(data_dump=False)
+
+        # create background traffic in carla
+        traffic_manager, bg_veh_list = \
+            scenario_manager.create_traffic_carla()
+
+        # create evaluation manager
+        eval_manager = \
+            EvaluationManager(scenario_manager.cav_world,
+                              script_name='coop_town06',
+                              current_time=scenario_params['current_time'])
+
+        spectator = scenario_manager.world.get_spectator()
+
+        # save the data collection protocol to the folder
+        # current_path = os.path.dirname(os.path.realpath(__file__))
+        # save_yaml_name = os.path.join(current_path,
+        #                               '../../data_dumping',
+        #                               scenario_params['current_time'],
+        #                               'data_protocol.yaml')
+        # save_yaml(scenario_params, save_yaml_name)
+
+        while True:
+            scenario_manager.tick()
+            transform = single_cav_list[0].vehicle.get_transform()
+            spectator.set_transform(carla.Transform(
+                transform.location +
+                carla.Location(
+                    z=70),
+                carla.Rotation(
+                    pitch=-
+                    90)))
+
+            for i, single_cav in enumerate(single_cav_list):
+                single_cav.update_info()
+                control = single_cav.run_step()
+                single_cav.vehicle.apply_control(control)
+
+            for rsu in rsu_list:
+                rsu.update_info()
+                rsu.run_step()
+
+    finally:
+        eval_manager.evaluate()
+
+        if opt.record:
+            scenario_manager.client.stop_recorder()
+
+        scenario_manager.close()
+
+        for v in single_cav_list:
+            v.destroy()
+        for r in rsu_list:
+            r.destroy()
+        for v in bg_veh_list:
+            v.destroy()

From 9ceba4d15b5f09a3bb92d1802b9ce4ce23551e5e Mon Sep 17 00:00:00 2001
From: ju40268 <ju40268@gmail.com>
Date: Mon, 20 Feb 2023 17:54:15 -0800
Subject: [PATCH 02/14] fix ml manager loading, make window size as old, remove
 the printing

---
 opencda/core/common/cav_world.py              |  15 +-
 .../sensing/perception/coperception_libs.py   |   8 +-
 .../core/sensing/perception/o3d_lidar_libs.py |   9 +-
 .../scenario_testing/config_yaml/copy.yaml    | 205 ------------------
 opencda/scenario_testing/v2xp_online_carla.py |   2 +-
 5 files changed, 17 insertions(+), 222 deletions(-)
 delete mode 100644 opencda/scenario_testing/config_yaml/copy.yaml

diff --git a/opencda/core/common/cav_world.py b/opencda/core/common/cav_world.py
index e03eab24..44c83244 100644
--- a/opencda/core/common/cav_world.py
+++ b/opencda/core/common/cav_world.py
@@ -36,26 +36,25 @@ class CavWorld(object):
         The machine learning manager class.
     """
 
-    def __init__(self, apply_ml=False):
+    def __init__(self, apply_ml=False, apply_coperception=False):
 
         self.vehicle_id_set = set()
         self._vehicle_manager_dict = {}
         self._platooning_dict = {}
         self._rsu_manager_dict = {}
         self.ml_manager = None
-        """
-        if apply_ml:
+
+        if apply_ml and apply_coperception:
+            ml_manager = getattr(importlib.import_module(
+                "opencda.customize.ml_libs.opencood_manager"), 'OpenCOODManager')
+            self.ml_manager = ml_manager()
+        elif apply_ml:
             # we import in this way so the user don't need to install ml
             # packages unless they require to
             ml_manager = getattr(importlib.import_module(
                 "opencda.customize.ml_libs.ml_manager"), 'MLManager')
             # initialize the ml manager to load the DL/ML models into memory
             self.ml_manager = ml_manager()
-        """
-        if apply_ml:
-            ml_manager = getattr(importlib.import_module(
-                "opencda.customize.ml_libs.opencood_manager"), 'OpenCOODManager')
-            self.ml_manager = ml_manager()
         # this is used only when co-simulation activated.
         self.sumo2carla_ids = {}
 
diff --git a/opencda/core/sensing/perception/coperception_libs.py b/opencda/core/sensing/perception/coperception_libs.py
index e5a68df6..f30dc6ab 100644
--- a/opencda/core/sensing/perception/coperception_libs.py
+++ b/opencda/core/sensing/perception/coperception_libs.py
@@ -18,12 +18,14 @@ def matrix2list(matrix):
         assert len(matrix.shape) == 2
         return matrix.tolist()
 
+    @staticmethod
     def calculate_time_delay(self):
         """
         TODO: needs revision to reflect the delay
         """
         return 0
 
+    @staticmethod
     def load_vehicles(self, objects):
         data = {}
         if 'vehicles' in objects:
@@ -51,12 +53,13 @@ def load_vehicles(self, objects):
                                    veh_bbx.extent.y,
                                    veh_bbx.extent.z],
                         "speed": veh_speed
-                }})
+                    }})
 
         return {
             'vehicles': data
         }
 
+    @staticmethod
     def load_transformation_matrix(self, is_ego, data):
         """
         TODO: needs revision to reflect the cur/delay params
@@ -137,8 +140,6 @@ def load_camera_data(self):
             lidar2camera = self.matrix2list(lidar2camera)
             camera_param.update({'extrinsic': lidar2camera})
             data.update({'camera%d' % i: camera_param})
-
-        print(f"CoperceptionLibs.load_camera_data: {data}.")
         return data
 
     def load_ego_data(self):
@@ -173,7 +174,6 @@ def load_ego_data(self):
             ],
             'ego_speed': float(self.localizer.get_ego_spd())
         }
-        print(f"CoperceptionLibs.load_ego_data: {data}")
         return data
 
     def load_plan_trajectory(self):
diff --git a/opencda/core/sensing/perception/o3d_lidar_libs.py b/opencda/core/sensing/perception/o3d_lidar_libs.py
index 31986498..e7c2b099 100644
--- a/opencda/core/sensing/perception/o3d_lidar_libs.py
+++ b/opencda/core/sensing/perception/o3d_lidar_libs.py
@@ -100,8 +100,8 @@ def o3d_visualizer_init(actor_id):
     """
     vis = o3d.visualization.Visualizer()
     vis.create_window(window_name=str(actor_id),
-                      width=960,
-                      height=640,
+                      width=480,
+                      height=320,
                       left=480,
                       top=270)
     vis.get_render_option().background_color = [0.05, 0.05, 0.05]
@@ -160,7 +160,7 @@ def o3d_visualizer_show(vis, count, point_cloud, objects):
             vis.remove_geometry(aabb)
 
 
-def o3d_visualizer_show_coperception(vis, count, point_cloud, pred_bbx, objects):
+def o3d_visualizer_show_coperception(vis, count, point_cloud, pred_bbx_tensor, objects):
     """
     Visualize the point cloud at runtime.
 
@@ -186,7 +186,7 @@ def o3d_visualizer_show_coperception(vis, count, point_cloud, pred_bbx, objects)
     if count == 2:
         vis.add_geometry(point_cloud)
 
-    oabbs_pred = bbx2oabb(pred_bbx, color=(1, 0, 0))
+    oabbs_pred = bbx2oabb(pred_bbx_tensor, color=(1, 0, 0))
     for ele in oabbs_pred:
         vis.add_geometry(ele)
 
@@ -212,6 +212,7 @@ def o3d_visualizer_show_coperception(vis, count, point_cloud, pred_bbx, objects)
             aabb = object_.o3d_bbx
             vis.remove_geometry(aabb)
 
+    # remove the prediction bbx drawing
     for ele in oabbs_pred:
         vis.remove_geometry(ele)
 
diff --git a/opencda/scenario_testing/config_yaml/copy.yaml b/opencda/scenario_testing/config_yaml/copy.yaml
deleted file mode 100644
index bd9096d6..00000000
--- a/opencda/scenario_testing/config_yaml/copy.yaml
+++ /dev/null
@@ -1,205 +0,0 @@
-description: |-
-  Copyright 2021 <UCLA Mobility Lab>
-  Author: Runsheng Xu <rxx3386@ucla.edu>
-  Content: This is the scenario testing configuration file for single vehicle perception/localization/behavior/control
-           testing in high speed in the customized 2lanefree simple version.
-
-
-# define carla simulation setting
-world:
-  sync_mode: true
-  client_port: 2000
-  fixed_delta_seconds: &delta 0.05
-  seed: 22
-  weather:
-    sun_altitude_angle: 15 # 90 is the midday and -90 is the midnight
-    cloudiness: 0 # 0 is the clean sky and 100 is the thickest cloud
-    precipitation: 0 # rain, 100 is the heaviest rain
-    precipitation_deposits: 0 # Determines the creation of puddles. Values range from 0 to 100, being 0 none at all and 100 a road completely capped with water.
-    wind_intensity: 0 # it will influence the rain
-    fog_density: 0 # fog thickness, 100 is the largest
-    fog_distance: 0  # Fog start distance. Values range from 0 to infinite.
-    fog_falloff: 0 # Density of the fog (as in specific mass) from 0 to infinity. The bigger the value, the more dense and heavy it will be, and the fog will reach smaller heights
-    wetness: 0
-
-# Define settings for multi-class blueprint spawning
-# Comment out this chunk of code or set use_multi_class_bp to be False if you don't want to spawn multi-class actors
-blueprint:
-  use_multi_class_bp: true
-  bp_meta_path: "opencda/assets/blueprint_meta/bbx_stats_0912.json" # Define the path for loading the blueprint metadata for defining the class of each blueprint
-  # Define blueprint type sample probabilities
-  bp_class_sample_prob:
-    car: 0.5
-    truck: 0.1
-    bus: 0.1
-    bicycle: 0.1
-    motorcycle: 0.1
-
-# Define the basic parameters of the rsu
-rsu_base: &rsu_base
-  sensing:
-    perception:
-      activate: false # when not activated, objects positions will be retrieved from server directly
-      cooperative: false
-      camera_visualize: 1 # how many camera images need to be visualized. 0 means no visualization for camera
-      camera_num: 4 # how many cameras are mounted on the vehicle. Maximum 3(frontal, left and right cameras)
-      lidar_visualize: true # whether to visualize lidar points using open3d
-      lidar: # lidar sensor configuration, check CARLA sensor reference for more details
-        channels: 32
-        range: 120
-        points_per_second: 1000000
-        rotation_frequency: 20 # the simulation is 20 fps
-        upper_fov: 2
-        lower_fov: -25
-        dropoff_general_rate: 0.3
-        dropoff_intensity_limit: 0.7
-        dropoff_zero_intensity: 0.4
-        noise_stddev: 0.02
-    localization:
-      activate: true # when not activated, ego position will be retrieved from server directly
-      dt: *delta # used for kalman filter
-      gnss: # gnss sensor configuration
-        noise_alt_stddev: 0.05
-        noise_lat_stddev: 3e-6
-        noise_lon_stddev: 3e-6
-
-# Define the basic parameters of the vehicles
-vehicle_base: &vehicle_base
-  sensing: &base_sensing
-    perception: &base_perception
-      activate: false # when not activated, objects positions will be retrieved from server directly
-      cooperative: true # cooperative perception
-      camera_visualize: 1 # how many camera images need to be visualized. 0 means no visualization for camera
-      camera_num: 4 # how many cameras are mounted on the vehicle. Maximum 3(frontal, left and right cameras)
-      lidar_visualize: true # whether to visualize lidar points using open3d
-      lidar: # lidar sensor configuration, check CARLA sensor reference for more details
-        channels: 32
-        range: 120
-        points_per_second: 1000000
-        rotation_frequency: 20 # the simulation is 20 fps
-        upper_fov: 2
-        lower_fov: -25
-        dropoff_general_rate: 0.3
-        dropoff_intensity_limit: 0.7
-        dropoff_zero_intensity: 0.4
-        noise_stddev: 0.02
-    localization: &base_localize
-      activate: true # when not activated, ego position will be retrieved from server directly
-      cooperative: false # cooperative perception
-      dt: *delta # used for kalman filter
-      gnss: # gnss sensor configuration
-        noise_alt_stddev: 0.005
-        noise_lat_stddev: 1e-6
-        noise_lon_stddev: 1e-6
-        heading_direction_stddev: 0.1 # degree
-        speed_stddev: 0.2
-      debug_helper: &loc_debug_helper
-        show_animation: false # whether to show real-time trajectory plotting
-        x_scale: 1.0 # used to multiply with the x coordinate to make the error on x axis clearer
-        y_scale: 100.0 # used to multiply with the y coordinate to make the error on y axis clearer
-  map_manager: &base_map_manager
-    pixels_per_meter: 2
-    raster_size: [ 224, 224 ]
-    lane_sample_resolution: 0.1
-    visualize: false
-    activate: false
-  behavior: &base_behavior
-    max_speed: 30 # maximum speed, km/h
-    tailgate_speed: 34 # when a vehicles needs to be close to another vehicle asap
-    speed_lim_dist: 3 # max_speed - speed_lim_dist = target speed
-    speed_decrease: 15 # used in car following mode to decrease speed for distance keeping
-    safety_time: 4 # ttc safety thresholding for decreasing speed
-    emergency_param: 0.4 # used to identify whether a emergency stop needed
-    ignore_traffic_light: true # whether to ignore traffic light
-    overtake_allowed: false # whether overtake allowed, typically false for platoon leader
-    collision_time_ahead: 1.5 # used for collision checking
-    overtake_counter_recover: 35 # the vehicle can not do another overtake during next certain steps
-    sample_resolution: 4.5 # the unit distance between two adjacent waypoints in meter
-    local_planner: &base_local_planner # trajectory planning related
-      buffer_size: 12 # waypoint buffer size
-      trajectory_update_freq: 15 # used to control trajectory points updating frequency
-      waypoint_update_freq: 9 # used to control waypoint updating frequency
-      trajectory_dt: 0.20 # for every dt seconds, we sample a trajectory point from the trajectory path as next goal state
-      min_dist: 3 # used to pop out the waypoints too close to current location
-      debug: false # whether to draw future/history waypoints
-      debug_trajectory: false # whether to draw the trajectory points and path
-  controller: &base_controller
-    type: pid_controller # this has to be exactly the same name as the controller py file
-    args:
-      lat:
-        k_p: 0.75
-        k_d: 0.02
-        k_i: 0.4
-      lon:
-        k_p: 0.37
-        k_d: 0.024
-        k_i: 0.032
-      dynamic: false # whether use dynamic pid setting
-      dt: *delta # this should be equal to your simulation time-step
-      max_brake: 1.0
-      max_throttle: 1.0
-      max_steering: 0.3
-  v2x: &base_v2x # communication related
-    enabled: true
-    communication_range: 35
-    loc_noise: 0.0
-    yaw_noise: 0.0
-    speed_noise: 0.0
-    lag: 0
-  cooperative:
-    options:
-      - lidar
-      # - camera uncomment camera if collecting the raw rgb camera image
-
-
-# define the background traffic control by carla
-carla_traffic_manager:
-  sync_mode: true # has to be same as the world setting
-  global_distance: 5 # the minimum distance in meters that vehicles have to keep with the rest
-  # Sets the difference the vehicle's intended speed and its current speed limit.
-  #  Carla default speed is 30 km/h, so -100 represents 60 km/h,
-  # and 20 represents 24 km/h
-  global_speed_perc: -10
-  set_osm_mode: true # Enables or disables the OSM mode.
-  auto_lane_change: true
-  random: true # whether to random select vehicles' color and model
-  ignore_lights_percentage: 100 # whether set the traffic ignore traffic lights
-  vehicle_list: ~  # a number or a list
-  # Used only when vehicle_list is a number.
-  #  x_min, x_max, y_min, y_max, x_step, y_step, vehicle_num
-  range:
-    - [-11, 12, 100, 200, 3.5, 15, 3]
-
-
-# define scenario. In this scenario, a 4-vehicle platoon already exists.
-scenario:
-  rsu_list:
-    - <<: *rsu_base
-      spawn_position: [12.00, 192.31, 3.0]
-      id: -1
-
-  single_cav_list:
-    - <<: *vehicle_base
-      spawn_position: [12.00, 192.31, 0.3, 0, -90, 0]
-      destination: [67.12, 150.2, 1.0]
-      v2x:
-        <<: *base_v2x
-        communication_range: 45
-      behavior:
-        <<: *base_behavior
-        local_planner:
-          <<: *base_local_planner
-          debug_trajectory: false
-          debug: false
-    - <<: *vehicle_base
-      spawn_position: [-5.51, 160.24, 0.3, 0, 90, 0]
-      destination: [-2.43, 263, 0.3]
-      v2x:
-        <<: *base_v2x
-        communication_range: 45
-      behavior:
-        <<: *base_behavior
-        local_planner:
-          <<: *base_local_planner
-          debug_trajectory: false
-          debug: false
\ No newline at end of file
diff --git a/opencda/scenario_testing/v2xp_online_carla.py b/opencda/scenario_testing/v2xp_online_carla.py
index 75fc93a0..15223eb2 100644
--- a/opencda/scenario_testing/v2xp_online_carla.py
+++ b/opencda/scenario_testing/v2xp_online_carla.py
@@ -21,7 +21,7 @@ def run_scenario(opt, config_yaml):
         scenario_params = load_yaml(config_yaml)
 
         # create CAV world
-        cav_world = CavWorld(opt.apply_ml)
+        cav_world = CavWorld(apply_ml=opt.apply_ml, apply_coperception=True)
 
         # create scenario manager
         scenario_manager = sim_api.ScenarioManager(scenario_params,

From f2d4438dc82211efbc272338c9f05bfc529cc831 Mon Sep 17 00:00:00 2001
From: ju40268 <ju40268@gmail.com>
Date: Tue, 21 Feb 2023 07:35:02 -0800
Subject: [PATCH 03/14] late fusion runnable - mostly done

---
 opencda/core/sensing/perception/coperception_libs.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/opencda/core/sensing/perception/coperception_libs.py b/opencda/core/sensing/perception/coperception_libs.py
index f30dc6ab..e861d1eb 100644
--- a/opencda/core/sensing/perception/coperception_libs.py
+++ b/opencda/core/sensing/perception/coperception_libs.py
@@ -19,14 +19,14 @@ def matrix2list(matrix):
         return matrix.tolist()
 
     @staticmethod
-    def calculate_time_delay(self):
+    def calculate_time_delay():
         """
         TODO: needs revision to reflect the delay
         """
         return 0
 
     @staticmethod
-    def load_vehicles(self, objects):
+    def load_vehicles(objects):
         data = {}
         if 'vehicles' in objects:
             vehicle_list = objects['vehicles']
@@ -60,7 +60,7 @@ def load_vehicles(self, objects):
         }
 
     @staticmethod
-    def load_transformation_matrix(self, is_ego, data):
+    def load_transformation_matrix(is_ego, data):
         """
         TODO: needs revision to reflect the cur/delay params
         """

From f26ab189fbea931edba2d22bf1c7315ccc5b6ec1 Mon Sep 17 00:00:00 2001
From: ju40268 <ju40268@gmail.com>
Date: Tue, 21 Feb 2023 11:13:26 -0800
Subject: [PATCH 04/14] pass in fusion method through arguments

---
 opencda/core/common/cav_world.py              | 4 ++--
 opencda/scenario_testing/v2xp_online_carla.py | 4 +++-
 2 files changed, 5 insertions(+), 3 deletions(-)

diff --git a/opencda/core/common/cav_world.py b/opencda/core/common/cav_world.py
index 44c83244..5314c92e 100644
--- a/opencda/core/common/cav_world.py
+++ b/opencda/core/common/cav_world.py
@@ -36,7 +36,7 @@ class CavWorld(object):
         The machine learning manager class.
     """
 
-    def __init__(self, apply_ml=False, apply_coperception=False):
+    def __init__(self, apply_ml=False, apply_coperception=False, fusion_method='late'):
 
         self.vehicle_id_set = set()
         self._vehicle_manager_dict = {}
@@ -47,7 +47,7 @@ def __init__(self, apply_ml=False, apply_coperception=False):
         if apply_ml and apply_coperception:
             ml_manager = getattr(importlib.import_module(
                 "opencda.customize.ml_libs.opencood_manager"), 'OpenCOODManager')
-            self.ml_manager = ml_manager()
+            self.ml_manager = ml_manager(fusion_method)
         elif apply_ml:
             # we import in this way so the user don't need to install ml
             # packages unless they require to
diff --git a/opencda/scenario_testing/v2xp_online_carla.py b/opencda/scenario_testing/v2xp_online_carla.py
index 15223eb2..88ce9cab 100644
--- a/opencda/scenario_testing/v2xp_online_carla.py
+++ b/opencda/scenario_testing/v2xp_online_carla.py
@@ -21,7 +21,9 @@ def run_scenario(opt, config_yaml):
         scenario_params = load_yaml(config_yaml)
 
         # create CAV world
-        cav_world = CavWorld(apply_ml=opt.apply_ml, apply_coperception=True)
+        cav_world = CavWorld(apply_ml=opt.apply_ml,
+                             apply_coperception=True,
+                             fusion_method='late')
 
         # create scenario manager
         scenario_manager = sim_api.ScenarioManager(scenario_params,

From 49a5c17e766b03af1529ac1321a11b9ec69f6b4e Mon Sep 17 00:00:00 2001
From: ju40268 <ju40268@gmail.com>
Date: Wed, 22 Feb 2023 13:44:35 -0800
Subject: [PATCH 05/14] add in support for early fusion datasets

---
 opencda/core/common/vehicle_manager.py        | 14 ++--
 .../sensing/perception/coperception_libs.py   | 75 +++++++++++--------
 .../core/sensing/perception/o3d_lidar_libs.py | 14 ++--
 .../sensing/perception/perception_manager.py  | 19 ++---
 opencda/customize/ml_libs/opencood_manager.py |  6 +-
 opencda/scenario_testing/v2xp_online_carla.py | 11 +--
 6 files changed, 73 insertions(+), 66 deletions(-)

diff --git a/opencda/core/common/vehicle_manager.py b/opencda/core/common/vehicle_manager.py
index b0f461e2..b359a25b 100644
--- a/opencda/core/common/vehicle_manager.py
+++ b/opencda/core/common/vehicle_manager.py
@@ -121,13 +121,6 @@ def __init__(
         # Control module
         self.controller = ControlManager(control_config)
 
-        if data_dumping:
-            self.data_dumper = DataDumper(self.perception_manager,
-                                          vehicle.id,
-                                          save_time=current_time)
-        else:
-            self.data_dumper = None
-
         # perception module
         # move it down here to pass in the behavior manager & localization manager
         self.perception_manager = PerceptionManager(
@@ -139,6 +132,13 @@ def __init__(
             cav_world=cav_world,
             data_dump=data_dumping)
 
+        if data_dumping:
+            self.data_dumper = DataDumper(self.perception_manager,
+                                          vehicle.id,
+                                          save_time=current_time)
+        else:
+            self.data_dumper = None
+
         cav_world.update_vehicle_manager(self)
 
     def set_destination(
diff --git a/opencda/core/sensing/perception/coperception_libs.py b/opencda/core/sensing/perception/coperception_libs.py
index e861d1eb..dd59b844 100644
--- a/opencda/core/sensing/perception/coperception_libs.py
+++ b/opencda/core/sensing/perception/coperception_libs.py
@@ -1,16 +1,21 @@
+import weakref
 import numpy as np
 
 from opencda.core.common.misc import get_speed
+from opencda.core.sensing.perception.obstacle_vehicle import \
+    ObstacleVehicle
 import opencda.core.sensing.perception.sensor_transformation as st
 from opencood.utils.transformation_utils import x1_to_x2
 
 
 class CoperceptionLibs:
-    def __init__(self, lidar, rgb_camera, localization_manager, behavior_agent):
+    def __init__(self, lidar, rgb_camera, localization_manager, behavior_agent, carla_world, cav_world):
         self.lidar = lidar
         self.rgb_camera = rgb_camera
         self.localizer = localization_manager
         self.agent = behavior_agent
+        self.carla_world = weakref.ref(carla_world)()
+        self.cav_world = weakref.ref(cav_world)()
         self.time_delay = self.calculate_time_delay()
 
     @staticmethod
@@ -26,39 +31,47 @@ def calculate_time_delay():
         return 0
 
     @staticmethod
-    def load_vehicles(objects):
-        data = {}
-        if 'vehicles' in objects:
-            vehicle_list = objects['vehicles']
-            for v in vehicle_list:
-                veh_carla_id = v.carla_id
-                veh_pos = v.get_transform()
-                veh_bbx = v.bounding_box
-                veh_speed = get_speed(v)
-
-                data.update({
-                    veh_carla_id: {
-                        'bp_id': v.type_id,
-                        'color': v.color,
-                        "location": [veh_pos.location.x,
-                                     veh_pos.location.y,
-                                     veh_pos.location.z],
-                        "center": [veh_bbx.location.x,
-                                   veh_bbx.location.y,
-                                   veh_bbx.location.z],
-                        "angle": [veh_pos.rotation.roll,
-                                  veh_pos.rotation.yaw,
-                                  veh_pos.rotation.pitch],
-                        "extent": [veh_bbx.extent.x,
-                                   veh_bbx.extent.y,
-                                   veh_bbx.extent.z],
-                        "speed": veh_speed
-                    }})
-
+    def load_vehicle_bbx(object_vehicle):
+        veh_pos = object_vehicle.get_transform()
+        veh_bbx = object_vehicle.bounding_box
+        veh_speed = get_speed(object_vehicle)
         return {
-            'vehicles': data
+            "location": [veh_pos.location.x,
+                         veh_pos.location.y,
+                         veh_pos.location.z],
+            "center": [veh_bbx.location.x,
+                       veh_bbx.location.y,
+                       veh_bbx.location.z],
+            "angle": [veh_pos.rotation.roll,
+                      veh_pos.rotation.yaw,
+                      veh_pos.rotation.pitch],
+            "extent": [veh_bbx.extent.x,
+                       veh_bbx.extent.y,
+                       veh_bbx.extent.z],
+            "speed": veh_speed
         }
 
+    def load_vehicles(self, ego_id, ego_pos):
+        def dist_to_ego(actor):
+            return actor.get_location().distance(ego_pos.location)
+
+        world = self.carla_world
+        vehicle_list = world.get_actors().filter("*vehicle*")
+        vehicle_list = [v for v in vehicle_list if dist_to_ego(v) < 50 and v.id != ego_id]
+        vehicle_dict = {}
+        if self.lidar:
+            for v in vehicle_list:
+                object_vehicle = ObstacleVehicle(None, None, v, self.lidar.sensor, self.cav_world.sumo2carla_ids)
+                vehicle_dict.update({object_vehicle.carla_id: self.load_vehicle_bbx(object_vehicle)})
+        else:
+            for v in vehicle_list:
+                object_vehicle = ObstacleVehicle(None, None, v, None, self.cav_world.sumo2carla_ids)
+                vehicle_dict.update({object_vehicle.carla_id: self.load_vehicle_bbx(object_vehicle)})
+        data = {
+            'vehicles': vehicle_dict
+        }
+        return data
+
     @staticmethod
     def load_transformation_matrix(is_ego, data):
         """
diff --git a/opencda/core/sensing/perception/o3d_lidar_libs.py b/opencda/core/sensing/perception/o3d_lidar_libs.py
index e7c2b099..8cc2e6e1 100644
--- a/opencda/core/sensing/perception/o3d_lidar_libs.py
+++ b/opencda/core/sensing/perception/o3d_lidar_libs.py
@@ -186,9 +186,10 @@ def o3d_visualizer_show_coperception(vis, count, point_cloud, pred_bbx_tensor, o
     if count == 2:
         vis.add_geometry(point_cloud)
 
-    oabbs_pred = bbx2oabb(pred_bbx_tensor, color=(1, 0, 0))
-    for ele in oabbs_pred:
-        vis.add_geometry(ele)
+    if pred_bbx_tensor is not None:  # sometimes pred is None
+        oabbs_pred = bbx2oabb(pred_bbx_tensor, color=(1, 0, 0))
+        for ele in oabbs_pred:
+            vis.add_geometry(ele)
 
     vis.update_geometry(point_cloud)
 
@@ -212,9 +213,10 @@ def o3d_visualizer_show_coperception(vis, count, point_cloud, pred_bbx_tensor, o
             aabb = object_.o3d_bbx
             vis.remove_geometry(aabb)
 
-    # remove the prediction bbx drawing
-    for ele in oabbs_pred:
-        vis.remove_geometry(ele)
+    if pred_bbx_tensor is not None:  # sometimes pred is None
+        # remove the prediction bbx drawing
+        for ele in oabbs_pred:
+            vis.remove_geometry(ele)
 
 
 def o3d_camera_lidar_fusion(objects,
diff --git a/opencda/core/sensing/perception/perception_manager.py b/opencda/core/sensing/perception/perception_manager.py
index 9b0100fd..90bc6777 100644
--- a/opencda/core/sensing/perception/perception_manager.py
+++ b/opencda/core/sensing/perception/perception_manager.py
@@ -456,7 +456,9 @@ def __init__(self, v2x_manager, localization_manager, behavior_agent, vehicle, c
             lidar=self.lidar,
             rgb_camera=self.rgb_camera,
             localization_manager=self.localization_manager,
-            behavior_agent=self.behavior_agent
+            behavior_agent=self.behavior_agent,
+            carla_world=self.carla_world,
+            cav_world=self.cav_world
         )
 
     def dist(self, a):
@@ -512,8 +514,10 @@ def detect(self, ego_pos):
 
     def coperception_mode(self, objects):
         """
-            Use OpenCOOD to detect objects
+        Use OpenCOOD to detect objects
         """
+        if self.lidar.data is None:
+            return objects
         data = OrderedDict()
         cav_id = str(self.id)
         data[cav_id] = OrderedDict()
@@ -526,7 +530,7 @@ def coperception_mode(self, objects):
         ego_data = self.coperception_libs.load_ego_data()
         plan_trajectory_data = self.coperception_libs.load_plan_trajectory()
         lidar_pose_data = self.coperception_libs.load_cur_lidar_pose()
-        vehicles = self.coperception_libs.load_vehicles(objects)
+        vehicles = self.coperception_libs.load_vehicles(cav_id, self.ego_pos)
         # update dic
         data[cav_id]['params'].update(plan_trajectory_data)
         data[cav_id]['params'].update(camera_data)
@@ -537,14 +541,12 @@ def coperception_mode(self, objects):
         transformation_matrix = self.coperception_libs.load_transformation_matrix(data[cav_id]['ego'],
                                                                                   data[cav_id]['params'])
         data[cav_id]['params'].update(transformation_matrix)
-
-        if self.lidar.data is None: return objects
-
         data[cav_id].update({'lidar_np': self.lidar.data})
 
+        # this doesn't exist on early/intermediate fusion dataset functions
+        # needs to manually added in the codebase
         reformat_data_dict = self.ml_manager.opencood_dataset.get_item_test(data)
-        output_dict = self.ml_manager.opencood_dataset.collate_batch_test(
-            [reformat_data_dict])  # should have batch size dim
+        output_dict = self.ml_manager.opencood_dataset.collate_batch_test([reformat_data_dict])  # should have batch size dim
         batch_data = self.ml_manager.to_device(output_dict)
         pred_box_tensor, pred_score, gt_box_tensor = self.ml_manager.inference(batch_data)
         # self.ml_manager.show_vis(pred_box_tensor, gt_box_tensor, batch_data)
@@ -643,7 +645,6 @@ def activate_mode(self, objects):
         # add traffic light
         objects = self.retrieve_traffic_lights(objects)
         self.objects = objects
-
         return objects
 
     def deactivate_mode(self, objects):
diff --git a/opencda/customize/ml_libs/opencood_manager.py b/opencda/customize/ml_libs/opencood_manager.py
index dba30f20..5e02815f 100644
--- a/opencda/customize/ml_libs/opencood_manager.py
+++ b/opencda/customize/ml_libs/opencood_manager.py
@@ -14,9 +14,9 @@
 from opencood.data_utils.datasets.intermediate_fusion_dataset import IntermediateFusionDataset
 
 model_mapping = {
-    'early': 'opencood/logs/pointpillar_early_fusion.yaml',
+    'early': 'opencood/logs/pointpillar_early_fusion',
     'late': 'opencood/logs/pointpillar_late_fusion',
-    'intermediate': 'opencood/logs/pointpillar_intermediate_fusion.yaml',
+    'intermediate': 'opencood/logs/pointpillar_intermediate_fusion',
 }
 
 DATASET_DICT = {
@@ -27,7 +27,7 @@
 
 
 class OpenCOODManager(object):
-    def __init__(self, fusion_method='late'):
+    def __init__(self, fusion_method):
         assert fusion_method in ['late', 'early', 'intermediate']
         self.fusion_method = fusion_method
         self.opt = argparse.Namespace(model_dir=model_mapping[fusion_method])
diff --git a/opencda/scenario_testing/v2xp_online_carla.py b/opencda/scenario_testing/v2xp_online_carla.py
index 88ce9cab..a1be1d34 100644
--- a/opencda/scenario_testing/v2xp_online_carla.py
+++ b/opencda/scenario_testing/v2xp_online_carla.py
@@ -23,7 +23,7 @@ def run_scenario(opt, config_yaml):
         # create CAV world
         cav_world = CavWorld(apply_ml=opt.apply_ml,
                              apply_coperception=True,
-                             fusion_method='late')
+                             fusion_method='early')
 
         # create scenario manager
         scenario_manager = sim_api.ScenarioManager(scenario_params,
@@ -53,15 +53,6 @@ def run_scenario(opt, config_yaml):
                               current_time=scenario_params['current_time'])
 
         spectator = scenario_manager.world.get_spectator()
-
-        # save the data collection protocol to the folder
-        # current_path = os.path.dirname(os.path.realpath(__file__))
-        # save_yaml_name = os.path.join(current_path,
-        #                               '../../data_dumping',
-        #                               scenario_params['current_time'],
-        #                               'data_protocol.yaml')
-        # save_yaml(scenario_params, save_yaml_name)
-
         while True:
             scenario_manager.tick()
             transform = single_cav_list[0].vehicle.get_transform()

From d9c00088ec42bf1470cf7a5b2260831728a88f4d Mon Sep 17 00:00:00 2001
From: ju40268 <ju40268@gmail.com>
Date: Wed, 22 Feb 2023 16:21:11 -0800
Subject: [PATCH 06/14] add ploting for showing both gt and prediction results,
 calculating the ap each frame, tested with intermediate and early dataset

---
 .../sensing/perception/coperception_libs.py   |  2 +-
 .../core/sensing/perception/o3d_lidar_libs.py | 55 +++++++------------
 .../sensing/perception/perception_manager.py  |  3 +-
 opencda/customize/ml_libs/opencood_manager.py | 23 ++++++--
 4 files changed, 41 insertions(+), 42 deletions(-)

diff --git a/opencda/core/sensing/perception/coperception_libs.py b/opencda/core/sensing/perception/coperception_libs.py
index dd59b844..2918bc3c 100644
--- a/opencda/core/sensing/perception/coperception_libs.py
+++ b/opencda/core/sensing/perception/coperception_libs.py
@@ -57,7 +57,7 @@ def dist_to_ego(actor):
 
         world = self.carla_world
         vehicle_list = world.get_actors().filter("*vehicle*")
-        vehicle_list = [v for v in vehicle_list if dist_to_ego(v) < 50 and v.id != ego_id]
+        vehicle_list = [v for v in vehicle_list if dist_to_ego(v) < 120 and v.id != ego_id]
         vehicle_dict = {}
         if self.lidar:
             for v in vehicle_list:
diff --git a/opencda/core/sensing/perception/o3d_lidar_libs.py b/opencda/core/sensing/perception/o3d_lidar_libs.py
index 8cc2e6e1..cee6f21a 100644
--- a/opencda/core/sensing/perception/o3d_lidar_libs.py
+++ b/opencda/core/sensing/perception/o3d_lidar_libs.py
@@ -160,45 +160,25 @@ def o3d_visualizer_show(vis, count, point_cloud, objects):
             vis.remove_geometry(aabb)
 
 
-def o3d_visualizer_show_coperception(vis, count, point_cloud, pred_bbx_tensor, objects):
-    """
-    Visualize the point cloud at runtime.
-
-    Parameters
-    ----------
-    vis : o3d.Visualizer
-        Visualization interface.
-
-    count : int
-        Current step since simulation started.
-
-    point_cloud : o3d.PointCloud
-        Open3d point cloud.
-
-    objects : dict
-        The dictionary containing objects.
-
-    Returns
-    -------
-
-    """
-
+def o3d_visualizer_show_coperception(vis, count, point_cloud, predict_bbx_tensor, gt_box_tensor, objects):
     if count == 2:
         vis.add_geometry(point_cloud)
-
-    if pred_bbx_tensor is not None:  # sometimes pred is None
-        oabbs_pred = bbx2oabb(pred_bbx_tensor, color=(1, 0, 0))
-        for ele in oabbs_pred:
-            vis.add_geometry(ele)
+    if predict_bbx_tensor is not None:
+        oabbs_pred = bbx2oabb(predict_bbx_tensor, color=(1, 0, 0))
+        for p in oabbs_pred:
+            vis.add_geometry(p)
+    if gt_box_tensor is not None:
+        oabbs_gt = bbx2oabb(gt_box_tensor, color=(0, 1, 0))
+        for g in oabbs_gt:
+            vis.add_geometry(g)
 
     vis.update_geometry(point_cloud)
 
     for key, object_list in objects.items():
-        # we only draw vehicles for now
         if key != 'vehicles':
             continue
-        for object_ in object_list:
-            aabb = object_.o3d_bbx
+        for o in object_list:
+            aabb = o.o3d_bbx
             vis.add_geometry(aabb)
 
     vis.poll_events()
@@ -209,14 +189,17 @@ def o3d_visualizer_show_coperception(vis, count, point_cloud, pred_bbx_tensor, o
     for key, object_list in objects.items():
         if key != 'vehicles':
             continue
-        for object_ in object_list:
-            aabb = object_.o3d_bbx
+        for o in object_list:
+            aabb = o.o3d_bbx
             vis.remove_geometry(aabb)
 
-    if pred_bbx_tensor is not None:  # sometimes pred is None
+    if predict_bbx_tensor is not None:
         # remove the prediction bbx drawing
-        for ele in oabbs_pred:
-            vis.remove_geometry(ele)
+        for p in oabbs_pred:
+            vis.remove_geometry(p)
+    if gt_box_tensor is not None:
+        for g in oabbs_gt:
+            vis.remove_geometry(g)
 
 
 def o3d_camera_lidar_fusion(objects,
diff --git a/opencda/core/sensing/perception/perception_manager.py b/opencda/core/sensing/perception/perception_manager.py
index 90bc6777..40ff81f6 100644
--- a/opencda/core/sensing/perception/perception_manager.py
+++ b/opencda/core/sensing/perception/perception_manager.py
@@ -550,6 +550,7 @@ def coperception_mode(self, objects):
         batch_data = self.ml_manager.to_device(output_dict)
         pred_box_tensor, pred_score, gt_box_tensor = self.ml_manager.inference(batch_data)
         # self.ml_manager.show_vis(pred_box_tensor, gt_box_tensor, batch_data)
+        self.ml_manager.evaluate_average_precision()
 
         if self.lidar_visualize:
             while self.lidar.data is None:
@@ -560,7 +561,7 @@ def coperception_mode(self, objects):
                 self.count,
                 self.lidar.o3d_pointcloud,
                 pred_box_tensor,
-                # gt_box_tensor,
+                gt_box_tensor,
                 objects)
         # add traffic light
         objects = self.retrieve_traffic_lights(objects)
diff --git a/opencda/customize/ml_libs/opencood_manager.py b/opencda/customize/ml_libs/opencood_manager.py
index 5e02815f..5a9fc8bf 100644
--- a/opencda/customize/ml_libs/opencood_manager.py
+++ b/opencda/customize/ml_libs/opencood_manager.py
@@ -6,9 +6,8 @@
 from opencood.visualization import vis_utils
 from opencood.tools import train_utils, inference_utils
 from opencood.utils import eval_utils
+from opencood.utils.eval_utils import calculate_ap
 from opencood.data_utils.datasets import build_dataset
-from opencood.data_utils.pre_processor import build_preprocessor
-from opencood.data_utils.post_processor import build_postprocessor
 from opencood.data_utils.datasets.late_fusion_dataset import LateFusionDataset
 from opencood.data_utils.datasets.early_fusion_dataset import EarlyFusionDataset
 from opencood.data_utils.datasets.intermediate_fusion_dataset import IntermediateFusionDataset
@@ -16,7 +15,7 @@
 model_mapping = {
     'early': 'opencood/logs/pointpillar_early_fusion',
     'late': 'opencood/logs/pointpillar_late_fusion',
-    'intermediate': 'opencood/logs/pointpillar_intermediate_fusion',
+    'intermediate': 'opencood/logs/pointpillar_attentive_fusion',
 }
 
 DATASET_DICT = {
@@ -86,9 +85,25 @@ def inference(self, batch_data):
                                    gt_box_tensor,
                                    self.result_stat,
                                    0.7)
-
         return pred_box_tensor, pred_score, gt_box_tensor
 
+    def evaluate_average_precision(self):
+        for iou, iou_dict in self.result_stat.items():
+            if iou_dict['gt'] == 0:
+                print("skip this evaluation as the gt is divided by 0.")
+                return
+        ap_30, mrec_30, mpre_30 = calculate_ap(self.result_stat, 0.30)
+        ap_50, mrec_50, mpre_50 = calculate_ap(self.result_stat, 0.50)
+        ap_70, mrec_70, mpre_70 = calculate_ap(self.result_stat, 0.70)
+        print('The Average Precision at IOU 0.3 is %.2f, '
+              'The Average Precision at IOU 0.5 is %.2f, '
+              'The Average Precision at IOU 0.7 is %.2f' % (ap_30, ap_50, ap_70))
+        self.result_stat = {
+            0.3: {'tp': [], 'fp': [], 'gt': 0},
+            0.5: {'tp': [], 'fp': [], 'gt': 0},
+            0.7: {'tp': [], 'fp': [], 'gt': 0}
+        }
+
     def show_vis(self, pred_box_tensor, gt_box_tensor, batch_data):
         vis_save_path = os.path.join(self.opt.model_dir, 'vis')
         if not os.path.exists(vis_save_path):

From 85297729a0ff26a5de8b1aecaf688a130b54a54e Mon Sep 17 00:00:00 2001
From: ju40268 <ju40268@gmail.com>
Date: Wed, 22 Feb 2023 16:22:49 -0800
Subject: [PATCH 07/14] apply the reformat

---
 opencda/customize/ml_libs/opencood_manager.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/opencda/customize/ml_libs/opencood_manager.py b/opencda/customize/ml_libs/opencood_manager.py
index 5a9fc8bf..5612b7b1 100644
--- a/opencda/customize/ml_libs/opencood_manager.py
+++ b/opencda/customize/ml_libs/opencood_manager.py
@@ -111,10 +111,10 @@ def show_vis(self, pred_box_tensor, gt_box_tensor, batch_data):
         vis_save_path = os.path.join(vis_save_path, '%05d.png' % self.vis_counter)
 
         vis_utils.visualize_single_sample_output_gt(pred_box_tensor,
-                                               gt_box_tensor,
-                                               batch_data['ego'][
-                                                   'origin_lidar'],
-                                               True,
-                                               vis_save_path,
-                                               mode='constant')
+                                                    gt_box_tensor,
+                                                    batch_data['ego'][
+                                                        'origin_lidar'],
+                                                    True,
+                                                    vis_save_path,
+                                                    mode='constant')
         self.vis_counter += 1

From 0098ba7232edaaa05af95659e7bd8f4e311ac048 Mon Sep 17 00:00:00 2001
From: ju40268 <ju40268@gmail.com>
Date: Thu, 23 Feb 2023 14:46:48 -0800
Subject: [PATCH 08/14] cherry-picked from the lidar comm side

---
 opencda/core/common/v2x_manager.py            | 25 +++++++++++++++++--
 opencda/core/common/vehicle_manager.py        |  4 ++-
 .../sensing/perception/perception_manager.py  | 18 ++++++++-----
 3 files changed, 38 insertions(+), 9 deletions(-)

diff --git a/opencda/core/common/v2x_manager.py b/opencda/core/common/v2x_manager.py
index fba0e193..da1687e0 100644
--- a/opencda/core/common/v2x_manager.py
+++ b/opencda/core/common/v2x_manager.py
@@ -70,6 +70,9 @@ def __init__(self, cav_world, config_yaml, vid):
         # ego position buffer. use deque so we can simulate lagging
         self.ego_pos = deque(maxlen=100)
         self.ego_spd = deque(maxlen=100)
+        # ego sensor buffer. Use deque so we can simulate lagging
+        self.ego_lidar = deque(maxlen=100)
+        self.ego_image = deque(maxlen=100)
         # used to exclude the cav self during searching
         self.vid = vid
 
@@ -89,12 +92,14 @@ def __init__(self, cav_world, config_yaml, vid):
         if 'lag' in config_yaml:
             self.lag = config_yaml['lag']
 
-    def update_info(self, ego_pos, ego_spd):
+    def update_info(self, ego_pos, ego_spd, ego_lidar, ego_image):
         """
         Update all communication plugins with current localization info.
         """
         self.ego_pos.append(ego_pos)
         self.ego_spd.append(ego_spd)
+        self.ego_lidar.append(ego_lidar)
+        self.ego_image.append(ego_image)
         self.search()
 
         # the ego pos in platooning_plugin is used for self-localization,
@@ -148,6 +153,20 @@ def get_ego_speed(self):
 
         return processed_ego_speed
 
+    def get_ego_lidar(self):
+        if not self.ego_lidar:
+            return
+        lidar = self.ego_lidar[0] if len(self.ego_lidar) < self.lag else \
+            self.ego_lidar[-1 - int(abs(self.lag))]
+        return lidar
+
+    def get_ego_rgb_image(self):
+        if not self.ego_image:
+            return
+        image = self.ego_image[0] if len(self.ego_image) < self.lag else \
+            self.ego_image[-1 - int(abs(self.lag))]
+        return image
+
     def search(self):
         """
         Search the CAVs nearby.
@@ -156,7 +175,7 @@ def search(self):
 
         for vid, vm in vehicle_manager_dict.items():
             # avoid the Nonetype error at the first simulation step
-            if not vm.v2x_manager.get_ego_pos():
+            if not vm.v2x_manager.get_ego_pos() or not vm.v2x_manager.get_ego_lidar():
                 continue
             # avoid add itself as the cav nearby
             if vid == self.vid:
@@ -167,6 +186,8 @@ def search(self):
 
             if distance < self.communication_range:
                 self.cav_nearby.update({vid: vm})
+            else:
+                self.cav_nearby.pop(vid, None)
     """
     -----------------------------------------------------------
                  Below is platooning related 
diff --git a/opencda/core/common/vehicle_manager.py b/opencda/core/common/vehicle_manager.py
index b359a25b..fdd495ec 100644
--- a/opencda/core/common/vehicle_manager.py
+++ b/opencda/core/common/vehicle_manager.py
@@ -181,6 +181,8 @@ def update_info(self):
 
         ego_pos = self.localizer.get_ego_pos()
         ego_spd = self.localizer.get_ego_spd()
+        ego_lidar = self.perception_manager.lidar
+        ego_image = self.perception_manager.rgb_camera
 
         # object detection
         objects = self.perception_manager.detect(ego_pos)
@@ -190,7 +192,7 @@ def update_info(self):
 
         # update ego position and speed to v2x manager,
         # and then v2x manager will search the nearby cavs
-        self.v2x_manager.update_info(ego_pos, ego_spd)
+        self.v2x_manager.update_info(ego_pos, ego_spd, ego_lidar, ego_image)
 
         self.agent.update_information(ego_pos, ego_spd, objects)
         # pass position and speed info to controller
diff --git a/opencda/core/sensing/perception/perception_manager.py b/opencda/core/sensing/perception/perception_manager.py
index 40ff81f6..103048ce 100644
--- a/opencda/core/sensing/perception/perception_manager.py
+++ b/opencda/core/sensing/perception/perception_manager.py
@@ -376,8 +376,8 @@ class PerceptionManager:
         Open3d point cloud visualizer.
     """
 
-    def __init__(self, v2x_manager, localization_manager, behavior_agent, vehicle, config_yaml, cav_world,
-                 data_dump=False, carla_world=None, infra_id=None):
+    def __init__(self, v2x_manager, localization_manager, behavior_agent, vehicle,
+                 config_yaml, cav_world, data_dump=False, carla_world=None, infra_id=None):
         self.vehicle = vehicle
         self.carla_world = carla_world if carla_world is not None \
             else self.vehicle.get_world()
@@ -494,15 +494,13 @@ def detect(self, ego_pos):
 
         """
         self.ego_pos = ego_pos
-
         objects = {
             'vehicles': [],
             'traffic_lights': []
         }
-
         if not self.activate:
+            self.search_nearby_cav()
             objects = self.deactivate_mode(objects)
-
         else:
             if self.cooperative:
                 objects = self.coperception_mode(objects)
@@ -546,7 +544,8 @@ def coperception_mode(self, objects):
         # this doesn't exist on early/intermediate fusion dataset functions
         # needs to manually added in the codebase
         reformat_data_dict = self.ml_manager.opencood_dataset.get_item_test(data)
-        output_dict = self.ml_manager.opencood_dataset.collate_batch_test([reformat_data_dict])  # should have batch size dim
+        output_dict = self.ml_manager.opencood_dataset.collate_batch_test(
+            [reformat_data_dict])  # should have batch size dim
         batch_data = self.ml_manager.to_device(output_dict)
         pred_box_tensor, pred_score, gt_box_tensor = self.ml_manager.inference(batch_data)
         # self.ml_manager.show_vis(pred_box_tensor, gt_box_tensor, batch_data)
@@ -569,6 +568,13 @@ def coperception_mode(self, objects):
 
         return objects
 
+    def search_nearby_cav(self):
+        if self.v2x_manager is not None:
+            print(f"nearby cav nearby {self.v2x_manager.cav_nearby}")
+            for _, vm in self.v2x_manager.cav_nearby.items():
+                lidar = vm.v2x_manager.get_ego_lidar()
+                print(f"lidar {lidar}")
+
     def activate_mode(self, objects):
         """
         Use Yolov5 + Lidar fusion to detect objects.

From ae348a35fc967cc2e74bb744401fe1aaa7313abe Mon Sep 17 00:00:00 2001
From: ju40268 <ju40268@gmail.com>
Date: Fri, 24 Feb 2023 17:06:09 -0800
Subject: [PATCH 09/14] communicate with ego, without applying projection

---
 opencda/core/common/v2x_manager.py            | 15 ++++-
 opencda/core/common/vehicle_manager.py        |  2 +-
 .../sensing/perception/coperception_libs.py   |  6 +-
 .../perception/coperception_manager.py        | 44 +++++++++++++
 .../sensing/perception/perception_manager.py  | 62 ++++++++-----------
 5 files changed, 86 insertions(+), 43 deletions(-)
 create mode 100644 opencda/core/sensing/perception/coperception_manager.py

diff --git a/opencda/core/common/v2x_manager.py b/opencda/core/common/v2x_manager.py
index da1687e0..2d74e8be 100644
--- a/opencda/core/common/v2x_manager.py
+++ b/opencda/core/common/v2x_manager.py
@@ -74,6 +74,7 @@ def __init__(self, cav_world, config_yaml, vid):
         self.ego_lidar = deque(maxlen=100)
         self.ego_image = deque(maxlen=100)
         # used to exclude the cav self during searching
+        self.ego_data = deque(maxlen=100)
         self.vid = vid
 
         # check if lag or noise needed to be added during communication
@@ -92,7 +93,7 @@ def __init__(self, cav_world, config_yaml, vid):
         if 'lag' in config_yaml:
             self.lag = config_yaml['lag']
 
-    def update_info(self, ego_pos, ego_spd, ego_lidar, ego_image):
+    def update_info(self, ego_pos, ego_spd, ego_lidar, ego_image, ego_data):
         """
         Update all communication plugins with current localization info.
         """
@@ -100,6 +101,7 @@ def update_info(self, ego_pos, ego_spd, ego_lidar, ego_image):
         self.ego_spd.append(ego_spd)
         self.ego_lidar.append(ego_lidar)
         self.ego_image.append(ego_image)
+        self.ego_data.append(ego_data)
         self.search()
 
         # the ego pos in platooning_plugin is used for self-localization,
@@ -167,6 +169,13 @@ def get_ego_rgb_image(self):
             self.ego_image[-1 - int(abs(self.lag))]
         return image
 
+    def get_ego_data(self):
+        if not self.ego_data:
+            return
+        data = self.ego_data[0] if len(self.ego_data) < self.lag else \
+            self.ego_data[-1 - int(abs(self.lag))]
+        return data
+
     def search(self):
         """
         Search the CAVs nearby.
@@ -185,9 +194,9 @@ def search(self):
                 vm.v2x_manager.get_ego_pos().location)
 
             if distance < self.communication_range:
-                self.cav_nearby.update({vid: vm})
+                self.cav_nearby.update({vm.vehicle.id: vm.v2x_manager})
             else:
-                self.cav_nearby.pop(vid, None)
+                self.cav_nearby.pop(vm.vehicle.id, None)
     """
     -----------------------------------------------------------
                  Below is platooning related 
diff --git a/opencda/core/common/vehicle_manager.py b/opencda/core/common/vehicle_manager.py
index fdd495ec..9881be6d 100644
--- a/opencda/core/common/vehicle_manager.py
+++ b/opencda/core/common/vehicle_manager.py
@@ -192,7 +192,7 @@ def update_info(self):
 
         # update ego position and speed to v2x manager,
         # and then v2x manager will search the nearby cavs
-        self.v2x_manager.update_info(ego_pos, ego_spd, ego_lidar, ego_image)
+        self.v2x_manager.update_info(ego_pos, ego_spd, ego_lidar, ego_image, self.v2x_manager)
 
         self.agent.update_information(ego_pos, ego_spd, objects)
         # pass position and speed info to controller
diff --git a/opencda/core/sensing/perception/coperception_libs.py b/opencda/core/sensing/perception/coperception_libs.py
index 2918bc3c..d7873885 100644
--- a/opencda/core/sensing/perception/coperception_libs.py
+++ b/opencda/core/sensing/perception/coperception_libs.py
@@ -123,9 +123,9 @@ def load_cur_lidar_pose(self):
             ]
         }
 
-    def load_camera_data(self):
+    def load_camera_data(self, lidar, rgb_camera):
         data = {}
-        for (i, camera) in enumerate(self.rgb_camera):
+        for (i, camera) in enumerate(rgb_camera):
             camera_param = {}
             camera_transformation = camera.sensor.get_transform()
             camera_param.update({'cords': [
@@ -144,7 +144,7 @@ def load_camera_data(self):
 
             # dump extrinsic matrix lidar2camera
             lidar2world = \
-                st.x_to_world_transformation(self.lidar.sensor.get_transform())
+                st.x_to_world_transformation(lidar.sensor.get_transform())
             camera2world = \
                 st.x_to_world_transformation(camera.sensor.get_transform())
 
diff --git a/opencda/core/sensing/perception/coperception_manager.py b/opencda/core/sensing/perception/coperception_manager.py
new file mode 100644
index 00000000..7d5240d1
--- /dev/null
+++ b/opencda/core/sensing/perception/coperception_manager.py
@@ -0,0 +1,44 @@
+from collections import OrderedDict
+
+
+class CoperceptionManager:
+    def __init__(self, vid, v2x_manager, coperception_libs):
+        self.vid = vid
+        self.v2x_manager = v2x_manager
+        self.coperception_libs = coperception_libs
+
+    def communicate(self):
+        data = {str(self.vid): {'is_ego': True, 'data': self.v2x_manager}}
+        if self.v2x_manager is not None:
+            for vid, vm in self.v2x_manager.cav_nearby.items():
+                data.update({str(vid): {'is_ego': False, 'data': vm}})
+        return data
+
+    def load_ego(self):
+        return {'is_ego': True, 'data': self.v2x_manager}
+
+    def prepare_data(self, lidar, ego_pos, cav_id, nearby_cav_dict):
+        lidar = nearby_cav_dict['data'].get_ego_lidar()
+        ego_pos = nearby_cav_dict['data'].get_ego_pos()
+        camera = nearby_cav_dict['data'].get_ego_rgb_image()
+        if lidar is None or ego_pos is None:
+            return
+        # stores the required field
+        data = {cav_id: OrderedDict()}
+        data[cav_id]['ego'] = nearby_cav_dict['is_ego']
+        data[cav_id]['time_delay'] = self.coperception_libs.time_delay
+        data[cav_id]['params'] = {}
+        camera_data = self.coperception_libs.load_camera_data(lidar, camera)
+        ego_data = self.coperception_libs.load_ego_data()
+        plan_trajectory_data = self.coperception_libs.load_plan_trajectory()
+        lidar_pose_data = self.coperception_libs.load_cur_lidar_pose()
+        vehicles = self.coperception_libs.load_vehicles(cav_id, ego_pos)
+        data[cav_id]['params'].update(plan_trajectory_data)
+        data[cav_id]['params'].update(camera_data)
+        data[cav_id]['params'].update(ego_data)
+        data[cav_id]['params'].update(lidar_pose_data)
+        data[cav_id]['params'].update(vehicles)
+        t_matrix = self.coperception_libs.load_transformation_matrix(data[cav_id]['ego'], data[cav_id]['params'])
+        data[cav_id]['params'].update(t_matrix)
+        data[cav_id].update({'lidar_np': lidar.data})
+        return data
diff --git a/opencda/core/sensing/perception/perception_manager.py b/opencda/core/sensing/perception/perception_manager.py
index 103048ce..5f91b301 100644
--- a/opencda/core/sensing/perception/perception_manager.py
+++ b/opencda/core/sensing/perception/perception_manager.py
@@ -17,6 +17,7 @@
 import opencda.core.sensing.perception.sensor_transformation as st
 from opencda.core.common.misc import \
     cal_distance_angle, get_speed, get_speed_sumo
+from opencda.core.sensing.perception.coperception_manager import CoperceptionManager
 from opencda.core.sensing.perception.obstacle_vehicle import \
     ObstacleVehicle
 from opencda.core.sensing.perception.static_obstacle import TrafficLight
@@ -461,6 +462,12 @@ def __init__(self, v2x_manager, localization_manager, behavior_agent, vehicle,
             cav_world=self.cav_world
         )
 
+        self.co_manager = CoperceptionManager(
+            vid=self.id,
+            v2x_manager=self.v2x_manager,
+            coperception_libs=self.coperception_libs
+        )
+
     def dist(self, a):
         """
         A fast method to retrieve the obstacle distance the ego
@@ -516,41 +523,32 @@ def coperception_mode(self, objects):
         """
         if self.lidar.data is None:
             return objects
+
+        enable_communicate = True
         data = OrderedDict()
-        cav_id = str(self.id)
-        data[cav_id] = OrderedDict()
-        # TODO: subject to changes later on?
-        data[cav_id]['ego'] = True  # assume itself is ego
-        data[cav_id]['time_delay'] = self.coperception_libs.time_delay
-        # gather calculated data
-        data[cav_id]['params'] = {}
-        camera_data = self.coperception_libs.load_camera_data()
-        ego_data = self.coperception_libs.load_ego_data()
-        plan_trajectory_data = self.coperception_libs.load_plan_trajectory()
-        lidar_pose_data = self.coperception_libs.load_cur_lidar_pose()
-        vehicles = self.coperception_libs.load_vehicles(cav_id, self.ego_pos)
-        # update dic
-        data[cav_id]['params'].update(plan_trajectory_data)
-        data[cav_id]['params'].update(camera_data)
-        data[cav_id]['params'].update(ego_data)
-        data[cav_id]['params'].update(lidar_pose_data)
-        data[cav_id]['params'].update(vehicles)
-
-        transformation_matrix = self.coperception_libs.load_transformation_matrix(data[cav_id]['ego'],
-                                                                                  data[cav_id]['params'])
-        data[cav_id]['params'].update(transformation_matrix)
-        data[cav_id].update({'lidar_np': self.lidar.data})
-
-        # this doesn't exist on early/intermediate fusion dataset functions
-        # needs to manually added in the codebase
+        if enable_communicate:
+            nearby_objects = self.co_manager.communicate()
+            for vid, nearby_cav_dict in nearby_objects.items():
+                nearby = self.co_manager.prepare_data(self.lidar, self.ego_pos, vid, nearby_cav_dict)
+                if nearby is None:
+                    return objects
+                data.update(nearby)
+        else:
+            ego_dict = {'is_ego': True, 'data': self.v2x_manager}
+            ego_data = self.co_manager.prepare_data(self.lidar, self.ego_pos, self.id, ego_dict)
+            if ego_data is None:
+                return objects
+            data.update(ego_data)
+        # inference
         reformat_data_dict = self.ml_manager.opencood_dataset.get_item_test(data)
         output_dict = self.ml_manager.opencood_dataset.collate_batch_test(
             [reformat_data_dict])  # should have batch size dim
         batch_data = self.ml_manager.to_device(output_dict)
-        pred_box_tensor, pred_score, gt_box_tensor = self.ml_manager.inference(batch_data)
+        predict_box_tensor, predict_score, gt_box_tensor = self.ml_manager.inference(batch_data)
         # self.ml_manager.show_vis(pred_box_tensor, gt_box_tensor, batch_data)
         self.ml_manager.evaluate_average_precision()
 
+        # plot
         if self.lidar_visualize:
             while self.lidar.data is None:
                 continue
@@ -559,22 +557,14 @@ def coperception_mode(self, objects):
                 self.o3d_vis,
                 self.count,
                 self.lidar.o3d_pointcloud,
-                pred_box_tensor,
+                predict_box_tensor,
                 gt_box_tensor,
                 objects)
-        # add traffic light
         objects = self.retrieve_traffic_lights(objects)
         self.objects = objects
 
         return objects
 
-    def search_nearby_cav(self):
-        if self.v2x_manager is not None:
-            print(f"nearby cav nearby {self.v2x_manager.cav_nearby}")
-            for _, vm in self.v2x_manager.cav_nearby.items():
-                lidar = vm.v2x_manager.get_ego_lidar()
-                print(f"lidar {lidar}")
-
     def activate_mode(self, objects):
         """
         Use Yolov5 + Lidar fusion to detect objects.

From daa9cc427710c1d4c7464896f3dcc4798acdc321 Mon Sep 17 00:00:00 2001
From: ju40268 <ju40268@gmail.com>
Date: Sat, 25 Feb 2023 13:11:35 -0800
Subject: [PATCH 10/14] add opencood repo

---
 opencda/core/common/v2x_manager.py            |     5 +-
 .../sensing/perception/coperception_libs.py   |    33 +-
 .../perception/coperception_manager.py        |    28 +-
 .../sensing/perception/perception_manager.py  |    39 +-
 opencda/customize/ml_libs/opencood_manager.py |     9 +
 .../config_yaml/v2xp_online_carla.yaml        |    14 +-
 opencda/scenario_testing/v2xp_online_carla.py |    18 +-
 opencood/LICENSE                              |    10 +
 opencood/README.md                            |   238 +
 opencood/docs/Makefile                        |    20 +
 opencood/docs/conf.py                         |    65 +
 opencood/docs/index.rst                       |    57 +
 opencood/docs/make.bat                        |    35 +
 opencood/docs/md_files/config_tutorial.md     |   139 +
 opencood/docs/md_files/contributor.md         |    19 +
 .../docs/md_files/data_annotation_tutorial.md |    94 +
 opencood/docs/md_files/data_intro.md          |   143 +
 opencood/docs/md_files/data_pipeline.png      |   Bin 0 -> 292761 bytes
 opencood/docs/md_files/installation.md        |    73 +
 opencood/docs/md_files/lidar_benchmark.md     |    29 +
 opencood/docs/md_files/logic_flow.md          |    85 +
 opencood/docs/requirements.txt                |     7 +
 .../2021_08_21_17_30_41/4279/000069.pcd       | 39730 +++++++++++++++
 .../2021_08_21_17_30_41/4279/000069.yaml      |   564 +
 .../4279/000069_camera0.png                   |   Bin 0 -> 581338 bytes
 .../4279/000069_camera1.png                   |   Bin 0 -> 636260 bytes
 .../4279/000069_camera2.png                   |   Bin 0 -> 561539 bytes
 .../4279/000069_camera3.png                   |   Bin 0 -> 627028 bytes
 .../2021_08_21_17_30_41/4288/000069.pcd       | 39844 ++++++++++++++++
 .../2021_08_21_17_30_41/4288/000069.yaml      |   561 +
 .../4288/000069_camera0.png                   |   Bin 0 -> 560288 bytes
 .../4288/000069_camera1.png                   |   Bin 0 -> 605594 bytes
 .../4288/000069_camera2.png                   |   Bin 0 -> 598768 bytes
 .../4288/000069_camera3.png                   |   Bin 0 -> 645799 bytes
 .../2021_08_21_17_30_41/4297/000069.pcd       | 39633 +++++++++++++++
 .../2021_08_21_17_30_41/4297/000069.yaml      |   447 +
 .../4297/000069_camera0.png                   |   Bin 0 -> 452527 bytes
 .../4297/000069_camera1.png                   |   Bin 0 -> 436842 bytes
 .../4297/000069_camera2.png                   |   Bin 0 -> 451241 bytes
 .../4297/000069_camera3.png                   |   Bin 0 -> 616425 bytes
 .../2021_08_21_17_30_41/data_protocol.yaml    |   236 +
 opencood/environment.yml                      |    18 +
 opencood/images/camera_demo.gif               |   Bin 0 -> 3387760 bytes
 opencood/images/demo1.gif                     |   Bin 0 -> 2162294 bytes
 opencood/images/logreplay.gif                 |   Bin 0 -> 5842295 bytes
 opencood/images/opencood.png                  |   Bin 0 -> 37361 bytes
 opencood/images/pytorch-logo-dark.png         |   Bin 0 -> 15625 bytes
 opencood/logreplay/README.md                  |    44 +
 opencood/logreplay/__init__.py                |     0
 opencood/logreplay/assets/__init__.py         |     0
 opencood/logreplay/assets/presave_lib.py      |   596 +
 opencood/logreplay/assets/utils.py            |    29 +
 opencood/logreplay/hypes_yaml/__init__.py     |     0
 opencood/logreplay/hypes_yaml/replay.yaml     |    61 +
 opencood/logreplay/map/__init__.py            |     0
 opencood/logreplay/map/map_drawing.py         |   220 +
 opencood/logreplay/map/map_manager.py         |   945 +
 opencood/logreplay/map/map_utils.py           |   257 +
 opencood/logreplay/scenario/__init__.py       |     0
 .../logreplay/scenario/scenarios_manager.py   |    79 +
 opencood/logreplay/scenario/scene_manager.py  |   465 +
 opencood/logreplay/sensors/__init__.py        |     0
 opencood/logreplay/sensors/base_sensor.py     |    22 +
 .../logreplay/sensors/bev_semantic_camera.py  |   216 +
 opencood/logreplay/sensors/semantic_lidar.py  |   140 +
 opencood/logreplay/sensors/sensor_manager.py  |    96 +
 opencood/logreplay/sensors/utils.py           |    29 +
 opencood/opencood/__init__.py                 |     0
 opencood/opencood/data_utils/__init__.py      |     0
 .../opencood/data_utils/augmentor/__init__.py |     0
 .../data_utils/augmentor/augment_utils.py     |    88 +
 .../data_utils/augmentor/data_augmentor.py    |   120 +
 .../opencood/data_utils/datasets/__init__.py  |    37 +
 .../data_utils/datasets/basedataset.py        |   619 +
 .../datasets/early_fusion_dataset.py          |   394 +
 .../datasets/early_fusion_vis_dataset.py      |   198 +
 .../datasets/intermediate_fusion_dataset.py   |   585 +
 .../intermediate_fusion_dataset_v2.py         |   390 +
 .../datasets/late_fusion_dataset.py           |   273 +
 .../data_utils/post_processor/__init__.py     |    26 +
 .../post_processor/base_postprocessor.py      |   147 +
 .../post_processor/bev_postprocessor.py       |   449 +
 .../post_processor/ciassd_postprocessor.py    |   169 +
 .../post_processor/fpvrcnn_postprocessor.py   |   204 +
 .../post_processor/voxel_postprocessor.py     |   433 +
 .../data_utils/pre_processor/__init__.py      |    32 +
 .../pre_processor/base_preprocessor.py        |    79 +
 .../pre_processor/bev_preprocessor.py         |   124 +
 .../pre_processor/sp_voxel_preprocessor.py    |   171 +
 .../pre_processor/voxel_preprocessor.py       |   158 +
 opencood/opencood/hypes_yaml/__init__.py      |     0
 .../hypes_yaml/ciassd_early_fusion.yaml       |   120 +
 .../ciassd_intermediate_fusion.yaml           |    89 +
 .../fpvrcnn_intermediate_fusion.yaml          |   176 +
 .../hypes_yaml/pixor_early_fusion.yaml        |    64 +
 .../hypes_yaml/pixor_intermediate_fusion.yaml |    64 +
 .../hypes_yaml/pixor_late_fusion.yaml         |    58 +
 .../hypes_yaml/point_pillar_cobevt.yaml       |   132 +
 .../hypes_yaml/point_pillar_early_fusion.yaml |    97 +
 .../hypes_yaml/point_pillar_fcooper.yaml      |   107 +
 .../point_pillar_intermediate_fusion.yaml     |   100 +
 .../hypes_yaml/point_pillar_late_fusion.yaml  |    97 +
 .../hypes_yaml/point_pillar_v2vnet.yaml       |   124 +
 .../hypes_yaml/point_pillar_v2xvit.yaml       |   162 +
 .../hypes_yaml/point_pillar_where2comm.yaml   |   132 +
 .../hypes_yaml/second_early_fusion.yaml       |    99 +
 .../second_intermediate_fusion.yaml           |    99 +
 .../hypes_yaml/second_late_fusion.yaml        |    99 +
 .../opencood/hypes_yaml/visualization.yaml    |    56 +
 .../hypes_yaml/voxelnet_early_fusion.yaml     |    88 +
 .../voxelnet_intermediate_fusion.yaml         |    89 +
 .../hypes_yaml/voxelnet_late_fusion.yaml      |    88 +
 opencood/opencood/hypes_yaml/yaml_utils.py    |   264 +
 opencood/opencood/loss/__init__.py            |     0
 opencood/opencood/loss/ciassd_loss.py         |   227 +
 opencood/opencood/loss/fpvrcnn_loss.py        |   193 +
 opencood/opencood/loss/pixor_loss.py          |   115 +
 opencood/opencood/loss/point_pillar_loss.py   |   248 +
 opencood/opencood/models/__init__.py          |     0
 opencood/opencood/models/ciassd.py            |    39 +
 opencood/opencood/models/fpvrcnn.py           |    81 +
 .../opencood/models/fuse_modules/__init__.py  |     0
 .../models/fuse_modules/f_cooper_fuse.py      |    30 +
 .../models/fuse_modules/fuse_utils.py         |    66 +
 opencood/opencood/models/fuse_modules/hmsa.py |   151 +
 .../opencood/models/fuse_modules/mswin.py     |   119 +
 .../opencood/models/fuse_modules/self_attn.py |    66 +
 .../fuse_modules/swap_fusion_modules.py       |   311 +
 .../opencood/models/fuse_modules/v2v_fuse.py  |   150 +
 .../models/fuse_modules/v2xvit_basic.py       |   191 +
 .../models/fuse_modules/where2comm_fuse.py    |   208 +
 opencood/opencood/models/pixor.py             |   310 +
 .../opencood/models/pixor_intermediate.py     |    93 +
 opencood/opencood/models/point_pillar.py      |    54 +
 .../opencood/models/point_pillar_cobevt.py    |   116 +
 .../opencood/models/point_pillar_fcooper.py   |   109 +
 .../models/point_pillar_intermediate.py       |    56 +
 .../models/point_pillar_transformer.py        |   122 +
 .../opencood/models/point_pillar_v2vnet.py    |   109 +
 .../models/point_pillar_where2comm.py         |   120 +
 opencood/opencood/models/second.py            |    59 +
 .../opencood/models/second_intermediate.py    |    62 +
 .../models/sub_modules/att_bev_backbone.py    |   145 +
 .../models/sub_modules/auto_encoder.py        |    67 +
 .../models/sub_modules/base_bev_backbone.py   |   122 +
 .../models/sub_modules/base_transformer.py    |   134 +
 .../models/sub_modules/cia_ssd_utils.py       |   100 +
 .../opencood/models/sub_modules/convgru.py    |   197 +
 .../models/sub_modules/downsample_conv.py     |    50 +
 .../models/sub_modules/height_compression.py  |    27 +
 .../opencood/models/sub_modules/matcher.py    |   132 +
 .../opencood/models/sub_modules/mean_vfe.py   |    34 +
 .../models/sub_modules/naive_compress.py      |    32 +
 .../opencood/models/sub_modules/pillar_vfe.py |   146 +
 .../sub_modules/point_pillar_scatter.py       |    48 +
 .../opencood/models/sub_modules/roi_head.py   |   280 +
 .../models/sub_modules/sparse_backbone_3d.py  |   146 +
 .../opencood/models/sub_modules/split_attn.py |    63 +
 .../sub_modules/torch_transformation_utils.py |   426 +
 opencood/opencood/models/sub_modules/vsa.py   |   277 +
 opencood/opencood/models/voxel_net.py         |   234 +
 .../opencood/models/voxel_net_intermediate.py |   183 +
 opencood/opencood/pcdet_utils/__init__.py     |     2 +
 .../pcdet_utils/iou3d_nms/iou3d_nms_utils.py  |   289 +
 .../pcdet_utils/iou3d_nms/src/iou3d_cpu.cpp   |   252 +
 .../pcdet_utils/iou3d_nms/src/iou3d_cpu.h     |    11 +
 .../pcdet_utils/iou3d_nms/src/iou3d_nms.cpp   |   188 +
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 .../pointnet2_batch/src/ball_query.cpp        |    42 +
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 .../pointnet2_batch/src/cuda_utils.h          |    15 +
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 .../pointnet2_batch/src/group_points_gpu.cu   |    92 +
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 .../pointnet2_batch/src/pointnet2_api.cpp     |    24 +
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 .../pointnet2_stack/pointnet2_modules.py      |   137 +
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 .../pointnet2_stack/src/ball_query.cpp        |    47 +
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 .../pointnet2_stack/src/ball_query_gpu.h      |    25 +
 .../pointnet2_stack/src/cuda_utils.h          |     9 +
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 .../pointnet2_stack/src/interpolate.cpp       |   110 +
 .../pointnet2_stack/src/interpolate_gpu.cu    |   195 +
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 .../roiaware_pool3d/roiaware_pool3d_utils.py  |   127 +
 .../roiaware_pool3d/src/roiaware_pool3d.cpp   |   177 +
 .../src/roiaware_pool3d_kernel.cu             |   359 +
 opencood/opencood/pcdet_utils/setup.py        |    66 +
 opencood/opencood/tools/__init__.py           |     0
 opencood/opencood/tools/debug_utils.py        |    71 +
 opencood/opencood/tools/inference.py          |   202 +
 opencood/opencood/tools/inference_utils.py    |   103 +
 opencood/opencood/tools/multi_gpu_utils.py    |    54 +
 opencood/opencood/tools/train.py              |   207 +
 opencood/opencood/tools/train_utils.py        |   268 +
 opencood/opencood/utils/__init__.py           |     0
 opencood/opencood/utils/box_overlaps.pyx      |   147 +
 opencood/opencood/utils/box_utils.py          |   956 +
 opencood/opencood/utils/common_utils.py       |   202 +
 opencood/opencood/utils/draco_compression.py  |    69 +
 opencood/opencood/utils/eval_utils.py         |   155 +
 opencood/opencood/utils/max_consensus.py      |   131 +
 opencood/opencood/utils/pcd_utils.py          |   203 +
 opencood/opencood/utils/setup.py              |     8 +
 .../opencood/utils/transformation_utils.py    |   108 +
 opencood/opencood/utils/visulizor.py          |    67 +
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 opencood/opencood/visualization/vis_utils.py  |   672 +
 opencood/requirements.txt                     |    17 +
 opencood/setup.py                             |    30 +
 230 files changed, 147086 insertions(+), 66 deletions(-)
 create mode 100644 opencood/LICENSE
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 create mode 100644 opencood/opencood/models/point_pillar_intermediate.py
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 create mode 100644 opencood/opencood/models/sub_modules/base_bev_backbone.py
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 create mode 100644 opencood/opencood/models/sub_modules/cia_ssd_utils.py
 create mode 100644 opencood/opencood/models/sub_modules/convgru.py
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 create mode 100644 opencood/opencood/models/sub_modules/height_compression.py
 create mode 100644 opencood/opencood/models/sub_modules/matcher.py
 create mode 100644 opencood/opencood/models/sub_modules/mean_vfe.py
 create mode 100644 opencood/opencood/models/sub_modules/naive_compress.py
 create mode 100644 opencood/opencood/models/sub_modules/pillar_vfe.py
 create mode 100644 opencood/opencood/models/sub_modules/point_pillar_scatter.py
 create mode 100644 opencood/opencood/models/sub_modules/roi_head.py
 create mode 100644 opencood/opencood/models/sub_modules/sparse_backbone_3d.py
 create mode 100644 opencood/opencood/models/sub_modules/split_attn.py
 create mode 100644 opencood/opencood/models/sub_modules/torch_transformation_utils.py
 create mode 100644 opencood/opencood/models/sub_modules/vsa.py
 create mode 100644 opencood/opencood/models/voxel_net.py
 create mode 100644 opencood/opencood/models/voxel_net_intermediate.py
 create mode 100644 opencood/opencood/pcdet_utils/__init__.py
 create mode 100644 opencood/opencood/pcdet_utils/iou3d_nms/iou3d_nms_utils.py
 create mode 100755 opencood/opencood/pcdet_utils/iou3d_nms/src/iou3d_cpu.cpp
 create mode 100755 opencood/opencood/pcdet_utils/iou3d_nms/src/iou3d_cpu.h
 create mode 100755 opencood/opencood/pcdet_utils/iou3d_nms/src/iou3d_nms.cpp
 create mode 100755 opencood/opencood/pcdet_utils/iou3d_nms/src/iou3d_nms.h
 create mode 100755 opencood/opencood/pcdet_utils/iou3d_nms/src/iou3d_nms_api.cpp
 create mode 100755 opencood/opencood/pcdet_utils/iou3d_nms/src/iou3d_nms_kernel.cu
 create mode 100755 opencood/opencood/pcdet_utils/pointnet2/pointnet2_batch/pointnet2_modules.py
 create mode 100755 opencood/opencood/pcdet_utils/pointnet2/pointnet2_batch/pointnet2_utils.py
 create mode 100755 opencood/opencood/pcdet_utils/pointnet2/pointnet2_batch/src/ball_query.cpp
 create mode 100755 opencood/opencood/pcdet_utils/pointnet2/pointnet2_batch/src/ball_query_gpu.cu
 create mode 100755 opencood/opencood/pcdet_utils/pointnet2/pointnet2_batch/src/ball_query_gpu.h
 create mode 100755 opencood/opencood/pcdet_utils/pointnet2/pointnet2_batch/src/cuda_utils.h
 create mode 100755 opencood/opencood/pcdet_utils/pointnet2/pointnet2_batch/src/group_points.cpp
 create mode 100755 opencood/opencood/pcdet_utils/pointnet2/pointnet2_batch/src/group_points_gpu.cu
 create mode 100755 opencood/opencood/pcdet_utils/pointnet2/pointnet2_batch/src/group_points_gpu.h
 create mode 100755 opencood/opencood/pcdet_utils/pointnet2/pointnet2_batch/src/interpolate.cpp
 create mode 100755 opencood/opencood/pcdet_utils/pointnet2/pointnet2_batch/src/interpolate_gpu.cu
 create mode 100755 opencood/opencood/pcdet_utils/pointnet2/pointnet2_batch/src/interpolate_gpu.h
 create mode 100755 opencood/opencood/pcdet_utils/pointnet2/pointnet2_batch/src/pointnet2_api.cpp
 create mode 100755 opencood/opencood/pcdet_utils/pointnet2/pointnet2_batch/src/sampling.cpp
 create mode 100755 opencood/opencood/pcdet_utils/pointnet2/pointnet2_batch/src/sampling_gpu.cu
 create mode 100755 opencood/opencood/pcdet_utils/pointnet2/pointnet2_batch/src/sampling_gpu.h
 create mode 100755 opencood/opencood/pcdet_utils/pointnet2/pointnet2_stack/pointnet2_modules.py
 create mode 100755 opencood/opencood/pcdet_utils/pointnet2/pointnet2_stack/pointnet2_utils.py
 create mode 100755 opencood/opencood/pcdet_utils/pointnet2/pointnet2_stack/src/ball_query.cpp
 create mode 100755 opencood/opencood/pcdet_utils/pointnet2/pointnet2_stack/src/ball_query_gpu.cu
 create mode 100755 opencood/opencood/pcdet_utils/pointnet2/pointnet2_stack/src/ball_query_gpu.h
 create mode 100755 opencood/opencood/pcdet_utils/pointnet2/pointnet2_stack/src/cuda_utils.h
 create mode 100755 opencood/opencood/pcdet_utils/pointnet2/pointnet2_stack/src/group_points.cpp
 create mode 100755 opencood/opencood/pcdet_utils/pointnet2/pointnet2_stack/src/group_points_gpu.cu
 create mode 100755 opencood/opencood/pcdet_utils/pointnet2/pointnet2_stack/src/group_points_gpu.h
 create mode 100755 opencood/opencood/pcdet_utils/pointnet2/pointnet2_stack/src/interpolate.cpp
 create mode 100755 opencood/opencood/pcdet_utils/pointnet2/pointnet2_stack/src/interpolate_gpu.cu
 create mode 100755 opencood/opencood/pcdet_utils/pointnet2/pointnet2_stack/src/interpolate_gpu.h
 create mode 100755 opencood/opencood/pcdet_utils/pointnet2/pointnet2_stack/src/pointnet2_api.cpp
 create mode 100755 opencood/opencood/pcdet_utils/pointnet2/pointnet2_stack/src/sampling.cpp
 create mode 100755 opencood/opencood/pcdet_utils/pointnet2/pointnet2_stack/src/sampling_gpu.cu
 create mode 100755 opencood/opencood/pcdet_utils/pointnet2/pointnet2_stack/src/sampling_gpu.h
 create mode 100755 opencood/opencood/pcdet_utils/roiaware_pool3d/roiaware_pool3d_utils.py
 create mode 100755 opencood/opencood/pcdet_utils/roiaware_pool3d/src/roiaware_pool3d.cpp
 create mode 100755 opencood/opencood/pcdet_utils/roiaware_pool3d/src/roiaware_pool3d_kernel.cu
 create mode 100644 opencood/opencood/pcdet_utils/setup.py
 create mode 100644 opencood/opencood/tools/__init__.py
 create mode 100644 opencood/opencood/tools/debug_utils.py
 create mode 100644 opencood/opencood/tools/inference.py
 create mode 100644 opencood/opencood/tools/inference_utils.py
 create mode 100644 opencood/opencood/tools/multi_gpu_utils.py
 create mode 100644 opencood/opencood/tools/train.py
 create mode 100644 opencood/opencood/tools/train_utils.py
 create mode 100644 opencood/opencood/utils/__init__.py
 create mode 100644 opencood/opencood/utils/box_overlaps.pyx
 create mode 100644 opencood/opencood/utils/box_utils.py
 create mode 100644 opencood/opencood/utils/common_utils.py
 create mode 100644 opencood/opencood/utils/draco_compression.py
 create mode 100644 opencood/opencood/utils/eval_utils.py
 create mode 100644 opencood/opencood/utils/max_consensus.py
 create mode 100644 opencood/opencood/utils/pcd_utils.py
 create mode 100644 opencood/opencood/utils/setup.py
 create mode 100644 opencood/opencood/utils/transformation_utils.py
 create mode 100644 opencood/opencood/utils/visulizor.py
 create mode 100644 opencood/opencood/version.py
 create mode 100644 opencood/opencood/visualization/__init__.py
 create mode 100644 opencood/opencood/visualization/vis_data_sequence.py
 create mode 100644 opencood/opencood/visualization/vis_utils.py
 create mode 100644 opencood/requirements.txt
 create mode 100644 opencood/setup.py

diff --git a/opencda/core/common/v2x_manager.py b/opencda/core/common/v2x_manager.py
index 2d74e8be..5a42cbc0 100644
--- a/opencda/core/common/v2x_manager.py
+++ b/opencda/core/common/v2x_manager.py
@@ -194,7 +194,10 @@ def search(self):
                 vm.v2x_manager.get_ego_pos().location)
 
             if distance < self.communication_range:
-                self.cav_nearby.update({vm.vehicle.id: vm.v2x_manager})
+                self.cav_nearby.update({vm.vehicle.id: {
+                    'vehicle_manager': vm,
+                    'v2x_manager': vm.v2x_manager
+                }})
             else:
                 self.cav_nearby.pop(vm.vehicle.id, None)
     """
diff --git a/opencda/core/sensing/perception/coperception_libs.py b/opencda/core/sensing/perception/coperception_libs.py
index d7873885..d92c5c18 100644
--- a/opencda/core/sensing/perception/coperception_libs.py
+++ b/opencda/core/sensing/perception/coperception_libs.py
@@ -36,32 +36,24 @@ def load_vehicle_bbx(object_vehicle):
         veh_bbx = object_vehicle.bounding_box
         veh_speed = get_speed(object_vehicle)
         return {
-            "location": [veh_pos.location.x,
-                         veh_pos.location.y,
-                         veh_pos.location.z],
-            "center": [veh_bbx.location.x,
-                       veh_bbx.location.y,
-                       veh_bbx.location.z],
-            "angle": [veh_pos.rotation.roll,
-                      veh_pos.rotation.yaw,
-                      veh_pos.rotation.pitch],
-            "extent": [veh_bbx.extent.x,
-                       veh_bbx.extent.y,
-                       veh_bbx.extent.z],
+            "location": [veh_pos.location.x, veh_pos.location.y, veh_pos.location.z],
+            "center": [veh_bbx.location.x, veh_bbx.location.y, veh_bbx.location.z],
+            "angle": [veh_pos.rotation.roll, veh_pos.rotation.yaw, veh_pos.rotation.pitch],
+            "extent": [veh_bbx.extent.x, veh_bbx.extent.y, veh_bbx.extent.z],
             "speed": veh_speed
         }
 
-    def load_vehicles(self, ego_id, ego_pos):
+    def load_vehicles(self, ego_id, ego_pos, lidar):
         def dist_to_ego(actor):
             return actor.get_location().distance(ego_pos.location)
 
         world = self.carla_world
         vehicle_list = world.get_actors().filter("*vehicle*")
-        vehicle_list = [v for v in vehicle_list if dist_to_ego(v) < 120 and v.id != ego_id]
+        vehicle_list = [v for v in vehicle_list if dist_to_ego(v) < 50 and v.id != ego_id]
         vehicle_dict = {}
-        if self.lidar:
+        if lidar:
             for v in vehicle_list:
-                object_vehicle = ObstacleVehicle(None, None, v, self.lidar.sensor, self.cav_world.sumo2carla_ids)
+                object_vehicle = ObstacleVehicle(None, None, v, lidar.sensor, self.cav_world.sumo2carla_ids)
                 vehicle_dict.update({object_vehicle.carla_id: self.load_vehicle_bbx(object_vehicle)})
         else:
             for v in vehicle_list:
@@ -110,8 +102,9 @@ def load_transformation_matrix(is_ego, data):
             'spatial_correction_matrix': spatial_correction_matrix
         }
 
-    def load_cur_lidar_pose(self):
-        lidar_transformation = self.lidar.sensor.get_transform()
+    @staticmethod
+    def load_cur_lidar_pose(lidar):
+        lidar_transformation = lidar.sensor.get_transform()
         return {
             'lidar_pose': [
                 lidar_transformation.location.x,
@@ -189,9 +182,9 @@ def load_ego_data(self):
         }
         return data
 
-    def load_plan_trajectory(self):
+    def load_plan_trajectory(self, agent):
         data = {'RSU': True}
-        if self.agent is not None:
+        if agent is not None:
             trajectory_deque = \
                 self.agent.get_local_planner().get_trajectory()
             trajectory_list = []
diff --git a/opencda/core/sensing/perception/coperception_manager.py b/opencda/core/sensing/perception/coperception_manager.py
index 7d5240d1..a9504467 100644
--- a/opencda/core/sensing/perception/coperception_manager.py
+++ b/opencda/core/sensing/perception/coperception_manager.py
@@ -8,31 +8,29 @@ def __init__(self, vid, v2x_manager, coperception_libs):
         self.coperception_libs = coperception_libs
 
     def communicate(self):
-        data = {str(self.vid): {'is_ego': True, 'data': self.v2x_manager}}
+        data = {}
         if self.v2x_manager is not None:
             for vid, vm in self.v2x_manager.cav_nearby.items():
-                data.update({str(vid): {'is_ego': False, 'data': vm}})
+                data.update({str(vid): vm})
         return data
 
-    def load_ego(self):
-        return {'is_ego': True, 'data': self.v2x_manager}
+    def prepare_data(self,
+                     cav_id,
+                     camera,
+                     lidar,
+                     pos,
+                     agent,
+                     is_ego):
 
-    def prepare_data(self, lidar, ego_pos, cav_id, nearby_cav_dict):
-        lidar = nearby_cav_dict['data'].get_ego_lidar()
-        ego_pos = nearby_cav_dict['data'].get_ego_pos()
-        camera = nearby_cav_dict['data'].get_ego_rgb_image()
-        if lidar is None or ego_pos is None:
-            return
-        # stores the required field
         data = {cav_id: OrderedDict()}
-        data[cav_id]['ego'] = nearby_cav_dict['is_ego']
+        data[cav_id]['ego'] = is_ego
         data[cav_id]['time_delay'] = self.coperception_libs.time_delay
         data[cav_id]['params'] = {}
         camera_data = self.coperception_libs.load_camera_data(lidar, camera)
         ego_data = self.coperception_libs.load_ego_data()
-        plan_trajectory_data = self.coperception_libs.load_plan_trajectory()
-        lidar_pose_data = self.coperception_libs.load_cur_lidar_pose()
-        vehicles = self.coperception_libs.load_vehicles(cav_id, ego_pos)
+        plan_trajectory_data = self.coperception_libs.load_plan_trajectory(agent)
+        lidar_pose_data = self.coperception_libs.load_cur_lidar_pose(lidar)
+        vehicles = self.coperception_libs.load_vehicles(cav_id, pos, lidar)
         data[cav_id]['params'].update(plan_trajectory_data)
         data[cav_id]['params'].update(camera_data)
         data[cav_id]['params'].update(ego_data)
diff --git a/opencda/core/sensing/perception/perception_manager.py b/opencda/core/sensing/perception/perception_manager.py
index 5f91b301..47b48b59 100644
--- a/opencda/core/sensing/perception/perception_manager.py
+++ b/opencda/core/sensing/perception/perception_manager.py
@@ -506,7 +506,6 @@ def detect(self, ego_pos):
             'traffic_lights': []
         }
         if not self.activate:
-            self.search_nearby_cav()
             objects = self.deactivate_mode(objects)
         else:
             if self.cooperative:
@@ -524,21 +523,33 @@ def coperception_mode(self, objects):
         if self.lidar.data is None:
             return objects
 
-        enable_communicate = True
+        enable_communicate = False
         data = OrderedDict()
+
+        ego_data = self.co_manager.prepare_data(
+            cav_id=self.id,
+            camera=self.rgb_camera,
+            lidar=self.lidar,
+            pos=self.ego_pos,
+            agent=self.behavior_agent,
+            is_ego=True
+        )
+        data.update(ego_data)
+
         if enable_communicate:
             nearby_objects = self.co_manager.communicate()
-            for vid, nearby_cav_dict in nearby_objects.items():
-                nearby = self.co_manager.prepare_data(self.lidar, self.ego_pos, vid, nearby_cav_dict)
-                if nearby is None:
-                    return objects
-                data.update(nearby)
-        else:
-            ego_dict = {'is_ego': True, 'data': self.v2x_manager}
-            ego_data = self.co_manager.prepare_data(self.lidar, self.ego_pos, self.id, ego_dict)
-            if ego_data is None:
-                return objects
-            data.update(ego_data)
+            for vid, nearby_data_dict in nearby_objects.items():
+                nearby_vm = nearby_data_dict['vehicle_manager']
+                nearby_v2x_manager = nearby_data_dict['v2x_manager']
+                nearby_data = self.co_manager.prepare_data(
+                    cav_id=vid,
+                    camera=nearby_v2x_manager.get_ego_rgb_image(),
+                    lidar=nearby_v2x_manager.get_ego_lidar(),
+                    pos=nearby_v2x_manager.get_ego_pos(),
+                    agent=nearby_vm.agent,
+                    is_ego=False
+                )
+                data.update(nearby_data)
         # inference
         reformat_data_dict = self.ml_manager.opencood_dataset.get_item_test(data)
         output_dict = self.ml_manager.opencood_dataset.collate_batch_test(
@@ -546,7 +557,7 @@ def coperception_mode(self, objects):
         batch_data = self.ml_manager.to_device(output_dict)
         predict_box_tensor, predict_score, gt_box_tensor = self.ml_manager.inference(batch_data)
         # self.ml_manager.show_vis(pred_box_tensor, gt_box_tensor, batch_data)
-        self.ml_manager.evaluate_average_precision()
+        # self.ml_manager.evaluate_final_average_precision()
 
         # plot
         if self.lidar_visualize:
diff --git a/opencda/customize/ml_libs/opencood_manager.py b/opencda/customize/ml_libs/opencood_manager.py
index 5612b7b1..1e8079c3 100644
--- a/opencda/customize/ml_libs/opencood_manager.py
+++ b/opencda/customize/ml_libs/opencood_manager.py
@@ -85,6 +85,7 @@ def inference(self, batch_data):
                                    gt_box_tensor,
                                    self.result_stat,
                                    0.7)
+
         return pred_box_tensor, pred_score, gt_box_tensor
 
     def evaluate_average_precision(self):
@@ -104,6 +105,14 @@ def evaluate_average_precision(self):
             0.7: {'tp': [], 'fp': [], 'gt': 0}
         }
 
+    def evaluate_final_average_precision(self):
+        ap_30, mrec_30, mpre_30 = calculate_ap(self.result_stat, 0.30)
+        ap_50, mrec_50, mpre_50 = calculate_ap(self.result_stat, 0.50)
+        ap_70, mrec_70, mpre_70 = calculate_ap(self.result_stat, 0.70)
+        print('The Average Precision at IOU 0.3 is %.2f, '
+              'The Average Precision at IOU 0.5 is %.2f, '
+              'The Average Precision at IOU 0.7 is %.2f' % (ap_30, ap_50, ap_70))
+
     def show_vis(self, pred_box_tensor, gt_box_tensor, batch_data):
         vis_save_path = os.path.join(self.opt.model_dir, 'vis')
         if not os.path.exists(vis_save_path):
diff --git a/opencda/scenario_testing/config_yaml/v2xp_online_carla.yaml b/opencda/scenario_testing/config_yaml/v2xp_online_carla.yaml
index 9c13b071..031f77ee 100644
--- a/opencda/scenario_testing/config_yaml/v2xp_online_carla.yaml
+++ b/opencda/scenario_testing/config_yaml/v2xp_online_carla.yaml
@@ -146,10 +146,6 @@ vehicle_base: &vehicle_base
     yaw_noise: 0.0
     speed_noise: 0.0
     lag: 0
-  cooperative:
-    options:
-      - lidar
-      # - camera uncomment camera if collecting the raw rgb camera image
 
 
 # define the background traffic control by carla
@@ -168,15 +164,15 @@ carla_traffic_manager:
   # Used only when vehicle_list is a number.
   #  x_min, x_max, y_min, y_max, x_step, y_step, vehicle_num
   range:
-    - [-11, 12, 100, 200, 3.5, 15, 10]
+    - [-11, 12, 100, 200, 3.5, 15, 25]
 
 
 # define scenario. In this scenario, a 4-vehicle platoon already exists.
 scenario:
-  rsu_list:
-    - <<: *rsu_base
-      spawn_position: [12.00, 192.31, 3.0]
-      id: -1
+#  rsu_list:
+#    - <<: *rsu_base
+#      spawn_position: [12.00, 192.31, 3.0]
+#      id: -1
 
   single_cav_list:
     - <<: *vehicle_base
diff --git a/opencda/scenario_testing/v2xp_online_carla.py b/opencda/scenario_testing/v2xp_online_carla.py
index a1be1d34..96ddea2d 100644
--- a/opencda/scenario_testing/v2xp_online_carla.py
+++ b/opencda/scenario_testing/v2xp_online_carla.py
@@ -39,8 +39,11 @@ def run_scenario(opt, config_yaml):
         single_cav_list = \
             scenario_manager.create_vehicle_manager(application=['single', 'cooperative'],
                                                     data_dump=False)
-        rsu_list = \
-            scenario_manager.create_rsu_manager(data_dump=False)
+        # single_cav_list = \
+        #     scenario_manager.create_vehicle_manager(application=['single'],
+        #                                             data_dump=False)
+        # rsu_list = \
+        #     scenario_manager.create_rsu_manager(data_dump=False)
 
         # create background traffic in carla
         traffic_manager, bg_veh_list = \
@@ -69,12 +72,13 @@ def run_scenario(opt, config_yaml):
                 control = single_cav.run_step()
                 single_cav.vehicle.apply_control(control)
 
-            for rsu in rsu_list:
-                rsu.update_info()
-                rsu.run_step()
+            # for rsu in rsu_list:
+            #     rsu.update_info()
+            #     rsu.run_step()
 
     finally:
         eval_manager.evaluate()
+        cav_world.ml_manager.evaluate_final_average_precision()
 
         if opt.record:
             scenario_manager.client.stop_recorder()
@@ -83,7 +87,7 @@ def run_scenario(opt, config_yaml):
 
         for v in single_cav_list:
             v.destroy()
-        for r in rsu_list:
-            r.destroy()
+        # for r in rsu_list:
+        #     r.destroy()
         for v in bg_veh_list:
             v.destroy()
diff --git a/opencood/LICENSE b/opencood/LICENSE
new file mode 100644
index 00000000..758e8d95
--- /dev/null
+++ b/opencood/LICENSE
@@ -0,0 +1,10 @@
+
+Academic Software License: © 2021 UCLA Mobility Lab (“Institution”).  Academic or nonprofit researchers are permitted to use this Software (as defined below) subject to Paragraphs 1-3:
+
+Institution hereby grants to you free of charge, so long as you are an academic or nonprofit researcher, a nonexclusive license under Institution’s copyright ownership interest in this software and any derivative works made by you thereof (collectively, the “Software”) to use, copy, and make derivative works of the Software solely for educational or academic research purposes, in all cases subject to the terms of this Academic Software License. Except as granted herein, all rights are reserved by Institution, including the right to pursue patent protection of the Software.
+
+Please note you are prohibited from further transferring the Software -- including any derivatives you make thereof -- to any person or entity. Failure by you to adhere to the requirements in Paragraphs 1 and 2 will result in immediate termination of the license granted to you pursuant to this Academic Software License effective as of the date you first used the Software.
+
+IN NO EVENT SHALL INSTITUTION BE LIABLE TO ANY ENTITY OR PERSON FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, INCLUDING LOST PROFITS, ARISING OUT OF THE USE OF THIS SOFTWARE, EVEN IF INSTITUTION HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. INSTITUTION SPECIFICALLY DISCLAIMS ANY AND ALL WARRANTIES, EXPRESS AND IMPLIED, INCLUDING, BUT NOT LIMITED TO, ANY IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE SOFTWARE IS PROVIDED “AS IS.” INSTITUTION HAS NO OBLIGATION TO PROVIDE MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS OF THIS SOFTWARE.
+
+Commercial entities: please contact the UCLA Mobility Lab at jiaqima@ucla.edu for licensing opportunities.
diff --git a/opencood/README.md b/opencood/README.md
new file mode 100644
index 00000000..cab6f05a
--- /dev/null
+++ b/opencood/README.md
@@ -0,0 +1,238 @@
+<div align="center">
+  <img src="images/opencood.png" width="600"/>
+  <div>&nbsp;</div>
+
+  <div>&nbsp;</div>
+</div>
+
+[![paper](https://img.shields.io/badge/arXiv-Paper-<COLOR>.svg)](https://arxiv.org/pdf/2109.07644.pdf)
+[![Documentation Status](https://readthedocs.org/projects/opencood/badge/?version=latest)](https://opencood.readthedocs.io/en/latest/?badge=latest) 
+[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) 
+
+OpenCOOD is an <strong>Open</strong> <strong>COO</strong>perative <strong>D</strong>etection framework for autonomous driving. It is also the official implementation of the <strong> ICRA 2022  </strong>
+paper [OPV2V.](https://arxiv.org/abs/2109.07644)
+
+<p align="center">
+<img src="images/demo1.gif" width="600" alt="" class="img-responsive">
+<img src="images/camera_demo.gif" width="600"  alt="" class="img-responsive">
+</p>
+
+## News:
+- 12/28/2022: OpenCOOD now support multi-gpu training.
+- 12/21/2022: V2XSet (ECCV2022) is supported by OpenCOOD now!
+- 12/16/2022: Both spconv 1.2.1 and spconv 2.x are supported! 
+- 12/04/2022: The log replay tool for OPV2V is online now! With 
+this toolbox, you can 100% replay all the events in the offline dataset and add/change any sensors/groundtruth you
+want to explore the tasks that the origin dataset do not support. Check [here](logreplay/README.md) to see more details.
+- 09/15/2022: So far OpenCOOD has supported several top conference papers, including ECCV,ICRA,CoRL,NeurIPS,WACV! The bottom of this project page lists the detailed information.
+## Features
+- Provide easy data API for multiple popular multi-agent perception dataset:
+  - [x] [OPV2V [ICRA2022]](https://mobility-lab.seas.ucla.edu/opv2v/)
+  - [x] [V2XSet [ECCV2022]](https://arxiv.org/pdf/2203.10638.pdf)
+  - [ ] [DAIR-V2X [CVPR2022]](https://arxiv.org/abs/2204.05575)
+- Provide APIs to allow users use different sensor modalities
+  - [x] LiDAR APIs
+  - [ ] Camera APIs
+  - [ ] Radar APIs
+- Provide multiple SOTA 3D detection backbone:
+    - [X] [PointPillar](https://arxiv.org/abs/1812.05784)
+    - [X] [Pixor](https://arxiv.org/abs/1902.06326)
+    - [X] [VoxelNet](https://arxiv.org/abs/1711.06396)
+    - [X] [SECOND](https://www.mdpi.com/1424-8220/18/10/3337)
+- Support multiple sparse convolution versions
+  - [X] Spconv 1.2.1
+  - [X] Spconv 2.x
+- Support  SOTA multi-agent perception models:
+    - [x] [Attentive Fusion [ICRA2022]](https://arxiv.org/abs/2109.07644)
+    - [x] [Cooper [ICDCS]](https://arxiv.org/abs/1905.05265)
+    - [x] [F-Cooper [SEC2019]](https://arxiv.org/abs/1909.06459)
+    - [x] [V2VNet [ECCV2022]](https://arxiv.org/abs/2008.07519)
+    - [x] [FPV-RCNN [RAL2022]](https://arxiv.org/pdf/2109.11615.pdf)
+    - [ ] [DiscoNet [NeurIPS2021]](https://arxiv.org/abs/2111.00643)
+    - [x] [V2X-ViT [ECCV2022]](https://github.com/DerrickXuNu/v2x-vit)
+    - [x] [CoBEVT [CoRL2022]](https://arxiv.org/abs/2207.02202)  
+    - [ ] [AdaFusion [WACV2023]](https://arxiv.org/abs/2208.00116)  
+    - [x] [Where2comm [NeurIPS2022]](https://arxiv.org/abs/2209.12836)
+
+- **Provide a convenient log replay toolbox for OPV2V dataset.** Check [here](logreplay/README.md) to see more details.
+
+## Data Downloading
+All the data can be downloaded from [google drive](https://drive.google.com/drive/folders/1dkDeHlwOVbmgXcDazZvO6TFEZ6V_7WUu). If you have a good internet, you can directly
+download the complete large zip file such as `train.zip`. In case you suffer from downloading large fiels, we also split each data set into small chunks, which can be found 
+in the directory ending with `_chunks`, such as `train_chunks`. After downloading, please run the following command to each set to merge those chunks together:
+```python
+cat train.zip.part* > train.zip
+unzip train.zip
+```
+
+## Installation
+Please refer to [data introduction](https://opencood.readthedocs.io/en/latest/md_files/data_intro.html)
+and [installation](https://opencood.readthedocs.io/en/latest/md_files/installation.html) guide to prepare
+data and install OpenCOOD. To see more details of OPV2V data, please check [our website.](https://mobility-lab.seas.ucla.edu/opv2v/)
+
+## Quick Start
+### Data sequence visualization
+To quickly visualize the LiDAR stream in the OPV2V dataset, first modify the `validate_dir`
+in your `opencood/hypes_yaml/visualization.yaml` to the opv2v data path on your local machine, e.g. `opv2v/validate`,
+and the run the following commond:
+```python
+cd ~/OpenCOOD
+python opencood/visualization/vis_data_sequence.py [--color_mode ${COLOR_RENDERING_MODE}]
+```
+Arguments Explanation:
+- `color_mode` : str type, indicating the lidar color rendering mode. You can choose from 'constant', 'intensity' or 'z-value'.
+
+
+### Train your model
+OpenCOOD uses yaml file to configure all the parameters for training. To train your own model
+from scratch or a continued checkpoint, run the following commonds:
+```python
+python opencood/tools/train.py --hypes_yaml ${CONFIG_FILE} [--model_dir  ${CHECKPOINT_FOLDER} --half]
+```
+Arguments Explanation:
+- `hypes_yaml`: the path of the training configuration file, e.g. `opencood/hypes_yaml/second_early_fusion.yaml`, meaning you want to train
+an early fusion model which utilizes SECOND as the backbone. See [Tutorial 1: Config System](https://opencood.readthedocs.io/en/latest/md_files/config_tutorial.html) to learn more about the rules of the yaml files.
+- `model_dir` (optional) : the path of the checkpoints. This is used to fine-tune the trained models. When the `model_dir` is
+given, the trainer will discard the `hypes_yaml` and load the `config.yaml` in the checkpoint folder.
+- `half` (optional): If set, the model will be trained with half precision. It cannot be set with multi-gpu training togetger.
+
+To train on **multiple gpus**, run the following command:
+```
+CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4  --use_env opencood/tools/train.py --hypes_yaml ${CONFIG_FILE} [--model_dir  ${CHECKPOINT_FOLDER}]
+```
+
+
+### Test the model
+Before you run the following command, first make sure the `validation_dir` in config.yaml under your checkpoint folder
+refers to the testing dataset path, e.g. `opv2v_data_dumping/test`.
+
+```python
+python opencood/tools/inference.py --model_dir ${CHECKPOINT_FOLDER} --fusion_method ${FUSION_STRATEGY} [--show_vis] [--show_sequence]
+```
+Arguments Explanation:
+- `model_dir`: the path to your saved model.
+- `fusion_method`: indicate the fusion strategy, currently support 'early', 'late', and 'intermediate'.
+- `show_vis`: whether to visualize the detection overlay with point cloud.
+- `show_sequence` : the detection results will visualized in a video stream. It can NOT be set with `show_vis` at the same time.
+
+The evaluation results  will be dumped in the model directory.
+
+## Benchmark and model zoo
+### Results on OPV2V LiDAR-track (AP@0.7 for no-compression/ compression)
+
+|                    | Spconv Version| Backbone   | Fusion Strategy  | Bandwidth (Megabit), <br/> before/after compression| Default Towns    |Culver City| Download |
+|--------------------| -------|--------   | ---------------  | ---------------                | -------------    |-----------| -------- |
+| Naive Late         | 1.2.1 | PointPillar        | Late      |    **0.024**/**0.024** |   0.781/0.781        | 0.668/0.668         |    [url](https://drive.google.com/file/d/1WTKooW6k0exLqoIE5Czqy6ptycYlgKZz/view?usp=sharing)   |
+| [Cooper](https://arxiv.org/abs/1905.05265)   | 1.2.1     | PointPillar        | Early  |   7.68/7.68   | 0.800/x         | 0.696/x       | [url](https://drive.google.com/file/d/16hVkjpBUGfCByHjSmT8atCSZ2z2KuJBj/view?usp=share_link)     | 
+| [Attentive Fusion](https://arxiv.org/abs/2109.07644)  | 1.2.1        | PointPillar        | Intermediate  | 126.8/1.98   | 0.815/0.810       | 0.735/0.731        | [url](https://drive.google.com/file/d/11pG0kf2uR9N_o_ACBi_zfd7flGCgZt40/view?usp=sharing)     | 
+| [F-Cooper](https://arxiv.org/abs/1909.06459)   | 1.2.1       | PointPillar        | Intermediate  | 72.08/1.12    | 0.790/0.788     | 0.728/0.726        | [url](https://drive.google.com/file/d/1CjXu9Y2ZTzJA6Oo3hnqFhbTqBVKq3mQb/view?usp=sharing)     | 
+| [V2VNet](https://arxiv.org/abs/2008.07519)  | 1.2.1        | PointPillar        | Intermediate  | 72.08/1.12    | **0.822**/0.814     | 0.734/0.729    | [url](https://drive.google.com/file/d/14xl_gNEIHcDw-SvQyO1ioQwyzGym-tKX/view?usp=sharing)     | 
+| [FPV-RCNN](https://arxiv.org/abs/2109.11615)   | 1.2.1       | PV-RCNN        | Intermediate(2 stage)  | 0.24/0.24    | 0.820/0.820     | 0.763/**0.763**    | [url](https://drive.google.com/file/d/1iOVi7holJ-Cu2P3dRv5HmOWlB5lkLukJ/view)     | 
+| [CoBEVT]( https://arxiv.org/pdf/2207.02202.pdf)  | 2.0       | PointPillar      | Intermediate  |  72.08/1.12     | **0.861**/**0.836**     | **0.773**/0.730   | [url](https://drive.google.com/drive/folders/1h5aawoH5l4cjWJBUSdQBVWd9B79nukWK?usp=share_link)     | 
+| Naive Late    | 1.2.1      | VoxelNet        | Late  | **0.024**/**0.024**    | 0.738/0.738          | 0.588/0.588        | [url]()    |
+| Cooper   | 1.2.1  | VoxelNet        | Early   |   7.68/7.68  | 0.758/x        | 0.677/x        | [url](https://drive.google.com/file/d/14WD7iLLyyCJJ3lApbYYdr5KOUM1ACnve/view?usp=sharing)     | 
+| Attentive Fusion    | 1.2.1     | VoxelNet        | Intermediate |   576.71/1.12   | **0.864**/**0.852**        | **0.775**/**0.746**       | [url](https://drive.google.com/file/d/16q8CfcB8dS4EVhJMvvEfn0gM2ynxZB3E/view?usp=sharing)      | 
+| Naive Late    | 1.2.1      | SECOND        | Late |  **0.024**/**0.024**    |  0.775/0.775        |0.682/0.682        | [url](https://drive.google.com/file/d/1VG_FKe1mKagPVGXH7UGHpyaM5q3cxtD8/view?usp=sharing)      |
+| Cooper  | 1.2.1   | SECOND        | Early  |   7.68/7.68   |  0.813/x       |  0.738/x     | [url](https://drive.google.com/file/d/1Z9io1VNcU-urcRW8l0ogWCTVCB53mw4N/view?usp=sharing)     | 
+| Attentive     | 1.2.1     | SECOND        | Intermediate |  63.4/0.99     |   **0.826**/**0.783**     | **0.760**/**0.760**    | [url](https://drive.google.com/file/d/1zEB8EyZ0X-WQykHFOM0pVwI8jXunRz1Z/view?usp=sharing)      | 
+| Naive Late    | 1.2.1      | PIXOR        | Late |    **0.024**/**0.024** |    0.578/0.578       |  0.360/0.360      | [url]()      |
+| Cooper | 1.2.1    | PIXOR        | Early |   7.68/7.68    |   0.678/x      | **0.558**/x      | [url](https://drive.google.com/file/d/1ZDLjtizZCuV6D92LloEPKRIw-LqxfE1j/view?usp=sharing)     | 
+| Attentive   | 1.2.1       | PIXOR        | Intermediate  |   313.75/1.22  |  **0.687**/**0.612**      | 0.546/**0.492**       | [url]()      |
+
+**Note**: 
+* We suggest using **PointPillar** as the backbone when you are creating your method and try to compare with
+our benchmark, as we implement most of the SOTA methods with this backbone only.
+* We assume the transimssion rate is 27Mbp/s. Considering the frequency of LiDAR is 10Hz, the 
+bandwidth requirement should be less than **2.7Mbp** to avoid severe delay. 
+* A 'x' in the benchmark table represents the bandwidth requirement is too large, which 
+can not be considered to employ in practice.
+
+
+### Results of BEV semantic segmentation on OPV2V camera-track (IoU)
+
+|                    | Backbone   | Fusion Strategy  | Vehicles| Road Surface   |Lane| Download |
+|--------------------| --------   | ---------------  | ---------------                | -------------    |-----------| -------- |
+| No Fusion        | CVT        | No Fusion      |    37.7 |   57.8        | 43.7     |    [None]()   |
+| Map Fusion      | CVT        | Late  |   45.1   |  60.0     | 44.1      | [None]()     | 
+| [Attentive Fusion](https://arxiv.org/abs/2109.07644)         | CVT        | Intermediate  | 51.9  |60.5       | 46.2        | [None]()     | 
+| [F-Cooper](https://arxiv.org/abs/1909.06459)         | CVT        | Intermediate  |52.5    | 60.4    | 46.5       | [None]()     | 
+| [V2VNet](https://arxiv.org/abs/2008.07519)         | CVT        | Intermediate  | 53.5     | 60.2     | 47.5   | [None]()     | 
+| [DiscoNet](https://arxiv.org/abs/2109.11615)         | CVT       | Intermediate  | 52.9   |  60.7   | 45.8    | [None]()     | 
+| [FuseBEVT](https://arxiv.org/pdf/2207.02202.pdf)        | CVT        | Intermediate  | 59.0     | 62.1        | 49.2      | [url]()    |
+| [CoBEVT](https://arxiv.org/pdf/2207.02202.pdf)        | SinBEVT        | Intermediate  | **60.4**     | **63.0**          | **53.0**      | [url](https://drive.google.com/drive/folders/1NLzyvMFxuv8Qy52q_OzcNsugTS5JacAT)    |
+
+**Note**: 
+To play with OPV2V camera data, please check here: https://github.com/DerrickXuNu/CoBEVT
+
+### Results of 3D Detection on V2XSet LiDAR-Track
+| Method       | Spconv Version | Backbone    | Perfect AP@0.5 | Perfect AP@0.7 | Noisy AP@0.5 | Noisy AP@0.7 | Download Link                                                            |
+|--------------|----------------|-------------|----------------|----------------|--------------|--------------|--------------------------------------------------------------------------|
+| No Fusion    |       2.0      | PointPillar | 60.6           | 40.2           | 60.6         | 40.2         |                                                                          |
+| Late Fusion  |       2.0      | PointPillar | 72.7           | 62.0           | 54.9         | 30.7         |                                                                          |
+| Early Fusion |       2.0      | PointPillar | 81.9           | 71.0           | 72.0         | 38.4         |                                                                          |
+| [F-Cooper](https://arxiv.org/abs/1909.06459) |       2.0      | PointPillar | 84.0           | 68.0           | 71.5         | 46.9         |                                                                          |
+| [Attentive Fusion](https://arxiv.org/abs/2109.07644)     |       2.0      | PointPillar | 80.7           | 66.4           | 70.9         | 48.7         |                                                                          |
+| [V2VNet](https://arxiv.org/abs/2008.07519)         |       2.0      | PointPillar | 84.5           | 67.7           | 79.1         | 49.3         |                                                                          |
+| [DiscoNet](https://arxiv.org/abs/2109.11615)      |       2.0      | PointPillar | 84.4           | 69.5           | 79.8         | 54.1         |                
+| [CoBEVT](https://arxiv.org/abs/2207.02202)      |       2.0      | PointPillar |    84.9      |  66.0   | 81.1   | 54.3  | [url](https://drive.google.com/file/d/1aNL2L779C5PBYIA5AjVWWNmld1tulens) ||
+| [Where2Comm](https://arxiv.org/abs/2209.12836)     |       2.0      | PointPillar |   85.5 | 65.4 | 82.0| 53.4 | [url](https://drive.google.com/file/d/1IAcnfLCSPIt9pyfiZcF1RnKOTkbIfUcJ/view?usp=share_link) ||
+| [V2X-ViT](https://arxiv.org/pdf/2203.10638.pdf)      |       2.0      | PointPillar | **88.2**           | **71.2**           | **83.6**         | **61.4**         | [url](https://drive.google.com/drive/folders/1h2UOPP2tNRkV_s6cbKcSfMvTgb8_ZFj9) |
+
+<strong>Important Notes for Training in V2XSet:</strong>
+1. When you train from scratch, please first set `async` and `loc_err` to false to train on perfect setting. Also, set `compression` to 0 at beginning.
+2. After the model on perfect setting converged, set `compression`  to 32 (please change the config yaml in your trained model directory) and continue training on the perfect setting for another 1-2 epoches.
+3. Next, set `async` to true, `async_mode` to 'real', `async_overhead` to 200 or 300, `loc_err` to true, `xyz_std` to 0.2, `rpy_std` to 0.2, and then continue training your model on this noisy setting. Please note that you are free to change these noise setting during training to obtain better performance.
+4. Eventually, use the model fine-tuned on noisy setting as the test model for both perfect and noisy setting.
+
+## Tutorials
+We have a series of tutorials to help you understand OpenCOOD more. Please check the series of our [tutorials](https://opencood.readthedocs.io/en/latest/md_files/config_tutorial.html).
+
+
+## Citation
+ If you are using our OpenCOOD framework or OPV2V dataset for your research, please cite the following paper:
+ ```bibtex
+@inproceedings{xu2022opencood,
+  author = {Runsheng Xu, Hao Xiang, Xin Xia, Xu Han, Jinlong Li, Jiaqi Ma},
+  title = {OPV2V: An Open Benchmark Dataset and Fusion Pipeline for Perception with Vehicle-to-Vehicle Communication},
+  booktitle = {2022 IEEE International Conference on Robotics and Automation (ICRA)},
+  year = {2022}}
+```
+
+## Supported Projects
+OpenCOOD has supported several top conference papers in cooperative perception field.
+
+**Bridging the Domain Gap for Multi-Agent Perception** <br>
+Runsheng Xu, Jinlong Li, Xiaoyu Dong, Hongkai Yu, Jiaqi Ma∗<br>
+*ICRA 2023* <br>
+[[Paper]](https://arxiv.org/pdf/2210.08451.pdf)[[Code]](https://github.com/DerrickXuNu/MPDA)
+
+**Model Agnostic Multi-agent Perception** <br>
+Runsheng Xu, Weizhe Chen, Hao Xiang,  Xin Xia, Lantao Liu, Jiaqi Ma∗<br>
+*ICRA 2023* <br>
+[[Paper]](https://arxiv.org/abs/2203.13168)[[Code]](https://github.com/DerrickXuNu/model-agnostic)
+
+**Where2comm: Communication-Efficient Collaborative Perception via Spatial Confidence Maps** <br>
+Yue Hu, Shaoheng Fang, Zixing Lei, Yiqi Zhong, Siheng Chen<br>
+*Neurips 2022* <br>
+[[Paper]](https://arxiv.org/abs/2209.12836) [[Code]](https://github.774.gs/MediaBrain-SJTU/Where2comm)
+
+**Adaptive Feature Fusion for Cooperative Perception using LiDAR Point Clouds** <br>
+Donghao Qiao, Farhana Zulkernine<br>
+*WACV 2023* <br>
+[[Paper]](https://arxiv.org/abs/2208.00116)
+
+
+**CoBEVT: Cooperative Bird's Eye View Semantic Segmentation with Sparse Transformers** <br>
+Runsheng Xu*, Zhengzhong Tu*, Hao Xiang, Wei Shao, Bolei Zhou, Jiaqi Ma <br>
+*CoRL2022* <br>
+[[Paper]](https://arxiv.org/abs/2207.02202) [[Code]](https://github.com/DerrickXuNu/CoBEVT)
+
+**V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer** <br>
+Runsheng Xu*, Hao Xiang*, Zhengzhong Tu*, Xin Xia, Ming-Hsuan Yang, Jiaqi Ma <br>
+*ECCV2022* <br>
+[[Paper]](https://arxiv.org/abs/2203.10638) [[Code]](https://github.com/DerrickXuNu/v2x-vit) [[Talk]](https://course.zhidx.com/c/MmQ1YWUyMzM1M2I3YzVlZjE1NzM=)
+
+**OPV2V: An Open Benchmark Dataset and Fusion Pipeline for Perception with Vehicle-to-Vehicle Communication** <br>
+Runsheng Xu*, Hao Xiang*, Xin Xia, Jinlong Li, Jiaqi Ma <br>
+*ICRA2022* <br>
+[[Paper]](https://arxiv.org/abs/2109.07644) [[Website]](https://mobility-lab.seas.ucla.edu/opv2v/) [[Code]](https://github.com/DerrickXuNu/OpenCOOD)
diff --git a/opencood/docs/Makefile b/opencood/docs/Makefile
new file mode 100644
index 00000000..d4bb2cbb
--- /dev/null
+++ b/opencood/docs/Makefile
@@ -0,0 +1,20 @@
+# Minimal makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line, and also
+# from the environment for the first two.
+SPHINXOPTS    ?=
+SPHINXBUILD   ?= sphinx-build
+SOURCEDIR     = .
+BUILDDIR      = _build
+
+# Put it first so that "make" without argument is like "make help".
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
diff --git a/opencood/docs/conf.py b/opencood/docs/conf.py
new file mode 100644
index 00000000..68710ba7
--- /dev/null
+++ b/opencood/docs/conf.py
@@ -0,0 +1,65 @@
+# Configuration file for the Sphinx documentation builder.
+#
+# This file only contains a selection of the most common options. For a full
+# list see the documentation:
+# https://www.sphinx-doc.org/en/master/usage/configuration.html
+
+# -- Path setup --------------------------------------------------------------
+
+# If extensions (or modules to document with autodoc) are in another directory,
+# add these directories to sys.path here. If the directory is relative to the
+# documentation root, use os.path.abspath to make it absolute, like shown here.
+#
+import os
+import sys
+# sys.path.insert(0, os.path.abspath('.'))
+sys.path.insert(0, os.path.abspath('./../'))
+
+
+# -- Project information -----------------------------------------------------
+
+project = 'OpenCOOD'
+copyright = '2021, UCLA Mobility Lab'
+author = 'Runsheng Xu, Hao Xiang, Jiaqi Ma'
+
+# The full version, including alpha/beta/rc tags
+release = '0.1'
+
+
+# -- General configuration ---------------------------------------------------
+
+# Add any Sphinx extension module names here, as strings. They can be
+# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
+# ones.
+extensions = [
+    'sphinx.ext.autodoc',
+    'sphinx.ext.napoleon',
+    'sphinx.ext.doctest',
+    'sphinx.ext.intersphinx',
+    'sphinx.ext.todo',
+    'sphinx.ext.coverage',
+    'sphinx.ext.mathjax',
+    'recommonmark',
+]
+
+# Add any paths that contain templates here, relative to this directory.
+templates_path = ['_templates']
+
+# List of patterns, relative to source directory, that match files and
+# directories to ignore when looking for source files.
+# This pattern also affects html_static_path and html_extra_path.
+exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
+
+todo_include_todos = False
+
+# -- Options for HTML output -------------------------------------------------
+
+# The theme to use for HTML and HTML Help pages.  See the documentation for
+# a list of builtin themes.
+#
+html_theme = 'sphinx_rtd_theme'
+
+# Add any paths that contain custom static files (such as style sheets) here,
+# relative to this directory. They are copied after the builtin static files,
+# so a file named "default.css" will overwrite the builtin "default.css".
+html_static_path = ['_static']
diff --git a/opencood/docs/index.rst b/opencood/docs/index.rst
new file mode 100644
index 00000000..64781a14
--- /dev/null
+++ b/opencood/docs/index.rst
@@ -0,0 +1,57 @@
+.. OpenCDA documentation master file, created by
+   sphinx-quickstart on Fri Jul  2 11:48:53 2021.
+   You can adapt this file completely to your liking, but it should at least
+   contain the root `toctree` directive.
+
+Welcome to OpenCOOD's documentation!
+===================================
+OpenCOOD is an open-source cooperative detection framework for autonomous driving. It provides SOTA cooperative detection algorithms,
+convenient APIs for the large-scale simulated V2V perception dataset `OPV2V <https://mobility-lab.seas.ucla.edu/opv2v/>`_, and a set of useful tools for log replay.
+
+In collaboration with `OpenCDA <https://github.com/ucla-mobility/OpenCDA>`_ , OpenCOOD is mainly focus on offline cooperative perception training and testing. If you are
+interested in online cooperative perception and the corresponding closed-loop simulation test, OpenCDA will be the best tool.
+
+OpenCOOD is a work in progress. Many features on the roadmap are being continuously developed. We welcome your contribution and please visit our Github repo
+for the latest release.
+
+    `OpenCOOD source code on  Github <https://github.com/DerrickXuNu/OpenCOOD>`_
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Getting Started
+
+   md_files/data_intro.md
+   md_files/installation.md
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Tutorials
+
+   md_files/config_tutorial.md
+   md_files/data_annotation_tutorial.md
+   md_files/logic_flow.md
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Additional Information
+
+   md_files/contributor.md
+
+**Citing OpenCOOD**\ :
+
+If you are using our OpenCOOD framework or codes for your development, please cite the following paper::
+
+    @inproceedings{xu2022opencood,
+      author = {Runsheng Xu, Hao Xiang, Xin Xia, Xu Han, Jinlong Li, Jiaqi Ma},
+      title = {OPV2V: An Open Benchmark Dataset and Fusion Pipeline for Perception with Vehicle-to-Vehicle Communication},
+      booktitle = {2022 IEEE International Conference on Robotics and Automation (ICRA)},
+      year = {2022}}
+
+Also, under this LICENSE, OpenCOOD is for non-commercial research only. Researchers can modify the source code for their own research only.
+
+Indices and tables
+==================
+
+* :ref:`genindex`
+* :ref:`modindex`
+* :ref:`search`
diff --git a/opencood/docs/make.bat b/opencood/docs/make.bat
new file mode 100644
index 00000000..2119f510
--- /dev/null
+++ b/opencood/docs/make.bat
@@ -0,0 +1,35 @@
+@ECHO OFF
+
+pushd %~dp0
+
+REM Command file for Sphinx documentation
+
+if "%SPHINXBUILD%" == "" (
+	set SPHINXBUILD=sphinx-build
+)
+set SOURCEDIR=.
+set BUILDDIR=_build
+
+if "%1" == "" goto help
+
+%SPHINXBUILD% >NUL 2>NUL
+if errorlevel 9009 (
+	echo.
+	echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
+	echo.installed, then set the SPHINXBUILD environment variable to point
+	echo.to the full path of the 'sphinx-build' executable. Alternatively you
+	echo.may add the Sphinx directory to PATH.
+	echo.
+	echo.If you don't have Sphinx installed, grab it from
+	echo.http://sphinx-doc.org/
+	exit /b 1
+)
+
+%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+goto end
+
+:help
+%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+
+:end
+popd
diff --git a/opencood/docs/md_files/config_tutorial.md b/opencood/docs/md_files/config_tutorial.md
new file mode 100644
index 00000000..70844bed
--- /dev/null
+++ b/opencood/docs/md_files/config_tutorial.md
@@ -0,0 +1,139 @@
+## Tutorial 1: Learn about Config System
+
+---
+We incorporate modular and inheritance design into the config system to enable users conveniently
+modify the model/training/inference parameters. Specifically, we use **yaml** files to configure all the 
+important parameters.
+
+### Config File Location
+All the yaml files should be saved in `opencood/hypes_yaml`, and users should use the `load_yaml()` function in [`opencood/hypes_yaml/yaml_utils.py`](https://github.com/DerrickXuNu/OpenCOOD/blob/main/opencood/hypes_yaml/yaml_utils.py#L8) to load the parameters into a dictionary. 
+
+### Config Name Style
+We follow the below style to name config yaml files.
+```python
+{backbone}_{fusion_strategy}.yaml
+```
+
+### A concrete example
+Now let's go through the `point_pillar_intermediate_fusion.yaml` as an example.
+```yaml
+name: point_pillar_intermediate_fusion # this parameter together with the current timestamp will  define the name of the saved folder for the model. 
+root_dir: "v2xset/train" # this is where the training data locate. It can be either opv2v/train or v2xset/train
+validate_dir: "v2xset/validate" # during training, it defines the validation folder. during testing, it defines the testing folder path.
+
+yaml_parser: "load_point_pillar_params" # we need specific loading functions for different backbones.
+train_params: # the common training parameters
+  batch_size: &batch_size 2
+  epoches: 60
+  eval_freq: 1
+  save_freq: 1
+
+wild_setting: # setting related to noise
+  async: true
+  async_mode: 'sim'
+  async_overhead: 100
+  backbone_delay: 10
+  data_size: 1.06
+  loc_err: true
+  ryp_std: 0.2
+  seed: 25
+  transmission_speed: 27
+  xyz_std: 0.2
+
+fusion:
+  core_method: 'IntermediateFusionDataset' # LateFusionDataset, EarlyFusionDataset, and IntermediateFusionDataset are supported
+  args:
+    cur_ego_pose_flag: True
+    # when the cur_ego_pose_flag is set to True, there is no time gap
+    # between  the time when the LiDAR data is captured by connected
+    # agents and when the extracted features are received by
+    # the ego vehicle, which is equal to implement STCM. When set to False,
+    # STCM has to be used. To validate STCM, V2X-ViT will set this as False.
+
+# preprocess-related
+preprocess:
+  # options: BasePreprocessor, SpVoxelPreprocessor, BevPreprocessor
+  core_method: 'SpVoxelPreprocessor'
+  args:
+    voxel_size: &voxel_size [0.4, 0.4, 4] # the voxel resolution for PointPillar
+    max_points_per_voxel: 32 # maximum points allowed in each voxel
+    max_voxel_train: 32000 # the maximum voxel number during training
+    max_voxel_test: 70000 # the maximum voxel number during testing
+  # LiDAR point cloud cropping range
+  cav_lidar_range: &cav_lidar [-140.8, -40, -3, 140.8, 40, 1]
+
+# data augmentation options.
+data_augment:
+  - NAME: random_world_flip
+    ALONG_AXIS_LIST: [ 'x' ]
+
+  - NAME: random_world_rotation
+    WORLD_ROT_ANGLE: [ -0.78539816, 0.78539816 ]
+
+  - NAME: random_world_scaling
+    WORLD_SCALE_RANGE: [ 0.95, 1.05 ]
+
+# post processing related.
+postprocess:
+  core_method: 'VoxelPostprocessor' # VoxelPostprocessor and BevPostprocessor are supported
+  anchor_args: # anchor generator parameters
+    cav_lidar_range: *cav_lidar # the range is consistent with the lidar cropping range to generate the correct ancrhors
+    l: 3.9 # the default length of the anchor
+    w: 1.6 # the default width
+    h: 1.56 # the default height
+    r: [0, 90] # the yaw angles. 0, 90 meaning for each voxel, two anchors will be generated with 0 and 90 degree yaw angle
+    feature_stride: 2 # the feature map is shrank twice compared the input voxel tensor
+    num: &achor_num 2 # for each location in the feature map, 2 anchors will be generated
+  target_args: # used to generate positive and negative samples for object detection
+    pos_threshold: 0.6 
+    neg_threshold: 0.45
+    score_threshold: 0.20
+  order: 'hwl' # hwl or lwh
+  max_num: 100 # maximum number of objects in a single frame. use this number to make sure different frames have the same dimension in the same batch
+  nms_thresh: 0.15
+
+# model related
+model:
+  core_method: point_pillar_opv2v # trainer will load the corresponding model python file with the same name
+  args: # detailed parameters of the point pillar model
+    voxel_size: *voxel_size 
+    lidar_range: *cav_lidar
+    anchor_number: *achor_num
+
+    pillar_vfe:
+      use_norm: true
+      with_distance: false
+      use_absolute_xyz: true
+      num_filters: [64]
+    point_pillar_scatter:
+      num_features: 64
+
+    base_bev_backbone:
+      layer_nums: [3, 5, 8]
+      layer_strides: [2, 2, 2]
+      num_filters: [64, 128, 256]
+      upsample_strides: [1, 2, 4]
+      num_upsample_filter: [128, 128, 128]
+      compression: 0 # whether to compress the features before fusion to reduce the bandwidth
+      backbone_fix: false # whether fix the pointpillar backbone weights during training.
+    anchor_num: *achor_num
+
+loss: # loss function
+  core_method: point_pillar_loss # trainer will load the loss function with the same name
+  args:
+    cls_weight: 1.0 # classification weights
+    reg: 2.0 # regression weights
+
+optimizer: # optimzer setup
+  core_method: Adam # the name has to exist in Pytorch optimizer library
+  lr: 0.002
+  args:
+    eps: 1e-10
+    weight_decay: 1e-4
+
+lr_scheduler: # learning rate schedular
+  core_method: multistep #step, multistep and Exponential are supported
+  gamma: 0.1
+  step_size: [15, 30]
+
+```
diff --git a/opencood/docs/md_files/contributor.md b/opencood/docs/md_files/contributor.md
new file mode 100644
index 00000000..fc72cfb5
--- /dev/null
+++ b/opencood/docs/md_files/contributor.md
@@ -0,0 +1,19 @@
+## About Us
+OpenCOOD is brought to you by [UCLA Mobility Lab](https://mobility-lab.seas.ucla.edu/).
+
+### Supervisor
+Dr. Jiaqi Ma (Associate Professor @ UCLA)
+- Lab Principal Investigator
+- [Linkedin](https://www.linkedin.com/in/jiaqi-ma-17037838/)
+- [Google Scholar](https://scholar.google.com/citations?user=S3cQz1AAAAAJ&hl=en)
+
+
+### Core Developer
+Runsheng Xu (Ph.D. Student @ UCLA):
+- [Homepage](https://derrickxunu.github.io/)
+- [Linkedin](https://www.linkedin.com/in/runsheng-xu/)
+
+Hao Xiang (Ph.D. Student @ UCLA):
+- [Homepage](https://xhwind.github.io/)
+- [Linkedin](https://www.linkedin.com/in/hao-xiang-42bb5a1b2/)
+
diff --git a/opencood/docs/md_files/data_annotation_tutorial.md b/opencood/docs/md_files/data_annotation_tutorial.md
new file mode 100644
index 00000000..b19a69df
--- /dev/null
+++ b/opencood/docs/md_files/data_annotation_tutorial.md
@@ -0,0 +1,94 @@
+## Tutorial 2:  Data Annotation Introduction
+
+---
+We save all groundtruth annotations per agent per timestamp in the yaml files. For instance,
+`2021_08_24_21_29_28/4805/000069.yaml` refers to the data annotations with the perspective of te
+agent 4805 at timestamp 69 in the scenario database `2021_08_24_21_29_28`. Here we go through an example:
+
+```yaml
+camera0: # parameters for frontal camera
+  cords: # the x,y,z,roll,yaw,pitch under CARLA map coordinate
+  - 141.35067749023438
+  - -388.642578125
+  - 1.0410505533218384
+  - 0.07589337974786758
+  - 174.18048095703125
+  - 0.20690691471099854
+  extrinsic: # extrinsic matrix from camera to LiDAR
+  - - 0.9999999999999999
+    - -5.1230071481984265e-18
+    - 9.322129061605055e-20
+    - -2.999993025731527
+  - - -2.5011383190939924e-18
+    - 1.0
+    - 1.1458579204685086e-19
+    - -3.934422863949294e-06
+  - - 2.7713237218713775e-20
+    - 3.7310309839064755e-20
+    - 1.0
+    - 0.8999999040861146
+  - - 0.0
+    - 0.0
+    - 0.0
+    - 1.0
+  intrinsic: # camera intrinsic matrix
+  - - 335.639852470912
+    - 0.0
+    - 400.0
+  - - 0.0
+    - 335.639852470912
+    - 300.0
+  - - 0.0
+    - 0.0
+    - 1.0
+camera1: ... # params of right rear camera
+camera2: ... # params of left rear camera
+canera3: ... # params of back camera
+ego_speed: 18.13 # agent's current speed, km/h
+lidar_pose: # LiDAR pose under CARLA map coordinate system
+- 144.33
+- -388.94
+- 1.93
+- 0.078
+- 174.18
+- 0.21
+plan_trajectory: # agent's planning trajectory
+- - 140.
+  - -388
+  - 87
+predicted_ego_pos: # agent's localization (x,y,z,roll,yaw,pitch) gained from GPS
+- 143.78
+- -388.94
+- 0.036
+- 0.080
+- -185.95
+- 0.18
+true_ego_pos: # agent's true localization
+- 143.83
+- -388.89
+- 0.032
+- 0.075
+- 174.18
+- 0.21
+vehicles: # the surrounding vehicles that have at least one LiDAR point hit from the agent
+  4796: # the id of the vehicle (i.e. object)
+    angle: # roll, yaw, pitch under CARLA map coordinate system
+    - 0.096
+    - -177.86
+    - 0.197
+    center: # the relative position from bounding box center to the frontal axis of this vehicle
+    - 0.0004
+    - 0.0005
+    - 0.71
+    extent: # half length, width and height of the vehicle in meter
+    - 2.45
+    - 1.06
+    - 0.75
+    location: # x, y ,z position of the center in the frontal axis of the vehicle under CARLA map coordinate system
+    - 158.55
+    - -385.75
+    - 0.032
+    speed: 19.47 # vehicle's speed
+  4880: ...
+```
+
diff --git a/opencood/docs/md_files/data_intro.md b/opencood/docs/md_files/data_intro.md
new file mode 100644
index 00000000..96d33242
--- /dev/null
+++ b/opencood/docs/md_files/data_intro.md
@@ -0,0 +1,143 @@
+# Data Preparation
+
+---
+OpenCOOD support both **OPV2V (ICRA2022)** and **V2XSet (ECCV2022)** dataset.
+
+## OPV2V
+All the data can be downloaded from [google drive](https://drive.google.com/drive/folders/1dkDeHlwOVbmgXcDazZvO6TFEZ6V_7WUu). If you have a good internet, you can directly
+download the complete large zip file such as `train.zip`. In case you suffer from downloading large fiels, we also split each data set into small chunks, which can be found 
+in the directory ending with `_chunks`, such as `train_chunks`. After downloading, please run the following command to each set to merge those chunks together:
+```python
+cat train.zip.part* > train.zip
+unzip train.zip
+```
+After downloading is finished, please make the file structured as following:
+
+```sh
+OpenCOOD # root of your OpenCOOD
+├── opv2v_data_dumping # the downloaded opv2v data
+│   ├── train
+│   ├── validate
+│   ├── test
+│   ├── test_culvercity
+├── opencood # the core codebase
+
+```
+
+
+OPV2V data is structured as following:
+
+```sh
+opv2v_data_dumping
+├── train # data for training
+│   ├── 2021_08_22_21_41_24  # scenario folder
+│     ├── data_protocol.yaml # the simulation parameters used to collect the data in Carla
+│     └──  1732 # The connected automated vehicle's id 
+│       └── 00000.pcd - 00700.pcd # the point clouds data from timestamp 0 to 700
+│       ├── 00000.yaml - 00700.yaml # corresponding metadata for each timestamp
+│       ├── 00000_camera0.png - 00700_camera0.png # frontal camera images
+│       ├── 00000_camera1.png - 00700_camera1.png # right rear camera images
+│       ├── 00000_camera2.png - 00700_camera2.png # left rear camera images
+│       └── 00000_camera3.png - 00700_camera3.png # back camera images
+├── validate  
+├── test
+├── test_culvercity
+```
+
+### 1. Data Split
+OPV2V dataset can be divided into 4 different folders: `train`, `validation`, `test`, and `test_culvercity`
+- `train`: contains all training data
+- `validate`: used for validation during training
+- `test`: test set for calrla default towns
+- `test_culvercity`: test set for the digital town of Culver City, Los Angeles
+
+### 2. Scenario Database
+OPV2V has 73 scenarios in total, where each of them contains data stream from different agents across different timestamps.
+Each scenario is named by the time it was gathered, e.g., `2021_08_22_21_41_24`.
+
+### 3. Agent Contents
+Under each scenario folder,  the data of every intelligent agent~(i.e. connected automated vehicle) appearing in the current scenario is saved in different folders. Each folder is named by the agent's unique id, e.g., 1732.
+
+In each agent folder, data across different timestamps will be saved. Those timestamps are represented by five digits integers
+as the prefix of the filenames (e.g., 00700.pcd). There are three types of files inside the agent folders: LiDAR point clouds (`.pcd` files), camera images (`.png` files), and metadata (`.yaml` files).
+
+#### 3.1 Lidar point cloud
+The LiDAR data is saved with Open3d package and has a postfix ".pcd" in the name. 
+
+#### 3.2 Camera images
+Each CAV is equipped with 4 RGB cameras (check https://mobility-lab.seas.ucla.edu/opv2v/ to see the mounted positions of these cameras) to capture the 360 degree of view of the surrounding scene.`camera0`, `camera1`, `camera2`, and `camera3` represent the front, right rear, left rear, and back cameras respectively.
+
+#### 3.3  Data Annotation
+All the metadata is saved in yaml files. It records the following important information at the current timestamp:
+- **ego information**:  Current ego pose with and without GPS noise under Carla world coordinates, ego speed in km/h, the LiDAR pose, and future planning trajectories. 
+- **calibration**: The intrinsic matrix and extrinsic matrix from each camera to the LiDAR sensor.
+- **objects annotation**: The pose and velocity of each surrounding human driving vehicle that has at least one point hit by the agent's LiDAR sensor. See [data annotation section](data_annotation_tutorial.md) for more details. 
+
+### 4. Data Collection Protocol
+Besides agent contents, every scenario database also has a yaml file named `data_protocol.yaml`. 
+This yaml file records the simulation configuration to collect the current scenario. It is used to log replay
+the data and enable users to add/modify sensors for new tasks without changing the original events.
+
+To help users collect customized data in CARLA (e.g., different sensor configurations and modalities) with similar data format and structure, we have released our OPV2V data collection code in the `feature/data_collection` branch of [OpenCDA](https://github.com/ucla-mobility/OpenCDA/tree/feature/data_collection). Users can refer to its [documentation](https://opencda-documentation.readthedocs.io/en/latest/md_files/introduction.html) for detailed instructions.
+
+---
+## V2XSet
+The data can be found from [google url](https://drive.google.com/drive/folders/1r5sPiBEvo8Xby-nMaWUTnJIPK6WhY1B6?usp=sharing).  Since the data for train/validate/test
+is very large, we  split each data set into small chunks, which can be found in the directory ending with `_chunks`, such as `train_chunks`. After downloading, please run the following command to each set to merge those chunks together:
+```
+cat train.zip.part* > train.zip
+unzip train.zip
+```
+
+### 1. Structure
+After downloading is finished, please make the file structured as following:
+
+```sh
+OpenCOOD # root of opencood
+├── v2xset # the downloaded v2xset data
+│   ├── train
+│   ├── validate
+│   ├── test
+├── opencood # the core codebase
+```
+
+### 2. Label format
+V2XSet's data label format is nearly the same with OPV2V, except:
+- OPV2V only has vehicles while V2XSet has infrastructure sensors
+- All the infrastructure sensors' folder is named with negative integer, e.g. `-1`
+
+---
+## Adding Noise to OPV2V and V2XSet
+
+Cooperative perception faces the challenge of GPS error and communication delay. Our OpenCOOD allows users 
+to add realistic GPS error and communication delay to the dataset.
+
+To add noise to both OPV2V and V2XSet, just add the following parameters to the model yaml file:
+```
+wild_setting: # setting related to noise
+  async: true
+  async_mode: 'sim'
+  async_overhead: 100
+  backbone_delay: 10
+  data_size: 1.06
+  loc_err: true
+  ryp_std: 0.2
+  seed: 25
+  transmission_speed: 27
+  xyz_std: 0.2
+```
+`async`: whether add communication delay. <br>
+`aysnc_mode`:  sim or real mode. In sim mode, the delay is a constant while in real mode, the delay has a uniform distribution.
+The major experiment in the paper used sim mode whereas the 'Effects of transmission size' study used real
+ mode. <br>
+`async_overhead`: the communication delay in ms. In sim mode, it represents a constant number. In real mode,
+the systematic async will be a random number from 0 to `aysnc_overhead`. <br>
+`backbone_delay`: an estimate of backbone computation time. Only useful in real mode. <br>
+`data_size`: transmission data size in Mb. Only used in real mode. <br>
+`transmission_speed`: data transmitting speed during communication. By default 27 Mb/s. Only used in real mode. <br>
+`loc_err`: whether to add localization error. <br>
+`xyz_std`: the standard deviation of positional GPS error. <br>
+`ryp_std`: the standard deviation of angular GPS error. <br>
+`seed`: random seed for noise simulation. <strong>please keep it as 25 during testing </strong>.
+
+To see a complete example, refer to [configuration tutorial section](config_tutorial.md)
\ No newline at end of file
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diff --git a/opencood/docs/md_files/installation.md b/opencood/docs/md_files/installation.md
new file mode 100644
index 00000000..a1726299
--- /dev/null
+++ b/opencood/docs/md_files/installation.md
@@ -0,0 +1,73 @@
+# Installation
+
+* [__System/Hardware Requirements__](#requirements)
+* [__Installation__](#installation)
+    * [__1. Dependency Installation__](#1-dependency-installation)
+    * [__2. Install Pytorch__](#2-pytorch-installation-18)
+    * [__3. Install Spconv__](#3-spconv-121-requred)
+
+
+
+
+---
+## System/Hardware Requirements
+To get started, the following requirements should be fulfilled.
+* __System requirements.__ OpenCOOD is tested under Ubuntu 18.04
+* __Adequate GPU.__ A minimum of 6GB gpu is recommended.
+* __Disk Space.__ Estimate 100GB of space is recommended for data downoading.
+* __Python__ Python3.7 is required.
+
+
+---
+## Installation
+### 1. Dependency Installation
+First, download OpenCOOD github to your local folder if you haven't done it yet.
+```sh
+git clone https://github.com/DerrickXuNu/OpenCOOD.git
+cd OpenCOOD
+```
+Next we create a conda environment and install the requirements.
+
+```sh
+conda env create -f environment.yml
+conda activate opencood
+python setup.py develop
+```
+
+If conda install failed,  install through pip
+```sh
+pip install -r requirements.txt
+```
+
+### 2. Pytorch Installation (>=1.8, tested on 1.8-1.12.0)
+Go to https://pytorch.org/ to install pytorch cuda version.
+
+### 3. Spconv (1.2.1 or 2.x)
+OpenCOOD support both spconv 1.2.1 and 2.x to generate voxel features. 
+
+To install spconv 1.2.1, please follow the guide in https://github.com/traveller59/spconv/tree/v1.2.1.
+
+To install spconv 2.x, please run the following commands (if you are using cuda 11.3):
+```python
+pip install spconv-cu113
+```
+#### Tips for installing spconv 1.2.1:
+1. make sure your cmake version >= 3.13.2
+2. CUDNN and CUDA runtime library (use `nvcc --version` to check) needs to be installed on your machine.
+
+
+
+### 4. Bbx IOU cuda version compile
+Install bbx nms calculation cuda version
+  
+  ```bash
+  python opencood/utils/setup.py build_ext --inplace
+  ```
+
+
+### 5. Dependencies for FPV-RCNN (optional)
+Install the dependencies for fpv-rcnn.
+  
+  ```bash
+  python opencood/pcdet_utils/setup.py build_ext --inplace
+  ```
diff --git a/opencood/docs/md_files/lidar_benchmark.md b/opencood/docs/md_files/lidar_benchmark.md
new file mode 100644
index 00000000..aeb75383
--- /dev/null
+++ b/opencood/docs/md_files/lidar_benchmark.md
@@ -0,0 +1,29 @@
+## Benchmark: 3D LiDAR  Detection
+
+---
+### Results on OPV2V dataset (AP@0.7 for no-compression/ compression)
+
+|                    | Backbone   | Fusion Strategy  | Bandwidth (Megabit), <br/> before/after compression| Default Towns    |Culver City| Download |
+|--------------------| --------   | ---------------  | ---------------                | -------------    |-----------| -------- |
+| Naive Late         | PointPillar        | Late      |    **0.024**/**0.024** |   0.781/0.781        | 0.668/0.668         |    [url](https://drive.google.com/file/d/1WTKooW6k0exLqoIE5Czqy6ptycYlgKZz/view?usp=sharing)   |
+| [Cooper](https://arxiv.org/abs/1905.05265)       | PointPillar        | Early  |   7.68/7.68   | 0.800/x         | 0.696/x       | [url](https://drive.google.com/file/d/1N1p6syxGSKD18ELgtBQoSuUzR8tX1JeE/view?usp=sharing)     | 
+| [Attentive Fusion](https://arxiv.org/abs/2109.07644)         | PointPillar        | Intermediate  | 126.8/1.98   | **0.815**/**0.810**       | **0.735**/**0.731**        | [url](https://drive.google.com/file/d/1u4w13SDzdGq6Irh2PHxT-qIlNXRT3z6Z/view?usp=sharing)     | 
+| [F-Cooper](https://arxiv.org/abs/1909.06459)         | PointPillar        | Intermediate  | 72.08/1.12    | 0.790/0.788     | 0.728/0.726        | [url](https://drive.google.com/file/d/1CjXu9Y2ZTzJA6Oo3hnqFhbTqBVKq3mQb/view?usp=sharing)     | 
+| Naive Late         | VoxelNet        | Late  | **0.024**/**0.024**    | 0.738/0.738          | 0.588/0.588        | [url]()    |
+| Cooper    | VoxelNet        | Early   |   7.68/7.68  | 0.758/x        | 0.677/x        | [url](https://drive.google.com/file/d/14WD7iLLyyCJJ3lApbYYdr5KOUM1ACnve/view?usp=sharing)     | 
+| Attentive Fusion        | VoxelNet        | Intermediate |   576.71/1.12   | **0.864**/**0.852**        | **0.775**/**0.746**       | [url](https://drive.google.com/file/d/16q8CfcB8dS4EVhJMvvEfn0gM2ynxZB3E/view?usp=sharing)      | 
+| Naive Late         | SECOND        | Late |  **0.024**/**0.024**    |  0.775/0.775        |0.682/0.682        | [url](https://drive.google.com/file/d/1VG_FKe1mKagPVGXH7UGHpyaM5q3cxtD8/view?usp=sharing)      |
+| Cooper    | SECOND        | Early  |   7.68/7.68   |  0.813/x       |  0.738/x     | [url](https://drive.google.com/file/d/1Z9io1VNcU-urcRW8l0ogWCTVCB53mw4N/view?usp=sharing)     | 
+| Attentive         | SECOND        | Intermediate |  63.4/0.99     |   **0.826**/**0.783**     | **0.760**/**0.760**    | [url](https://drive.google.com/file/d/1zEB8EyZ0X-WQykHFOM0pVwI8jXunRz1Z/view?usp=sharing)      | 
+| Naive Late         | PIXOR        | Late |    **0.024**/**0.024** |    0.578/0.578       |  0.360/0.360      | [url]()      |
+| Cooper    | PIXOR        | Early |   7.68/7.68    |   0.678/x      | **0.558**/x      | [url](https://drive.google.com/file/d/1ZDLjtizZCuV6D92LloEPKRIw-LqxfE1j/view?usp=sharing)     | 
+| Attentive         | PIXOR        | Intermediate  |   313.75/1.22  |  **0.687**/**0.612**      | 0.546/**0.492**       | [url]()      |
+
+
+**Note**: 
+* We suggest using **PointPillar** as the backbone when you are creating your method and try to compare with
+our benchmark, as we implement most of the SOTA methods with this backbone only.
+* We assume the transimssion rate is 27Mbp/s. Considering the frequency of LiDAR is 10Hz, the 
+bandwidth requirement should be less than **2.7Mbp** to avoid severe delay. 
+* A 'x' in the benchmark table represents the bandwidth requirement is too large, which 
+can not be considered to employ in practice.
\ No newline at end of file
diff --git a/opencood/docs/md_files/logic_flow.md b/opencood/docs/md_files/logic_flow.md
new file mode 100644
index 00000000..a4e6eefb
--- /dev/null
+++ b/opencood/docs/md_files/logic_flow.md
@@ -0,0 +1,85 @@
+## Tutorial 3: OpenCOOD Pipeline
+
+---
+![teaser](data_pipeline.png)
+
+In this tutorial, we will go through the pipeline of OpenCOOD from loading data to generate structure predictions.
+As the figure shown above, there are five important modules: **configuration system**, **data loading**, **preprocessor**, **postprocessor**, and **model**.
+
+### Configuration System
+
+---
+The configuration system will read the yaml file defined by users to load all parameters for data I/O, training schema, and model. The configuration will be saved in a dictionary. Please refer to [tutorial 1](config_tutorial.md) for more details.
+
+### Data Loading
+
+---
+As different fusion strategies can lead to different data processing (e.g., early fusion aggregates all raw
+LiDAR point clouds whereas intermediate fusion aggregates intermediate deep features), we have three
+distinct PyTorch dataset class: `EarlyFusionDataset`, `LateFusionDataset`, and `IntermediateFusionDataset`.
+These three classes share the same interface and inherit from `BaseDataset`.
+
+#### BaseDataset
+There are two major tasks for `BaseDataset`. 
+- First, given the path to the dataset, `BaseDataset` will construct a scenario database (dictionary format). The scenarior database is indexed by scenarior id, agent id and timestamp and returns file pathes for various sensors and saved yaml data. Here is an example of how the database structured:
+```yaml
+
+{scenario_id : 
+    {agent_1 : 
+       {timestamp1 : 
+          {yaml: path (str),
+           lidar: path (str), 
+           cameras: alist of path to 4 dfferent cameras}}}}
+
+```
+- Second, during the runtime, given an index from PyTorch dataloader, `BaseDataset` will retrieve the corresponding path information from scenario database, load the data from disk and return the raw format data:
+```yaml
+{cav_id :
+   {ego : True or False, # whether this CAV is regarded as ego
+    params: a dictionary, # contains all metadata of this CAV and annotations for surrounding objects
+    lidar_np: a (n, 4) numpy array for the raw point cloud,
+    camera_rgbs: a list of rgb images from 4 cameras on the CAV.
+   }
+}
+```
+
+#### EarlyFusion, LateFusion, IntermediateFusion dataset
+All of these 3 classes will inherit the `BaseDataset` to first construct a scenario database. During runtime, they will first call the `__get_item__` function from `BaseDataset` to get the raw format data. Next, they will do:
+
+1. Project all objects' bounding box coordinates from Carla Map global reference to the ego vehicle's reference. Note that for late fusion, the CAV project to its own coordinate frame instead of the ego vehicle.
+2. For early fusion and intermediate fusion, all agents will project their LiDAR point clouds to ego vehicle's coordinate frame.
+3. Call the corresponding `PreProcessor` to convert the raw point clouds to voxel/bev features.
+4. Call the corresponding `PostProcessor` to convert the objects annotation to the correct format for the loss computing.
+5. Structure all the information in a dictionary. The processed format will be:
+```yaml
+{ agent_id:
+   { 'object_bbx_center': numpy array, # the bounding boxes of all objects with padding 
+     'object_bbx_mask': numpy array, # indicate the valid index for object_bbx_center
+     'object_ids': a list of integer, # objects
+     'anchor_box': numpy array, # pre-defined anchor box for detection
+     'processed_lidar': numpy array, # voxel/bev features generated from preprocessor
+     'label_dict': numpy array, # the groundtruth that can be directly used for computing loss during training 
+   }
+}
+```
+Notice here we just mention the general process and data format, these 3 classes have some differences in details.
+For example, early fusion and intermediate fusion only have a single `agent_id` element -- `ego`, while late fusion
+has multiple `agent_id`. Please read the code for more details.
+
+#### Collate Function
+As our data format is customized, all 3 dataset classes have their own `collate_fn` for the PyTorch dataloader.  The output of the collate function will be a similar dictionary as the one mentioned above except that all values are now in torch.Tensor format.
+
+### Preprocessor and Postprocessor
+
+---
+The preprocessor is mainly used to convert the raw point clouds to voxels or 2D bev features (refer to PIXOR [paper](https://arxiv.org/abs/1902.06326)).
+
+The postprocessor has three purposes:
+- Convert the bounding boxes of the objects to the correct format so that the models can use to compute the loss.
+- Generate the predefined anchor box if needed.
+- Convert the output of the models to the correct format for evaluation and visualization.
+
+### Models
+
+---
+All the models' architecture designs are saved in `opencood/models`. For early fusion and late fusion, each model file only have the backbone codes. For intermediate fusion, the model files have both backbone and fusion net codes. 
\ No newline at end of file
diff --git a/opencood/docs/requirements.txt b/opencood/docs/requirements.txt
new file mode 100644
index 00000000..1aa9ab2b
--- /dev/null
+++ b/opencood/docs/requirements.txt
@@ -0,0 +1,7 @@
+matplotlib==3.4.2
+numpy
+open3d
+opencv-python
+cython
+tensorboardX
+shapely
diff --git a/opencood/dummy_v2xset/validate/2021_08_21_17_30_41/4279/000069.pcd b/opencood/dummy_v2xset/validate/2021_08_21_17_30_41/4279/000069.pcd
new file mode 100644
index 00000000..f20b846d
--- /dev/null
+++ b/opencood/dummy_v2xset/validate/2021_08_21_17_30_41/4279/000069.pcd
@@ -0,0 +1,39730 @@
+# .PCD v0.7 - Point Cloud Data file format
+VERSION 0.7
+FIELDS x y z rgb
+SIZE 4 4 4 4
+TYPE F F F F
+COUNT 1 1 1 1
+WIDTH 39719
+HEIGHT 1
+VIEWPOINT 0 0 0 1 0 0 0
+POINTS 39719
+DATA ascii
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diff --git a/opencood/dummy_v2xset/validate/2021_08_21_17_30_41/4288/000069.pcd b/opencood/dummy_v2xset/validate/2021_08_21_17_30_41/4288/000069.pcd
new file mode 100644
index 00000000..3e6cb861
--- /dev/null
+++ b/opencood/dummy_v2xset/validate/2021_08_21_17_30_41/4288/000069.pcd
@@ -0,0 +1,39844 @@
+# .PCD v0.7 - Point Cloud Data file format
+VERSION 0.7
+FIELDS x y z rgb
+SIZE 4 4 4 4
+TYPE F F F F
+COUNT 1 1 1 1
+WIDTH 39833
+HEIGHT 1
+VIEWPOINT 0 0 0 1 0 0 0
+POINTS 39833
+DATA ascii
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+-69.96691132 -0.2812512517 2.443312168 1.772425076e-38
+-90.58176422 -0.7283445001 3.163277626 1.625488282e-38
+-70.62337494 -0.8517290354 2.466396809 1.763241526e-38
+-70.44780731 -1.132823944 2.460404396 1.763241526e-38
+-68.8398819 -1.383833289 2.404420853 1.781608625e-38
+-68.4131546 -1.650384903 2.389729261 1.781608625e-38
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diff --git a/opencood/dummy_v2xset/validate/2021_08_21_17_30_41/4288/000069.yaml b/opencood/dummy_v2xset/validate/2021_08_21_17_30_41/4288/000069.yaml
new file mode 100644
index 00000000..181380cb
--- /dev/null
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diff --git a/opencood/dummy_v2xset/validate/2021_08_21_17_30_41/4297/000069.pcd b/opencood/dummy_v2xset/validate/2021_08_21_17_30_41/4297/000069.pcd
new file mode 100644
index 00000000..3e2690f3
--- /dev/null
+++ b/opencood/dummy_v2xset/validate/2021_08_21_17_30_41/4297/000069.pcd
@@ -0,0 +1,39633 @@
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diff --git a/opencood/dummy_v2xset/validate/2021_08_21_17_30_41/4297/000069.yaml b/opencood/dummy_v2xset/validate/2021_08_21_17_30_41/4297/000069.yaml
new file mode 100644
index 00000000..ddb05fa7
--- /dev/null
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diff --git a/opencood/dummy_v2xset/validate/2021_08_21_17_30_41/data_protocol.yaml b/opencood/dummy_v2xset/validate/2021_08_21_17_30_41/data_protocol.yaml
new file mode 100644
index 00000000..50ef8376
--- /dev/null
+++ b/opencood/dummy_v2xset/validate/2021_08_21_17_30_41/data_protocol.yaml
@@ -0,0 +1,236 @@
+carla_traffic_manager:
+  auto_lane_change: true
+  global_distance: 1
+  global_speed_perc: -10
+  ignore_lights_percentage: 0
+  random: true
+  range:
+  - - -276
+    - -240
+    - -22
+    - 30
+    - 3.5
+    - 3.5
+    - 30
+  set_osm_mode: true
+  sync_mode: true
+  vehicle_list: null
+current_time: '2021_10_24_18_21_32'
+description: "Copyright 2021 <UCLA Mobility Lab>\nAuthor: Runsheng Xu <rxx3386@ucla.edu>\n\
+  Content: This is the scenario testing configuration file for single vehicle perception/localization/behavior/control\n\
+  \         testing in high speed in the customized 2lanefree simple version."
+scenario:
+  rsu_list: []
+  single_cav_list:
+  - behavior:
+      collision_time_ahead: 1.5
+      emergency_param: 0.4
+      ignore_traffic_light: false
+      local_planner:
+        buffer_size: 12
+        debug: false
+        debug_trajectory: true
+        min_dist: 3
+        trajectory_dt: 0.2
+        trajectory_update_freq: 15
+        waypoint_update_freq: 9
+      max_speed: 24
+      overtake_allowed: false
+      overtake_counter_recover: 35
+      safety_time: 4
+      sample_resolution: 4.5
+      speed_decrease: 15
+      speed_lim_dist: 3
+      tailgate_speed: 34
+    controller: &id001
+      args:
+        dt: 0.05
+        dynamic: false
+        lat:
+          k_d: 0.02
+          k_i: 0.4
+          k_p: 0.75
+        lon:
+          k_d: 0.024
+          k_i: 0.032
+          k_p: 0.37
+        max_brake: 1.0
+        max_steering: 0.3
+        max_throttle: 1.0
+      type: pid_controller
+    destination:
+    - -275.62
+    - 50.2
+    - 0.3
+    - 0
+    - 90
+    - 0
+    sensing: &id002
+      localization:
+        activate: true
+        debug_helper:
+          show_animation: false
+          x_scale: 1.0
+          y_scale: 100.0
+        dt: 0.05
+        gnss:
+          heading_direction_stddev: 0.1
+          noise_alt_stddev: 0.005
+          noise_lat_stddev: 1.0e-06
+          noise_lon_stddev: 1.0e-06
+          speed_stddev: 0.2
+      perception:
+        activate: false
+        camera_num: 4
+        camera_visualize: 1
+        lidar:
+          channels: 64
+          dropoff_general_rate: 0.1
+          dropoff_intensity_limit: 0.7
+          dropoff_zero_intensity: 0.15
+          lower_fov: -25
+          noise_stddev: 0.02
+          points_per_second: 1300000
+          range: 120
+          rotation_frequency: 20
+          upper_fov: 2
+        lidar_visualize: true
+    spawn_position:
+    - -275.62
+    - -21.2
+    - 0.3
+    - 0
+    - 90
+    - 0
+    v2x:
+      communication_range: 45
+      enabled: true
+      lag: 0
+      loc_noise: 0.0
+      speed_noise: 0.0
+      yaw_noise: 0.0
+  - behavior:
+      collision_time_ahead: 1.5
+      emergency_param: 0.4
+      ignore_traffic_light: false
+      local_planner:
+        buffer_size: 12
+        debug: false
+        debug_trajectory: true
+        min_dist: 3
+        trajectory_dt: 0.2
+        trajectory_update_freq: 15
+        waypoint_update_freq: 9
+      max_speed: 24
+      overtake_allowed: false
+      overtake_counter_recover: 35
+      safety_time: 4
+      sample_resolution: 4.5
+      speed_decrease: 15
+      speed_lim_dist: 3
+      tailgate_speed: 34
+    controller: *id001
+    destination:
+    - -162.53
+    - -84.63
+    - 0.3
+    sensing: *id002
+    spawn_position:
+    - -265.64
+    - 25.59
+    - 0.35
+    - 0
+    - -90
+    - 0
+    v2x:
+      communication_range: 45
+      enabled: true
+      lag: 0
+      loc_noise: 0.0
+      speed_noise: 0.0
+      yaw_noise: 0.0
+  - behavior:
+      collision_time_ahead: 1.5
+      emergency_param: 0.4
+      ignore_traffic_light: false
+      local_planner:
+        buffer_size: 12
+        debug: false
+        debug_trajectory: true
+        min_dist: 3
+        trajectory_dt: 0.2
+        trajectory_update_freq: 15
+        waypoint_update_freq: 9
+      max_speed: 24
+      overtake_allowed: false
+      overtake_counter_recover: 35
+      safety_time: 4
+      sample_resolution: 4.5
+      speed_decrease: 15
+      speed_lim_dist: 3
+      tailgate_speed: 34
+    controller: *id001
+    destination:
+    - -162.53
+    - -84.63
+    - 0.3
+    sensing: *id002
+    spawn_position:
+    - -230.64
+    - -0.4591
+    - 1.0
+    - 0
+    - 180
+    - 0
+    v2x:
+      communication_range: 45
+      enabled: true
+      lag: 0
+      loc_noise: 0.0
+      speed_noise: 0.0
+      yaw_noise: 0.0
+vehicle_base:
+  behavior:
+    collision_time_ahead: 1.5
+    emergency_param: 0.4
+    ignore_traffic_light: false
+    local_planner:
+      buffer_size: 12
+      debug: false
+      debug_trajectory: false
+      min_dist: 3
+      trajectory_dt: 0.2
+      trajectory_update_freq: 15
+      waypoint_update_freq: 9
+    max_speed: 24
+    overtake_allowed: false
+    overtake_counter_recover: 35
+    safety_time: 4
+    sample_resolution: 4.5
+    speed_decrease: 15
+    speed_lim_dist: 3
+    tailgate_speed: 34
+  controller: *id001
+  sensing: *id002
+  v2x:
+    communication_range: 35
+    enabled: true
+    lag: 0
+    loc_noise: 0.0
+    speed_noise: 0.0
+    yaw_noise: 0.0
+world:
+  client_port: 2000
+  fixed_delta_seconds: 0.05
+  seed: 81
+  sync_mode: true
+  weather:
+    cloudiness: 0
+    fog_density: 0
+    fog_distance: 0
+    fog_falloff: 0
+    precipitation: 0
+    precipitation_deposits: 0
+    sun_altitude_angle: 15
+    wetness: 0
+    wind_intensity: 0
diff --git a/opencood/environment.yml b/opencood/environment.yml
new file mode 100644
index 00000000..e2baa48e
--- /dev/null
+++ b/opencood/environment.yml
@@ -0,0 +1,18 @@
+name: opencood
+channels:
+  - defaults
+dependencies:
+  - pip==21.1.2
+  - python==3.7.11
+  - pip:
+    - matplotlib==3.4.2
+    - numpy
+    - open3d
+    - opencv-python==4.5.5.62
+    - cython
+    - pygame
+    - tensorboardX
+    - shapely==2.0.0
+    - einops
+    - cumm
+    - timm
diff --git a/opencood/images/camera_demo.gif b/opencood/images/camera_demo.gif
new file mode 100644
index 0000000000000000000000000000000000000000..2107af463c9f8a52e1dafffcc48cb0015468b6ac
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diff --git a/opencood/logreplay/README.md b/opencood/logreplay/README.md
new file mode 100644
index 00000000..4cd012dd
--- /dev/null
+++ b/opencood/logreplay/README.md
@@ -0,0 +1,44 @@
+# CARLA Log Replay Tool for Cooperative Perception Dataset
+<p align="center">
+<img src="../images/logreplay.gif" width="600" alt="" class="img-responsive">
+</p>
+This repository provides a toolbox to replay simulated cooperative perception dataset in CARLA. With 
+this toolbox, you can 100% replay all the events in the offline dataset and add/change any sensors/groundtruth you
+want to explore the tasks that the origin dataset do not support.
+
+## Features
+- Support replaying multiple cooperative perception dataset in CARLA
+  - [x] [OPV2V [ICRA2022]](https://mobility-lab.seas.ucla.edu/opv2v/)
+  - [ ] [V2XSet [ECCV2022]]()
+- Help you add new sensors to the agents to have additional sensor data, including but not limited to:
+  - Depth sensor
+  - Event camera
+  - Semantic LiDAR
+  - Radar
+  
+- Dump new groundtruth that the origin dataset does not have, including but not limited to:
+  - HDMap ([CoBEVT](https://arxiv.org/abs/2207.02202) (CoRL2022) used this groundtruth )
+  - Trajectory for each vehicle
+- Change the number of CAVs! The original OPV2V data has fixed CAVs, but with this tool, you can
+assign any vehicle to a CAV equipped with sensors.
+
+## How to use
+### Step0: Install CARLA 0.9.12 and CARLA API to your python environment
+Check the CARLA official website: https://carla.readthedocs.io/en/latest/start_quickstart/
+### Step1: Yaml file configuration
+Please change the parameters in `logreplay/hypes_yaml/replay.yaml` to configure your replay.
+We have provided very detailed explanation for each line in the yaml. 
+### Step2: add new sensors if necessary
+We have by default provide Semantic Lidar and BEV semantic camera sensors in the yaml file and codebase.
+If you want to add some new sensors, please add your sensor under `logreplay/sensors/` and refer to the `semantic_lidar`
+or `bev_semantic_camera.py` to write your own sensor class.
+### Step3: Run CARLA
+Start your CARLA simulator first. Right now we only support version 0.9.12 
+### Step4: run the log replay tool
+Execute the following command:
+```python
+cd logreplay
+python scenario/scenearios_manager.py
+```
+
+
diff --git a/opencood/logreplay/__init__.py b/opencood/logreplay/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/opencood/logreplay/assets/__init__.py b/opencood/logreplay/assets/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/opencood/logreplay/assets/presave_lib.py b/opencood/logreplay/assets/presave_lib.py
new file mode 100644
index 00000000..9a2e198a
--- /dev/null
+++ b/opencood/logreplay/assets/presave_lib.py
@@ -0,0 +1,596 @@
+BLUE_PRINT_LIB = {
+    "vehicle.audi.a2": {
+        "location": [
+            1.1874361216523965e-10,
+            0.00033203110797330737,
+            0.7818066477775574
+        ],
+        "extent": [
+            1.852684736251831,
+            0.8943392634391785,
+            0.7745251059532166
+        ],
+        "num_wheel": 4
+    },
+    "vehicle.audi.tt": {
+        "location": [
+            -0.00037109359982423484,
+            1.3271345133958334e-10,
+            0.6926488876342773
+        ],
+        "extent": [
+            2.0906050205230713,
+            0.9970585703849792,
+            0.6926480531692505
+        ],
+        "num_wheel": 4
+    },
+    "vehicle.micro.microlino": {
+        "location": [
+            -2.0954755292068583e-11,
+            -5.8593730500433594e-05,
+            0.6771557331085205
+        ],
+        "extent": [
+            1.1036475896835327,
+            0.7404598593711853,
+            0.6880123615264893
+        ],
+        "num_wheel": 4
+    },
+    "vehicle.bmw.grandtourer": {
+        "location": [
+            -1.885927941591703e-10,
+            -0.0005273435963317752,
+            0.7651049494743347
+        ],
+        "extent": [
+            2.3055028915405273,
+            1.1208566427230835,
+            0.8336379528045654
+        ],
+        "num_wheel": 4
+    },
+    "vehicle.mercedes.sprinter": {
+        "location": [
+            -0.010537102818489075,
+            0.004140627570450306,
+            1.2882421016693115
+        ],
+        "extent": [
+            2.957595109939575,
+            0.9942164421081543,
+            1.2803276777267456
+        ],
+        "num_wheel": 4
+    },
+    "vehicle.dodge.charger_police": {
+        "location": [
+            0.023632802069187164,
+            0.0006054601399227977,
+            0.7926709055900574
+        ],
+        "extent": [
+            2.487122058868408,
+            1.0192005634307861,
+            0.7710590958595276
+        ],
+        "num_wheel": 4
+    },
+    "vehicle.jeep.wrangler_rubicon": {
+        "location": [
+            0.00021484403987415135,
+            0.00103515584487468,
+            0.9322753548622131
+        ],
+        "extent": [
+            1.9331103563308716,
+            0.9525982737541199,
+            0.9389679431915283
+        ],
+        "num_wheel": 4
+    },
+    "vehicle.mini.cooper_s": {
+        "location": [
+            -2.0954755292068583e-11,
+            -5.8593730500433594e-05,
+            0.6930102109909058
+        ],
+        "extent": [
+            1.9029000997543335,
+            0.985137939453125,
+            0.7375151515007019
+        ],
+        "num_wheel": 4
+    },
+    "vehicle.nissan.micra": {
+        "location": [
+            3.492459099696532e-10,
+            0.0009765622089616954,
+            0.7812451124191284
+        ],
+        "extent": [
+            1.8166879415512085,
+            0.9225568771362305,
+            0.7506412863731384
+        ],
+        "num_wheel": 4
+    },
+    "vehicle.carlamotors.carlacola": {
+        "location": [
+            0.0,
+            0.0,
+            1.2337206602096558
+        ],
+        "extent": [
+            2.601919174194336,
+            1.3134948015213013,
+            1.2337223291397095
+        ],
+        "num_wheel": 4
+    },
+    "vehicle.mercedes.coupe_2020": {
+        "location": [
+            -0.00259765493683517,
+            9.289941038659322e-10,
+            0.7203612923622131
+        ],
+        "extent": [
+            2.3368194103240967,
+            1.0011461973190308,
+            0.7209736704826355
+        ],
+        "num_wheel": 4
+    },
+    "vehicle.dodge.charger_police_2020": {
+        "location": [
+            0.109501913189888,
+            -3.916094470923781e-08,
+            0.8174071907997131
+        ],
+        "extent": [
+            2.6187572479248047,
+            1.0485419034957886,
+            0.819191575050354
+        ],
+        "num_wheel": 4
+    },
+    "vehicle.mini.cooper_s_2021": {
+        "location": [
+            -0.031249990686774254,
+            1.1175869119028903e-08,
+            0.8910351395606995
+        ],
+        "extent": [
+            2.2763495445251465,
+            1.0485360622406006,
+            0.8835831880569458
+        ],
+        "num_wheel": 4
+    },
+    "vehicle.bh.crossbike": {
+        "location": [
+            0.00029444904066622257,
+            195.28602600097656,
+            0.5411913990974426
+        ],
+        "extent": [
+            0.7436444163322449,
+            0.0,
+            0.5397894978523254
+        ],
+        "num_wheel": 2
+    },
+    "vehicle.nissan.patrol": {
+        "location": [
+            -0.05737302824854851,
+            0.00015627048560418189,
+            0.9351245164871216
+        ],
+        "extent": [
+            2.3022549152374268,
+            0.9657964706420898,
+            0.9274230599403381
+        ],
+        "num_wheel": 4
+    },
+    "vehicle.kawasaki.ninja": {
+        "location": [
+            -0.01960790529847145,
+            195.23898315429688,
+            0.5854394435882568
+        ],
+        "extent": [
+            1.0166761875152588,
+            0.0,
+            0.5727267861366272
+        ],
+        "num_wheel": 2
+    },
+    "vehicle.lincoln.mkz_2020": {
+        "location": [
+            -0.006289059761911631,
+            2.249143760124639e-09,
+            0.7437793016433716
+        ],
+        "extent": [
+            2.44619083404541,
+            1.115301489830017,
+            0.7400735020637512
+        ],
+        "num_wheel": 4
+    },
+    "vehicle.lincoln.mkz_2017": {
+        "location": [
+            0.004042967688292265,
+            -1.4458780839277097e-09,
+            0.718859851360321
+        ],
+        "extent": [
+            2.4508416652679443,
+            1.0641621351242065,
+            0.7553732395172119
+        ],
+        "num_wheel": 4
+    },
+    "vehicle.ford.mustang": {
+        "location": [
+            0.03213866055011749,
+            -1.149368333841494e-08,
+            0.6504687070846558
+        ],
+        "extent": [
+            2.358762502670288,
+            0.947413444519043,
+            0.650469958782196
+        ],
+        "num_wheel": 4
+    },
+    "vehicle.tesla.cybertruck": {
+        "location": [
+            0.0,
+            0.0,
+            1.049096703529358
+        ],
+        "extent": [
+            3.1367764472961426,
+            1.1947870254516602,
+            1.049095630645752
+        ],
+        "num_wheel": 4
+    },
+    "vehicle.ford.ambulance": {
+        "location": [
+            -0.2885546088218689,
+            0.001933696330524981,
+            1.2156102657318115
+        ],
+        "extent": [
+            3.18282151222229,
+            1.1755871772766113,
+            1.215687870979309
+        ],
+        "num_wheel": 4
+    },
+    "vehicle.mercedes.coupe": {
+        "location": [
+            -2.7939672797572257e-10,
+            -0.0007812496623955667,
+            0.6667944192886353
+        ],
+        "extent": [
+            2.5133883953094482,
+            1.0757731199264526,
+            0.8253258466720581
+        ],
+        "num_wheel": 4
+    },
+    "vehicle.seat.leon": {
+        "location": [
+            -0.00014648489013779908,
+            -0.0015624993247911334,
+            0.7396337985992432
+        ],
+        "extent": [
+            2.0964150428771973,
+            0.9080929160118103,
+            0.7369155883789062
+        ],
+        "num_wheel": 4
+    },
+    "vehicle.nissan.patrol_2021": {
+        "location": [
+            0.028378896415233612,
+            -1.0149086016042475e-08,
+            1.0167187452316284
+        ],
+        "extent": [
+            2.782914400100708,
+            1.0749834775924683,
+            1.0225735902786255
+        ],
+        "num_wheel": 4
+    },
+    "vehicle.vespa.zx125": {
+        "location": [
+            0.010460472665727139,
+            195.32354736328125,
+            0.6211523413658142
+        ],
+        "extent": [
+            0.9023334980010986,
+            0.0,
+            0.6178141832351685
+        ],
+        "num_wheel": 2
+    },
+    "vehicle.yamaha.yzf": {
+        "location": [
+            -0.004266120493412018,
+            195.2182159423828,
+            0.64984130859375
+        ],
+        "extent": [
+            1.1047229766845703,
+            0.0,
+            0.6255727410316467
+        ],
+        "num_wheel": 2
+    },
+    "vehicle.chevrolet.impala": {
+        "location": [
+            5.859437442268245e-05,
+            0.0017968743341043591,
+            0.70149165391922
+        ],
+        "extent": [
+            2.6787397861480713,
+            1.0166014432907104,
+            0.7053293585777283
+        ],
+        "num_wheel": 4
+    },
+    "vehicle.volkswagen.t2": {
+        "location": [
+            0.0013281241990625858,
+            -0.0005468753515742719,
+            1.0144726037979126
+        ],
+        "extent": [
+            2.2402184009552,
+            1.034657597541809,
+            1.0188959836959839
+        ],
+        "num_wheel": 4
+    },
+    "vehicle.citroen.c3": {
+        "location": [
+            8.451751520865969e-10,
+            0.002363280626013875,
+            0.76191645860672
+        ],
+        "extent": [
+            1.9938424825668335,
+            0.9254241585731506,
+            0.8085547685623169
+        ],
+        "num_wheel": 4
+    },
+    "vehicle.diamondback.century": {
+        "location": [
+            1.1252891454205383e-05,
+            195.3052520751953,
+            0.525390625
+        ],
+        "extent": [
+            0.8214218020439148,
+            0.0,
+            0.5119513869285583
+        ],
+        "num_wheel": 2
+    },
+    "vehicle.tesla.model3": {
+        "location": [
+            0.02921873889863491,
+            -1.0449437759518787e-08,
+            0.7355297803878784
+        ],
+        "extent": [
+            2.3958897590637207,
+            1.081725001335144,
+            0.744159996509552
+        ],
+        "num_wheel": 4
+    },
+    "vehicle.carlamotors.firetruck": {
+        "location": [
+            -0.25343742966651917,
+            0.005312588065862656,
+            1.9133495092391968
+        ],
+        "extent": [
+            4.234020709991455,
+            1.4455441236495972,
+            1.9137061834335327
+        ],
+        "num_wheel": 4
+    },
+    "vehicle.audi.etron": {
+        "location": [
+            0.0,
+            0.0,
+            0.8205029368400574
+        ],
+        "extent": [
+            2.427854299545288,
+            1.0163782835006714,
+            0.8246796727180481
+        ],
+        "num_wheel": 4
+    },
+    "vehicle.dodge.charger_2020": {
+        "location": [
+            -0.006230466067790985,
+            2.228188966668654e-09,
+            0.76557856798172
+        ],
+        "extent": [
+            2.5030298233032227,
+            1.0485419034957886,
+            0.7673624753952026
+        ],
+        "num_wheel": 4
+    },
+    "vehicle.gazelle.omafiets": {
+        "location": [
+            -0.01564301736652851,
+            195.3675537109375,
+            0.5801635384559631
+        ],
+        "extent": [
+            0.9177202582359314,
+            0.0,
+            0.5628286004066467
+        ],
+        "num_wheel": 2
+    },
+    "vehicle.harley-davidson.low_rider": {
+        "location": [
+            -0.008875441737473011,
+            195.3657989501953,
+            0.6599145531654358
+        ],
+        "extent": [
+            1.1778701543807983,
+            0.0,
+            0.6382853388786316
+        ],
+        "num_wheel": 2
+    },
+    "vehicle.toyota.prius": {
+        "location": [
+            0.001992186764255166,
+            -0.00039062555879354477,
+            0.724169909954071
+        ],
+        "extent": [
+            2.256761312484741,
+            1.0034072399139404,
+            0.7624167203903198
+        ],
+        "num_wheel": 4
+    }
+}
+
+TOWN_DICTIONARY = {
+    '2021_08_20_21_48_35': 'Town06',
+    '2021_08_18_19_48_05': 'Town06',
+    '2021_08_20_21_10_24': 'Town06',
+    '2021_08_21_09_28_12': 'Town06',
+    '2021_08_22_07_52_02': 'Town05',
+    '2021_08_22_09_08_29': 'Town05',
+    '2021_08_22_21_41_24': 'Town05',
+    '2021_08_23_12_58_19': 'Town05',
+    '2021_08_23_15_19_19': 'Town04',
+    '2021_08_23_16_06_26': 'Town04',
+    '2021_08_23_17_22_47': 'Town04',
+    '2021_08_23_21_07_10': 'Town10HD',
+    '2021_08_23_21_47_19': 'Town10HD',
+    '2021_08_24_07_45_41': 'Town10HD',
+    '2021_08_24_11_37_54': 'Town07',
+    '2021_08_24_20_09_18': 'Town04',
+    '2021_08_24_20_49_54': 'Town04',
+    '2021_08_24_21_29_28': 'Town04',
+    '2021_08_16_22_26_54': 'Town06',
+    '2021_08_18_09_02_56': 'Town06',
+    '2021_08_18_18_33_56': 'Town06',
+    '2021_08_18_21_38_28': 'Town06',
+    '2021_08_18_22_16_12': 'Town06',
+    '2021_08_18_23_23_19': 'Town06',
+    '2021_08_19_15_07_39': 'Town06',
+    '2021_08_20_16_20_46': 'Town06',
+    '2021_08_20_20_39_00': 'Town06',
+    '2021_08_20_21_00_19': 'Town06',
+    '2021_08_21_09_09_41': 'Town06',
+    '2021_08_21_15_41_04': 'Town05',
+    '2021_08_21_16_08_42': 'Town05',
+    '2021_08_21_17_00_32': 'Town05',
+    '2021_08_21_21_35_56': 'Town05',
+    '2021_08_21_22_21_37': 'Town05',
+    '2021_08_22_06_43_37': 'Town05',
+    '2021_08_22_07_24_12': 'Town05',
+    '2021_08_22_08_39_02': 'Town05',
+    '2021_08_22_09_43_53': 'Town05',
+    '2021_08_22_10_10_40': 'Town05',
+    '2021_08_22_10_46_58': 'Town06',
+    '2021_08_22_11_29_38': 'Town06',
+    '2021_08_22_22_30_58': 'Town05',
+    '2021_08_23_10_47_16': 'Town04',
+    '2021_08_23_11_06_41': 'Town05',
+    '2021_08_23_11_22_46': 'Town04',
+    '2021_08_23_12_13_48': 'Town05',
+    '2021_08_23_13_10_47': 'Town05',
+    '2021_08_23_16_42_39': 'Town04',
+    '2021_08_23_17_07_55': 'Town04',
+    '2021_08_23_19_27_57': 'Town10HD',
+    '2021_08_23_20_47_11': 'Town10HD',
+    '2021_08_23_22_31_01': 'Town10HD',
+    '2021_08_23_23_08_17': 'Town10HD',
+    '2021_08_24_09_25_42': 'Town07',
+    '2021_08_24_09_58_32': 'Town07',
+    '2021_08_24_12_19_30': 'Town07',
+    '2021_09_09_13_20_58': 'Town03',
+    '2021_09_09_19_27_35': 'Town01',
+    '2021_09_10_12_07_11': 'Town04',
+    '2021_09_09_23_21_21': 'Town03',
+    '2021_08_21_17_30_41': 'Town05',
+    '2021_08_22_13_37_16': 'Town06',
+    '2021_08_22_22_01_17': 'Town05',
+    '2021_08_23_10_51_24': 'Town05',
+    '2021_08_23_13_17_21': 'Town05',
+    '2021_08_23_19_42_07': 'Town10HD',
+    '2021_09_09_22_21_11': 'Town02',
+    '2021_09_11_00_33_16': 'Town10HD',
+    '2021_08_18_19_11_02': 'Town06'
+}
+
+
+class bcolors:
+    HEADER = '\033[95m'
+    OKBLUE = '\033[94m'
+    OKCYAN = '\033[96m'
+    OKGREEN = '\033[92m'
+    WARNING = '\033[93m'
+    FAIL = '\033[91m'
+    ENDC = '\033[0m'
+    BOLD = '\033[1m'
+    UNDERLINE = '\033[4m'
+
+
+EXCLUDE_ROAD_MAP = ['2021_08_18_19_11_02',
+                    '2021_08_21_09_09_41',
+                    '2021_08_23_16_42_39',
+                    '2021_08_23_17_07_55',
+                    '2021_08_24_12_19_30',
+                    '2021_08_24_09_25_42',
+                    '2021_08_24_09_58_32',
+                    '2021_08_24_12_19_30',
+                    '2021_08_21_09_28_12',
+                    '2021_08_24_11_37_54',
+                    '2021_08_23_17_22_47',
+                    '2021_08_24_20_09_18']
+
+OR_Z_VALUE_MAP = ['2021_08_22_09_43_53',
+                  '2021_08_22_10_10_40',
+                  '2021_08_23_12_13_48',
+                  '2021_08_23_13_10_47',
+                  '2021_08_24_09_25_42',
+                  '2021_08_24_09_58_32',
+                  '2021_08_24_12_19_30',
+                  '2021_09_09_23_21_21',
+                  '2021_09_10_12_07_11',
+                  '2021_08_22_09_08_29',
+                  '2021_08_23_12_58_19',
+                  '2021_08_23_17_22_47',
+                  '2021_08_24_11_37_54',
+                  '2021_08_24_20_09_18']
diff --git a/opencood/logreplay/assets/utils.py b/opencood/logreplay/assets/utils.py
new file mode 100644
index 00000000..5c605d2a
--- /dev/null
+++ b/opencood/logreplay/assets/utils.py
@@ -0,0 +1,29 @@
+import numpy as np
+
+from logreplay.assets.presave_lib import TOWN_DICTIONARY, BLUE_PRINT_LIB
+
+
+def find_town(scenario_name):
+    """
+    Given the scenario name, find the corresponding town name,
+
+    Parameters
+    ----------
+    scenario_name : str
+        The scenario name.
+
+    Returns
+    -------
+    The corresponding town's name.
+    """
+    return TOWN_DICTIONARY[scenario_name]
+
+
+def find_blue_print(extent):
+    for blueprint_name, blueprint_content in BLUE_PRINT_LIB.items():
+        bp_extent = blueprint_content['extent']
+        if abs(extent[0] - bp_extent[0]) < 0.001 and \
+                abs(extent[1] - bp_extent[1]) < 0.001:
+            return blueprint_name
+
+    return None
diff --git a/opencood/logreplay/hypes_yaml/__init__.py b/opencood/logreplay/hypes_yaml/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/opencood/logreplay/hypes_yaml/replay.yaml b/opencood/logreplay/hypes_yaml/replay.yaml
new file mode 100644
index 00000000..4f2ddce3
--- /dev/null
+++ b/opencood/logreplay/hypes_yaml/replay.yaml
@@ -0,0 +1,61 @@
+root_dir: '/data/opv2v/train' # your original opv2v dataset
+output_dir: '/data/opv2v/train/additional' # the new folder to save the dumped new data
+map: # corresponding to bev map
+  activate: true # whether activate HDMap module
+  visualize: true # whether to visualize the BEV Map for each CAV
+
+  save_yml: false # whether to save the map meta data in yaml files
+  save_static: false # whether to dump road segmentation mask
+  save_lane: true # whether to dump lane segmentation mask
+  save_dynamic: false # whether to dump dynamic object BEV segmentaition mask
+  save_bev_vis: true # whether to dump the BEV visualization image that has all elements
+  save_bev_sem: true # whether to dump the BEV semantic mask that contains all elements
+
+  radius: 50 # the BEV Map range, e.g., 50 means a square around the ego whose side length is 100
+  raster_size: [256, 256] # the dumped BEV images resolution
+  lane_sample_resolution: 0.1 # the lane keypoint sampling resolution
+
+  static:
+    draw_lane: true # whether to draw lane on the BEV visualization image
+    draw_traffic_light: true # if set to true, the lanes that are influenced by traffic light will change color according to the light state, e.g., yellow, red, green
+    exclude_road: false # set this to false by default
+    exclude_intersection_lane: true # whether to draw the lane topology in the intersection
+    z_filter_value: 6 # the road that is higher or lower relative to ego's position than the threshold won't be drawed
+    other_objs: ['terrain', 'building', 'sidewalk'] # besides, lane and road, what other objects need to render on the BEV visualization image
+  dynamic:
+    exclude_self: true # if set to true, ego won't be drawed
+    exclude_off_road: true # eclude the objects that are off road
+    visibility: false # whether save visibility mask under ego coordinate. when set to true, semantic lidar has to be implemented
+    visibility_corp: false # whether save visibility mask that can be seen for all cavs. when set to true, semantic lidar has to be implemented
+
+sensor: # new attached sensor
+  sensor_list:
+    - name: 'semantic_lidar'
+      args: &base_semantic_lidar
+        upper_fov: 10
+        lower_fov: -30
+        channels: 32
+        rotation_frequency: 20
+        points_per_second: 250000
+        range: 50
+        relative_pose: front
+        thresh: 5 # mininum number of points hit to be regarded as visible
+    - name: 'semantic_lidar'
+      args:
+        <<: *base_semantic_lidar
+        relative_pose: left
+    - name: 'semantic_lidar'
+      args:
+        <<: *base_semantic_lidar
+        relative_pose: right
+    - name: 'semantic_lidar'
+      args:
+        <<: *base_semantic_lidar
+        relative_pose: back
+    - name: 'bev_semantic_camera'
+      args:
+        visualize: true
+        fov: 45
+        image_size_x: 512
+        image_size_y: 512
+        height: 115.88
\ No newline at end of file
diff --git a/opencood/logreplay/map/__init__.py b/opencood/logreplay/map/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/opencood/logreplay/map/map_drawing.py b/opencood/logreplay/map/map_drawing.py
new file mode 100644
index 00000000..93c6d838
--- /dev/null
+++ b/opencood/logreplay/map/map_drawing.py
@@ -0,0 +1,220 @@
+# -*- coding: utf-8 -*-
+
+"""Rasterization drawing functions
+"""
+
+# Author: Runsheng Xu <rxx3386@ucla.edu>
+# License: TDG-Attribution-NonCommercial-NoDistrib
+
+import numpy as np
+import cv2
+
+# sub-pixel drawing precision constants
+CV2_SUB_VALUES = {"shift": 9, "lineType": cv2.LINE_AA}
+CV2_SHIFT_VALUE = 2 ** CV2_SUB_VALUES["shift"]
+INTERPOLATION_POINTS = 20
+
+AGENT_COLOR = (255, 255, 255)
+ROAD_COLOR = (255, 255, 255)
+ROAD_COLOR_VIS =  (17, 17, 31)
+Lane_COLOR = {'normal': (255, 217, 82),
+              'red': (255, 0, 0),
+              'yellow': (255, 255, 0),
+              'green': (0, 255, 0)}
+BUILD_COLOR = (70, 70, 70)
+Terrain_COLOR = (145, 170, 100)
+SideWalk_COLOR = (244, 35, 232)
+# color map
+OBJ_COLOR_MAP = {'building': BUILD_COLOR,
+                 'terrain': Terrain_COLOR,
+                 'sidewalk': SideWalk_COLOR}
+
+
+def cv2_subpixel(coords: np.ndarray) -> np.ndarray:
+    """
+    Cast coordinates to numpy.int but keep fractional part by previously multiplying by 2**CV2_SHIFT
+    cv2 calls will use shift to restore original values with higher precision
+
+    Args:
+        coords (np.ndarray): XY coords as float
+
+    Returns:
+        np.ndarray: XY coords as int for cv2 shift draw
+    """
+    coords = coords * CV2_SHIFT_VALUE
+    coords = coords.astype(np.int)
+    return coords
+
+
+def draw_agent(agent_list, image):
+    """
+    Draw agent mask on image.
+
+    Parameters
+    ----------
+    agent_list : list
+        The agent corner list.
+
+    image : np.ndarray
+        The image to be drawn.
+
+    Returns
+    -------
+    Drawn image.
+    """
+    for agent_corner in agent_list:
+        agent_corner = agent_corner.reshape(-1, 2)
+        cv2.fillPoly(image, [agent_corner], AGENT_COLOR,
+                     **CV2_SUB_VALUES)
+    return image
+
+
+def draw_road(lane_area_list, image, visualize=False):
+    """
+    Draw poly for road.
+
+    Parameters
+    ----------
+    visualize : bool
+        If set to true, the road segment will be black color.
+
+    lane_area_list : list
+        List of lane coordinates
+
+    image : np.ndarray
+        image to be drawn
+
+    Returns
+    -------
+    drawed image.
+    """
+    color = ROAD_COLOR if not visualize else ROAD_COLOR_VIS
+
+    for lane_area in lane_area_list:
+        lane_area = lane_area.reshape(-1, 2)
+        cv2.fillPoly(image, [lane_area], color,
+                     **CV2_SUB_VALUES)
+    return image
+
+def road_exclude(static_road):
+    """
+    Exclude the road segment that is not connected to the ego vehicle
+    position.
+
+    Parameters
+    ----------
+    static_road : np.ndarray
+        The static bev map with road segment.
+
+    Returns
+    -------
+    The road without unrelated road.
+    """
+    binary_bev = cv2.cvtColor(static_road, cv2.COLOR_BGR2GRAY)
+    _, label, stats, _ = cv2.connectedComponentsWithStats(binary_bev)
+
+    ego_label = label[static_road.shape[0] // 2, static_road.shape[1] // 2]
+    static_road[label != ego_label] = 0
+
+    return static_road
+
+
+def draw_lane(lane_area_list, lane_type_list, image,
+              intersection_list=None, vis=True):
+    """
+    Draw lanes on image (polylines).
+
+    Parameters
+    ----------
+    intersection_list : list
+    lane_area_list : list
+        List of lane coordinates
+
+    lane_type_list : list
+        List of lane types, normal, red, green or yellow.
+
+    image : np.ndarray
+        image to be drawn
+
+    vis : bool
+        Whether to visualize
+
+    Returns
+    -------
+    drawed image.
+    """
+    if intersection_list is None:
+        intersection_list = [False] * len(lane_area_list)
+
+    for (lane_area, lane_type, inter_flag) in zip(lane_area_list,
+                                                  lane_type_list,
+                                                  intersection_list):
+        if inter_flag:
+            continue
+        cv2.polylines(image,
+                      lane_area,
+                      False,
+                      Lane_COLOR[lane_type] if vis else (255, 255, 255),
+                      **CV2_SUB_VALUES)
+
+    return image
+
+
+def draw_crosswalks(lane_area_list, image):
+    """
+    Draw lanes on image (polylines).
+
+    Parameters
+    ----------
+    lane_area_list : list
+        List of cross coordinates
+
+    image : np.ndarray
+        image to be drawn
+
+    vis : bool
+        Whether to visualize
+
+    Returns
+    -------
+    drawed image.
+    """
+    for lane_area in lane_area_list:
+        up_line = lane_area[0]
+        bottom_line = lane_area[1]
+
+        cv2.line(image, (up_line[0, 0], up_line[0, 1]),
+                 (up_line[-1, 0], up_line[-1, 1]),
+                 (255, 255, 255), 2, **CV2_SUB_VALUES)
+        cv2.line(image, (bottom_line[0, 0], bottom_line[0, 1]),
+                 (bottom_line[-1, 0], bottom_line[-1, 1]),
+                 (255, 255, 255), 2, **CV2_SUB_VALUES)
+        cv2.line(image, (up_line[0, 0], up_line[0, 1]),
+                 (bottom_line[-1, 0], bottom_line[-1, 1]),
+                 (255, 255, 255), 2, **CV2_SUB_VALUES)
+        cv2.line(image, (up_line[-1, 0], up_line[-1, 1]),
+                 (bottom_line[0, 0], bottom_line[0, 1]),
+                 (255, 255, 255), 2, **CV2_SUB_VALUES)
+    return image
+
+
+def draw_city_objects(city_obj_info, image):
+    """
+    Draw static objects other than lane, road, crosswalks on image.
+
+    Parameters
+    ----------
+    city_obj_info : dict
+    image : np.ndarray
+
+    Returns
+    -------
+    Drew image.
+    """
+    for obj_category, obj_content in city_obj_info.items():
+        for _, obj in obj_content.items():
+            obj_corner = obj['corner_area'].reshape(-1, 2)
+            cv2.fillPoly(image, [obj_corner], OBJ_COLOR_MAP[obj_category],
+                         **CV2_SUB_VALUES)
+    return image
+
diff --git a/opencood/logreplay/map/map_manager.py b/opencood/logreplay/map/map_manager.py
new file mode 100644
index 00000000..88a177c6
--- /dev/null
+++ b/opencood/logreplay/map/map_manager.py
@@ -0,0 +1,945 @@
+# -*- coding: utf-8 -*-
+
+"""HDMap manager
+"""
+
+# Author: Runsheng Xu <rxx3386@ucla.edu>
+# License: TDG-Attribution-NonCommercial-NoDistrib
+
+import math
+import os.path
+import uuid
+
+import cv2
+import carla
+import numpy as np
+from matplotlib.path import Path
+from shapely.geometry import Polygon
+
+from logreplay.assets.presave_lib import EXCLUDE_ROAD_MAP, OR_Z_VALUE_MAP
+from logreplay.map.map_utils import \
+    world_to_sensor, lateral_shift, list_loc2array, list_wpt2array, \
+    convert_tl_status, exclude_off_road_agents, retrieve_city_object_info, \
+    obj_in_range
+from logreplay.map.map_drawing import \
+    cv2_subpixel, draw_agent, draw_road, \
+    draw_lane, road_exclude, draw_crosswalks, draw_city_objects
+from opencood.hypes_yaml.yaml_utils import save_yaml_wo_overwriting
+CV2_SUB_VALUES = {"shift": 9, "lineType": cv2.LINE_AA}
+
+
+class MapManager(object):
+    """
+    This class is used to manage HD Map. We emulate the style of Lyft dataset.
+
+    Parameters
+    ----------
+    world : Carla.World
+        CARLA world.
+
+    config : dict
+        All the map manager parameters.
+
+    output_root : str
+        The data dump root folder.
+
+    scene_name : str
+        The name of the scene.
+
+    Attributes
+    ----------
+    world : carla.world
+        Carla simulation world.
+
+    meter_per_pixel : float
+        m/pixel
+
+    raster_size : float
+        The rasterization map size in pixels.
+
+    radius_meter : float
+        The valid radius(m) in the center of the rasterization map.
+
+    topology : list
+        Map topology in list.
+
+    lane_info : dict
+        A dictionary that contains all lane information.
+
+    crosswalk_info : dict
+        A dictionary that contains all crosswalk information.
+        todo: will be implemented in the next version.
+
+    traffic_light_info : dict
+        A dictionary that contains all traffic light information.
+
+    bound_info : dict
+        A dictionary that saves boundary information of lanes and crosswalks.
+        It is used to efficiently filter out invalid lanes/crosswarlks.
+
+    lane_sample_resolution : int
+        The sampling resolution for drawing lanes.
+
+    draw_lane : bool
+        Whether to draw lane on the static bev map
+
+    static_bev : np.array
+        The static bev map containing lanes and drivable road information.
+
+    dynamic_bev : np.array
+        The dynamic bev map containing vehicle's information.
+
+    vis_bev : np.array
+        The comprehensive bev map for visualization.
+
+    """
+
+    def __init__(self, world, config, output_root, scene_name):
+        self.world = world
+        self.carla_map = world.get_map()
+        self.center = None
+        self.actor_id = None
+        self.out_root = output_root
+        self.scene_name = scene_name
+
+        # whether to activate this module
+        self.activate = config['activate']
+        if not self.activate:
+            return
+
+        # save flags
+        self.save_yml = config['save_yml']
+        self.save_static = config['save_static']
+        self.save_dynamic = config['save_dynamic']
+        self.save_lane = config['save_lane']
+        self.save_bev_vis = config['save_bev_vis']
+
+        # whether to visualize the bev map while running simulation
+        self.visualize = config['visualize']
+        # whether exclude the road that is unrelated to the ego vehicle
+        self.exclude_road = config['static']['exclude_road']
+        if self.scene_name in EXCLUDE_ROAD_MAP:
+            self.exclude_road = True
+
+        self.radius_meter = config['radius']
+        self.z_filter_value = config['static']['z_filter_value']
+        self.exclude_intersection_lane = \
+            config['static']['exclude_intersection_lane']
+        self.other_render_objs = config['static']['other_objs']
+
+        assert config['raster_size'][0] == config['raster_size'][1]
+        self.raster_size = np.array([config['raster_size'][0],
+                                     config['raster_size'][1]])
+        self.pixels_per_meter = self.raster_size[0] / (self.radius_meter * 2)
+        self.meter_per_pixel = 1 / self.pixels_per_meter
+        self.lane_sample_resolution = config['lane_sample_resolution']
+
+        # list of all start waypoints in HD Map
+        topology = [x[0] for x in self.carla_map.get_topology()]
+        # sort by altitude
+        self.topology = sorted(topology, key=lambda w: w.transform.location.z)
+
+        # basic elements in HDMap: lane, crosswalk and traffic light
+        self.lane_info = {}
+        self.crosswalk_info = {}
+        self.traffic_light_info = {}
+        # this is mainly used for efficient filtering
+        self.bound_info = {'lanes': {},
+                           'crosswalks': {}}
+        self.traffic_stop_pos = []
+
+        self.retrieve_light_stop_pos()
+        # generate information for traffic light
+        self.generate_tl_info(self.world)
+        # generate lane, crosswalk and boundary information
+        self.generate_lane_cross_info()
+
+        # generate labels
+        self.other_objs_info = \
+            retrieve_city_object_info(self.world,
+                                      self.other_render_objs)
+
+        # static related info
+        self.draw_lane = config['static']['draw_lane']
+        self.draw_traffic_light = config['static']['draw_traffic_light']
+        # dynamic related info
+        self.exclude_self = config['dynamic']['exclude_self']
+        self.exclude_off_road = config['dynamic']['exclude_off_road']
+
+        # whether to check visibility in camera for ego only
+        self.visibility = config['dynamic']['visibility']
+        # visible agent id for ego
+        self.vis_ids = []
+
+        # whether to check visibility in camera for multi-agent perspective
+        self.visibility_corp = config['dynamic']['visibility_corp']
+        # visible agent id for all vehicles
+        self.vis_corp_ids = []
+
+        # bev maps
+        self.dynamic_bev = 255 * np.zeros(
+            shape=(self.raster_size[1], self.raster_size[0], 3),
+            dtype=np.uint8)
+        self.vis_mask = 255 * np.zeros(
+            shape=(self.raster_size[1], self.raster_size[0], 3),
+            dtype=np.uint8)
+        self.vis_corp_mask = 255 * np.zeros(
+            shape=(self.raster_size[1], self.raster_size[0], 3),
+            dtype=np.uint8)
+        self.static_bev = 255 * np.zeros(
+            shape=(self.raster_size[1], self.raster_size[0], 3),
+            dtype=np.uint8)
+        self.lane_bev = 255 * np.zeros(
+            shape=(self.raster_size[1], self.raster_size[0], 3),
+            dtype=np.uint8)
+        self.vis_bev = 255 * np.ones(
+            shape=(self.raster_size[1], self.raster_size[0], 3),
+            dtype=np.uint8)
+
+    def run_step(self, cav_id, cav_content, veh_dict):
+        """
+        Rasterization + Visualize the bev map if needed.
+
+        Parameters
+        ----------
+        cav_id : str
+            The cav's original id in the dataset.
+        cav_content : dict
+            The cav's information.
+        veh_dict : dict
+            All vehicles' information. Used for visibility check for coop.
+        """
+        if not self.activate:
+            return
+
+        self.actor_id = cav_content['actor_id']
+        self.center = cav_content['cur_pose']
+        self.agent_id = cav_id
+        self.current_timstamp = cav_content['cur_count']
+
+        # clean buffer every round starts
+        self.vis_ids = []
+        self.vis_corp_ids = []
+
+        if self.visibility:
+            self.vis_ids = self.check_visibility_single(cav_content,
+                                                        self.vis_ids)
+        if self.visibility_corp:
+            self.vis_corp_ids = self.check_visibility_corp(veh_dict,
+                                                           self.vis_corp_ids)
+
+        self.rasterize_static()
+        self.rasterize_dynamic()
+
+        if self.exclude_off_road:
+            self.dynamic_bev = exclude_off_road_agents(self.static_bev,
+                                                       self.dynamic_bev)
+
+        if self.visualize:
+            cv2.imshow('the bev map of agent %s' % self.agent_id,
+                       self.vis_bev)
+            cv2.waitKey(1)
+        self.data_dump()
+
+    def check_visibility_corp(self, veh_dict, vis_corp_mask_list):
+        for _, veh_contnet in veh_dict.items():
+            if 'cav' in veh_contnet:
+                vis_corp_mask_list += \
+                    self.check_visibility_single(veh_contnet,
+                                                 vis_corp_mask_list)
+        vis_corp_mask_list = list(set(vis_corp_mask_list))
+        return vis_corp_mask_list
+
+    # check visibility agent list for ego vehicle
+    @staticmethod
+    def check_visibility_single(cav_content, vis_mask_list):
+        """
+        Retrieve visible objects in the ego camera.
+
+        Parameters
+        ----------
+        vis_mask_list : list
+        cav_content : dict
+        """
+
+        sensor_manager = cav_content['sensor_manager']
+        meta_data = sensor_manager.sensor_meta
+        for key, item in meta_data.items():
+            if 'semantic_lidar' in key:
+                vis_mask_list += item
+        vis_mask_list = list(set(vis_mask_list))
+        return vis_mask_list
+
+    @staticmethod
+    def get_bounds(left_lane, right_lane):
+        """
+        Get boundary information of a lane.
+
+        Parameters
+        ----------
+        left_lane : np.array
+            shape: (n, 3)
+        right_lane : np.array
+            shape: (n,3)
+        Returns
+        -------
+        bound : np.array
+        """
+        x_min = min(np.min(left_lane[:, 0]),
+                    np.min(right_lane[:, 0]))
+        y_min = min(np.min(left_lane[:, 1]),
+                    np.min(right_lane[:, 1]))
+        z_min = min(np.min(left_lane[:, 2]),
+                    np.min(right_lane[:, 2]))
+        x_max = max(np.max(left_lane[:, 0]),
+                    np.max(right_lane[:, 0]))
+        y_max = max(np.max(left_lane[:, 1]),
+                    np.max(right_lane[:, 1]))
+        z_max = max(np.max(left_lane[:, 2]),
+                    np.max(right_lane[:, 2]))
+
+        bounds = np.asarray([[[x_min, y_min], [x_max, y_max], [z_min, z_max]]])
+
+        return bounds
+
+    def agents_in_range(self,
+                        radius,
+                        agents_dict):
+        """
+        Filter out all agents out of the radius.
+
+        Parameters
+        ----------
+        radius : float
+            Radius in meters
+
+        agents_dict : dict
+            Dictionary containing all dynamic agents.
+
+        Returns
+        -------
+        The dictionary that only contains the agent in range.
+        """
+        final_agents = {}
+
+        # convert center to list format
+        center = [self.center.location.x, self.center.location.y]
+
+        for agent_id, agent in agents_dict.items():
+            location = agent['location']
+            distance = math.sqrt((location[0] - center[0]) ** 2 + \
+                                 (location[1] - center[1]) ** 2)
+            if distance < radius:
+                final_agents.update({agent_id: agent})
+
+        return final_agents
+
+    def indices_in_bounds(self,
+                          bounds: np.ndarray,
+                          half_extent: float) -> np.ndarray:
+        """
+        Get indices of elements for which the bounding box described by bounds
+        intersects the one defined around center (square with side 2*half_side)
+
+        Parameters
+        ----------
+        bounds :np.ndarray
+            array of shape Nx2x2 [[x_min,y_min],[x_max, y_max]]
+
+        half_extent : float
+            half the side of the bounding box centered around center
+
+        Returns
+        -------
+        np.ndarray: indices of elements inside radius from center
+        """
+        x_center, y_center, z_center = \
+            self.center.location.x, \
+            self.center.location.y, \
+            self.center.location.z
+
+        x_min_in = x_center > bounds[:, 0, 0] - half_extent
+        y_min_in = y_center > bounds[:, 0, 1] - half_extent
+        x_max_in = x_center < bounds[:, 1, 0] + half_extent
+        y_max_in = y_center < bounds[:, 1, 1] + half_extent
+
+        z_min_in = abs(z_center - bounds[:, 2, 0]) < self.z_filter_value
+        z_max_in = abs(z_center - bounds[:, 2, 1]) < self.z_filter_value
+
+        # this several scenes has some speciality and needs to be hardcoded
+        if self.scene_name in OR_Z_VALUE_MAP:
+            z_flag = z_min_in | z_max_in
+        else:
+            z_flag = z_min_in & z_max_in
+
+        return np.nonzero(x_min_in & y_min_in & x_max_in & y_max_in
+                          & z_flag)[0]
+
+    def retrieve_light_stop_pos(self):
+        # retrieve all stop and traffic light position
+        all_actors = self.world.get_actors()
+        for actor in all_actors:
+            if 'traffic_light' in actor.type_id or 'stop' in actor.type_id:
+                location = actor.get_transform().location
+                self.traffic_stop_pos.append((location.x, location.y))
+
+    def associate_lane_tl(self, mid_lane):
+        """
+        Given the waypoints for a certain lane, find the traffic light that
+        influence it.
+
+        Parameters
+        ----------
+        mid_lane : np.ndarray
+            The middle line of the lane.
+        Returns
+        -------
+        associate_tl_id : str
+            The associated traffic light id.
+        """
+        associate_tl_id = ''
+
+        for tl_id, tl_content in self.traffic_light_info.items():
+            trigger_poly = tl_content['corners']
+            # use Path to do fast computation
+            trigger_path = Path(trigger_poly.boundary)
+            # check if any point in the middle line inside the trigger area
+            check_array = trigger_path.contains_points(mid_lane[:, :2])
+
+            if check_array.any():
+                associate_tl_id = tl_id
+        return associate_tl_id
+
+    def data_dump(self):
+        """
+        Dump the data to the corresponding folder.
+        """
+        save_name = os.path.join(self.out_root, self.agent_id)
+
+        if not os.path.exists(save_name):
+            os.makedirs(save_name)
+
+        map_info = {'bev_map': {
+            'raster_size_pixel': self.raster_size,
+            'radius_meter': self.radius_meter,
+            'pixel_per_meter': self.pixels_per_meter,
+            'topology': self.valid_lane_info
+        }}
+        if self.save_yml:
+            # save metadata
+            save_yaml_name = os.path.join(save_name,
+                                          self.current_timstamp +
+                                          '_additional.yaml')
+            save_yaml_wo_overwriting(map_info, save_yaml_name)
+
+        if self.save_static:
+            # save rgb image
+            save_static_name = os.path.join(save_name,
+                                            self.current_timstamp +
+                                            '_bev_static.png')
+            cv2.imwrite(save_static_name, self.static_bev)
+
+        if self.save_lane:
+            # save lane topology seg
+            save_lane_name = os.path.join(save_name,
+                                          self.current_timstamp +
+                                          '_bev_lane.png')
+            cv2.imwrite(save_lane_name, self.lane_bev)
+
+        if self.save_dynamic:
+            # save dynamic bev
+            save_dynamic_name = os.path.join(save_name,
+                                             self.current_timstamp +
+                                             '_bev_dynamic.png')
+            cv2.imwrite(save_dynamic_name, self.dynamic_bev)
+
+        if self.visibility:
+            # save  visibility map for ego agent
+            save_visibility_name = os.path.join(save_name,
+                                                self.current_timstamp +
+                                                '_bev_visibility.png')
+            cv2.imwrite(save_visibility_name, self.vis_mask)
+
+        if self.visibility_corp:
+            # save  visibility map for ego agent
+            save_visibility_name = os.path.join(save_name,
+                                                self.current_timstamp +
+                                                '_bev_visibility_corp.png')
+            cv2.imwrite(save_visibility_name, self.vis_corp_mask)
+
+        if self.save_bev_vis:
+            # save visualize bev
+            save_vis_name = os.path.join(save_name,
+                                         self.current_timstamp +
+                                         '_bev_vis.png')
+            cv2.imwrite(save_vis_name, self.vis_bev)
+
+    def generate_lane_cross_info(self):
+        """
+        From the topology generate all lane and crosswalk
+        information in a dictionary under world's coordinate frame.
+        """
+        # list of str
+        lanes_id = []
+        crosswalks_ids = []
+
+        # boundary of each lane for later filtering
+        lanes_bounds = np.empty((0, 3, 2), dtype=np.float)
+        crosswalks_bounds = np.empty((0, 3, 2), dtype=np.float)
+
+        # for crosswalk information
+        crosswalks_list = self.split_cross_walks()
+        for (i, crosswalk) in enumerate(crosswalks_list):
+            crosswalk_id = uuid.uuid4().hex[:6].upper()
+            crosswalks_ids.append(crosswalk_id)
+
+            cross_marking = np.array(crosswalk)
+            bound = self.get_bounds(cross_marking, cross_marking)
+            crosswalks_bounds = np.append(crosswalks_bounds, bound, axis=0)
+
+            self.crosswalk_info.update({crosswalk_id: {'xyz': cross_marking}})
+
+        self.bound_info['crosswalks']['ids'] = crosswalks_ids
+        self.bound_info['crosswalks']['bounds'] = crosswalks_bounds
+
+        # loop all waypoints to get lane information
+        for (i, waypoint) in enumerate(self.topology):
+            # unique id for each lane
+            lane_id = uuid.uuid4().hex[:6].upper()
+            lanes_id.append(lane_id)
+
+            intersection_flag = True if waypoint.is_intersection and \
+                                        self.exclude_intersection_lane else False
+            # we need to exclude the intersections without traffic light/stops
+            if intersection_flag:
+                intersection_flag = False
+                for t_pos in self.traffic_stop_pos:
+                    distance = np.sqrt((t_pos[0]-waypoint.transform.location.x)**2 +
+                                       (t_pos[1]-waypoint.transform.location.y)**2)
+                    if distance <= 30:
+                        intersection_flag = True
+                        break
+
+            waypoints = [waypoint]
+            nxt = waypoint.next(self.lane_sample_resolution)[0]
+            # looping until next lane
+            while nxt.road_id == waypoint.road_id \
+                    and nxt.lane_id == waypoint.lane_id:
+                waypoints.append(nxt)
+                nxt = nxt.next(self.lane_sample_resolution)[0]
+
+            # waypoint is the centerline, we need to calculate left lane mark
+            left_marking = [lateral_shift(w.transform, -w.lane_width * 0.5) for
+                            w in waypoints]
+            right_marking = [lateral_shift(w.transform, w.lane_width * 0.5) for
+                             w in waypoints]
+            # convert the list of carla.Location to np.array
+            left_marking = list_loc2array(left_marking)
+            right_marking = list_loc2array(right_marking)
+            mid_lane = list_wpt2array(waypoints)
+
+            # get boundary information
+            bound = self.get_bounds(left_marking, right_marking)
+            lanes_bounds = np.append(lanes_bounds, bound, axis=0)
+
+            # associate with traffic light
+            tl_id = self.associate_lane_tl(mid_lane)
+
+            self.lane_info.update({lane_id: {'xyz_left': left_marking,
+                                             'xyz_right': right_marking,
+                                             'xyz_mid': mid_lane,
+                                             'tl_id': tl_id,
+                                             'intersection_flag':
+                                                 intersection_flag}})
+        # boundary information
+        self.bound_info['lanes']['ids'] = lanes_id
+        self.bound_info['lanes']['bounds'] = lanes_bounds
+
+    def split_cross_walks(self):
+        """
+        Find each crosswalk with their key points.
+        """
+        all_cross_walks = self.carla_map.get_crosswalks()
+        cross_walks_list = []
+
+        tmp_list = []
+        for key_points in all_cross_walks:
+            if (key_points.x, key_points.y, key_points.z) in tmp_list:
+                cross_walks_list.append(tmp_list)
+                tmp_list = []
+            else:
+                tmp_list.append((key_points.x, key_points.y, key_points.z))
+
+        return cross_walks_list
+
+    def generate_tl_info(self, world):
+        """
+        Generate traffic light information under world's coordinate frame.
+
+        Parameters
+        ----------
+        world : carla.world
+            Carla simulator env.
+
+        Returns
+        -------
+        A dictionary containing traffic lights information.
+        """
+        tl_list = world.get_actors().filter('traffic.traffic_light*')
+
+        for tl_actor in tl_list:
+            tl_id = uuid.uuid4().hex[:4].upper()
+
+            base_transform = tl_actor.get_transform()
+            base_rot = base_transform.rotation.yaw
+            # this is where the vehicle stops
+            area_loc = base_transform.transform(
+                tl_actor.trigger_volume.location)
+            area_transform = carla.Transform(area_loc,
+                                             carla.Rotation(yaw=base_rot))
+
+            # bbx extent
+            ext = tl_actor.trigger_volume.extent
+            # the y is to narrow
+            ext.y += 0.5
+            ext_corners = np.array([
+                [-ext.x, -ext.y],
+                [ext.x, -ext.y],
+                [ext.x, ext.y],
+                [-ext.x, ext.y]])
+            for i in range(ext_corners.shape[0]):
+                corrected_loc = area_transform.transform(
+                    carla.Location(ext_corners[i][0], ext_corners[i][1]))
+                ext_corners[i, 0] = corrected_loc.x
+                ext_corners[i, 1] = corrected_loc.y
+
+            # use shapely lib to convert to polygon
+            corner_poly = Polygon(ext_corners)
+            self.traffic_light_info.update(
+                {tl_id: {'actor': tl_actor,
+                         'corners': corner_poly,
+                         'base_rot': base_rot,
+                         'base_transform': base_transform
+                         }})
+
+    def generate_lane_area(self, xyz_left, xyz_right):
+        """
+        Generate the lane area poly under rasterization map's center
+        coordinate frame.
+
+        Parameters
+        ----------
+        xyz_left : np.ndarray
+            Left lanemarking of a lane, shape: (n, 3).
+        xyz_right : np.ndarray
+            Right lanemarking of a lane, shape: (n, 3).
+
+        Returns
+        -------
+        lane_area : np.ndarray
+            Combine left and right lane together to form a polygon.
+        """
+        lane_area = np.zeros((2, xyz_left.shape[0], 2))
+        # convert coordinates to center's coordinate frame
+        xyz_left = xyz_left.T
+        xyz_left = np.r_[
+            xyz_left, [np.ones(xyz_left.shape[1])]]
+        xyz_right = xyz_right.T
+        xyz_right = np.r_[
+            xyz_right, [np.ones(xyz_right.shape[1])]]
+
+        # ego's coordinate frame
+        xyz_left = world_to_sensor(xyz_left, self.center).T
+        xyz_right = world_to_sensor(xyz_right, self.center).T
+
+        # to image coordinate frame
+        lane_area[0] = xyz_left[:, :2]
+        lane_area[1] = xyz_right[::-1, :2]
+        # switch x and y
+        lane_area = lane_area[..., ::-1]
+        # y revert
+        lane_area[:, :, 1] = -lane_area[:, :, 1]
+
+        lane_area[:, :, 0] = lane_area[:, :, 0] * self.pixels_per_meter + \
+                             self.raster_size[0] // 2
+        lane_area[:, :, 1] = lane_area[:, :, 1] * self.pixels_per_meter + \
+                             self.raster_size[1] // 2
+
+        # to make more precise polygon
+        lane_area = cv2_subpixel(lane_area)
+
+        return lane_area
+
+    def generate_cross_area(self, xyz):
+        """
+        Generate the cross lane under rasterization map's center
+        coordinate frame.
+
+        Parameters
+        ----------
+        xyz : np.ndarray
+            Crosswalk lane marking, shape: (n, 3).
+        Returns
+        -------
+        lane_area : np.ndarray
+            Combine up and down line together.
+        """
+        if xyz.shape[0] == 2:
+            print('dman')
+        lane_area = np.zeros((2, xyz.shape[0] // 2, 2))
+        # convert coordinates to center's coordinate frame
+        xyz = xyz.T
+        xyz = np.r_[
+            xyz, [np.ones(xyz.shape[1])]]
+
+        # ego's coordinate frame
+        xyz = world_to_sensor(xyz, self.center).T
+
+        # to image coordinate frame
+        lane_area[0] = xyz[:xyz.shape[0] // 2, :2]
+        lane_area[1] = xyz[xyz.shape[0] // 2:, :2]
+        # switch x and y
+        lane_area = lane_area[..., ::-1]
+        # y revert
+        lane_area[:, :, 1] = -lane_area[:, :, 1]
+
+        lane_area[:, :, 0] = lane_area[:, :, 0] * self.pixels_per_meter + \
+                             self.raster_size[0] // 2
+        lane_area[:, :, 1] = lane_area[:, :, 1] * self.pixels_per_meter + \
+                             self.raster_size[1] // 2
+        # to make more precise polygon
+        lane_area = cv2_subpixel(lane_area)
+
+        return lane_area
+
+    def generate_agent_area(self, corners):
+        """
+        Convert the agent's bbx corners from world coordinates to
+        rasterization coordinates.
+
+        Parameters
+        ----------
+        corners : list
+            The four corners of the agent's bbx under world coordinate.
+
+        Returns
+        -------
+        agent four corners in image.
+        """
+        # (4, 3) numpy array
+        corners = np.array(corners)
+        # for homogeneous transformation
+        corners = corners.T
+        corners = np.r_[
+            corners, [np.ones(corners.shape[1])]]
+        # convert to ego's coordinate frame
+        corners = world_to_sensor(corners, self.center).T
+        corners = corners[:, :2]
+
+        # switch x and y
+        corners = corners[..., ::-1]
+        # y revert
+        corners[:, 1] = -corners[:, 1]
+
+        corners[:, 0] = corners[:, 0] * self.pixels_per_meter + \
+                        self.raster_size[0] // 2
+        corners[:, 1] = corners[:, 1] * self.pixels_per_meter + \
+                        self.raster_size[1] // 2
+
+        # to make more precise polygon
+        corner_area = cv2_subpixel(corners[:, :2])
+
+        return corner_area
+
+    def load_agents_world(self):
+        """
+        Load all the dynamic agents info from server directly
+        into a  dictionary.
+
+        Returns
+        -------
+        The dictionary contains all agents info in the carla world.
+        """
+
+        agent_list = self.world.get_actors().filter('vehicle.*')
+        dynamic_agent_info = {}
+
+        for agent in agent_list:
+            agent_id = agent.id
+
+            agent_transform = agent.get_transform()
+            agent_loc = [agent_transform.location.x,
+                         agent_transform.location.y,
+                         agent_transform.location.z, ]
+
+            agent_yaw = agent_transform.rotation.yaw
+
+            # calculate 4 corners
+            bb = agent.bounding_box.extent
+            corners = [
+                carla.Location(x=-bb.x, y=-bb.y),
+                carla.Location(x=-bb.x, y=bb.y),
+                carla.Location(x=bb.x, y=bb.y),
+                carla.Location(x=bb.x, y=-bb.y)
+            ]
+            # corners are originally in ego coordinate frame, convert to
+            # world coordinate
+            agent_transform.transform(corners)
+            corners_reformat = [[x.x, x.y, x.z] for x in corners]
+
+            dynamic_agent_info[agent_id] = {'location': agent_loc,
+                                            'yaw': agent_yaw,
+                                            'corners': corners_reformat}
+        return dynamic_agent_info
+
+    def rasterize_dynamic(self):
+        """
+        Rasterize the dynamic agents.
+
+        Returns
+        -------
+        Rasterization image.
+        """
+        self.dynamic_bev = 255 * np.zeros(
+            shape=(self.raster_size[1], self.raster_size[0], 3),
+            dtype=np.uint8)
+        self.vis_mask = 255 * np.zeros(
+            shape=(self.raster_size[1], self.raster_size[0], 3),
+            dtype=np.uint8)
+        self.vis_corp_mask = 255 * np.zeros(
+            shape=(self.raster_size[1], self.raster_size[0], 3),
+            dtype=np.uint8)
+        # filter using half a radius from the center
+        raster_radius = \
+            float(np.linalg.norm(self.raster_size *
+                                 np.array([self.meter_per_pixel,
+                                           self.meter_per_pixel]))) / 2
+        # retrieve all agents
+        dynamic_agents = self.load_agents_world()
+        # filter out agents out of range
+        final_agents = self.agents_in_range(raster_radius,
+                                            dynamic_agents)
+
+        corner_list = []
+        vis_corner_list = []
+        vis_corp_corner_list = []
+        for agent_id, agent in final_agents.items():
+            # in case we don't want to draw the cav itself
+            if agent_id == self.actor_id and self.exclude_self:
+                continue
+            agent_corner = self.generate_agent_area(agent['corners'])
+            corner_list.append(agent_corner)
+
+            # get visibility for single vehicle
+            if agent_id in self.vis_ids:
+                vis_corner_list.append(agent_corner)
+            # get visibility for multi-agents
+            if agent_id in self.vis_corp_ids:
+                vis_corp_corner_list.append(agent_corner)
+
+        self.dynamic_bev = draw_agent(corner_list, self.dynamic_bev)
+        self.vis_bev = draw_agent(corner_list, self.vis_bev)
+        self.vis_mask = draw_agent(vis_corner_list, self.vis_mask)
+        self.vis_corp_mask = draw_agent(vis_corp_corner_list,
+                                        self.vis_corp_mask)
+
+    def rasterize_static(self):
+        """
+        Generate the static bev map.
+        """
+        self.static_bev = 255 * np.zeros(
+            shape=(self.raster_size[1], self.raster_size[0], 3),
+            dtype=np.uint8)
+        self.lane_bev = 255 * np.zeros(
+            shape=(self.raster_size[1], self.raster_size[0], 3),
+            dtype=np.uint8)
+        self.vis_bev = 255 * np.ones(
+            shape=(self.raster_size[1], self.raster_size[0], 3),
+            dtype=np.uint8)
+
+        # filter using half a radius from the center
+        raster_radius = \
+            float(np.linalg.norm(self.raster_size *
+                                 np.array([self.meter_per_pixel,
+                                           self.meter_per_pixel]))) / 2
+        lane_indices = self.indices_in_bounds(self.bound_info['lanes'][
+                                                  'bounds'],
+                                              raster_radius)
+        cross_indices = self.indices_in_bounds(self.bound_info['crosswalks'][
+                                                  'bounds'],
+                                              raster_radius)
+
+        # --------- Retrieve road topology related first ---------- #
+        lanes_area_list = []
+        cross_area_list = []
+        lane_type_list = []
+        intersection_list = []
+
+        for idx, cross_idx in enumerate(cross_indices):
+            cross_idx = self.bound_info['crosswalks']['ids'][cross_idx]
+            cross_info = self.crosswalk_info[cross_idx]
+
+            cross_area = self.generate_cross_area(cross_info['xyz'])
+            cross_area_list.append(cross_area)
+
+        for idx, lane_idx in enumerate(lane_indices):
+            lane_idx = self.bound_info['lanes']['ids'][lane_idx]
+            lane_info = self.lane_info[lane_idx]
+
+            # save the topology info for this cav
+            self.valid_lane_info = lane_info
+
+            xyz_left, xyz_right = \
+                lane_info['xyz_left'], lane_info['xyz_right']
+
+            # generate lane area
+            lane_area = self.generate_lane_area(xyz_left, xyz_right)
+            lanes_area_list.append(lane_area)
+
+            # intersection flag
+            intersection_list.append(lane_info['intersection_flag'])
+
+            # check the associated traffic light
+            associated_tl_id = lane_info['tl_id']
+            if associated_tl_id and self.draw_traffic_light:
+                tl_actor = self.traffic_light_info[associated_tl_id]['actor']
+                status = convert_tl_status(tl_actor.get_state())
+                lane_type_list.append(status)
+            else:
+                lane_type_list.append('normal')
+
+        self.static_bev = draw_road(lanes_area_list,
+                                    self.static_bev)
+        if self.exclude_road:
+            self.static_bev = road_exclude(self.static_bev)
+
+        if self.draw_lane:
+            self.lane_bev = draw_lane(lanes_area_list, lane_type_list,
+                                      self.lane_bev, intersection_list,
+                                      vis=False)
+            self.lane_bev = draw_crosswalks(cross_area_list, self.lane_bev)
+            self.lane_bev[self.static_bev == 0] = 0
+
+        # --------- Retrieve city objects such as buildings ---------- #
+        # first filter out objects out of range
+        final_city_objs = obj_in_range(self.center,
+                                       raster_radius,
+                                       self.other_objs_info)
+        for obj_cat, obj_content in final_city_objs.items():
+            for obj_id, obj in obj_content.items():
+                corner_area = self.generate_agent_area(obj['corners'])
+                obj['corner_area'] = corner_area
+
+        # we try to draw everything for visualization, but only dumping the
+        # elements we need.
+        self.vis_bev = draw_city_objects(final_city_objs, self.vis_bev)
+        self.vis_bev = draw_road(lanes_area_list,
+                                 self.vis_bev,
+                                 visualize=True)
+        self.vis_bev = draw_lane(lanes_area_list, lane_type_list,
+                                 self.vis_bev, intersection_list)
+        self.vis_bev = draw_crosswalks(cross_area_list, self.vis_bev)
+        self.vis_bev = cv2.cvtColor(self.vis_bev, cv2.COLOR_RGB2BGR)
+
+    def destroy(self):
+        cv2.destroyAllWindows()
diff --git a/opencood/logreplay/map/map_utils.py b/opencood/logreplay/map/map_utils.py
new file mode 100644
index 00000000..91e097b7
--- /dev/null
+++ b/opencood/logreplay/map/map_utils.py
@@ -0,0 +1,257 @@
+# -*- coding: utf-8 -*-
+
+"""HDMap utilities
+"""
+
+# Author: Runsheng Xu <rxx3386@ucla.edu>
+# License: TDG-Attribution-NonCommercial-NoDistrib
+
+import carla
+import numpy as np
+import uuid
+import math
+from enum import IntEnum
+
+LABEL_TO_CARLA = {'building': carla.CityObjectLabel.Buildings,
+                  'terrain': carla.CityObjectLabel.Terrain,
+                  'sidewalk': carla.CityObjectLabel.Sidewalks}
+
+
+class InterpolationMethod(IntEnum):
+    INTER_METER = 0  # fixed interpolation at a given step in meters
+    INTER_ENSURE_LEN = 1  # ensure we always get the same number of elements
+
+
+def lateral_shift(transform, shift):
+    transform.rotation.yaw += 90
+    return transform.location + shift * transform.get_forward_vector()
+
+
+def list_loc2array(list_location):
+    """
+    Convert list of carla location to np.array
+    Parameters
+    ----------
+    list_location : list
+        List of carla locations.
+
+    Returns
+    -------
+    loc_array : np.array
+        Numpy array of shape (N, 3)
+    """
+    loc_array = np.zeros((len(list_location), 3))
+    for (i, carla_location) in enumerate(list_location):
+        loc_array[i, 0] = carla_location.x
+        loc_array[i, 1] = carla_location.y
+        loc_array[i, 2] = carla_location.z
+
+    return loc_array
+
+
+def list_wpt2array(list_wpt):
+    """
+    Convert list of carla transform to np.array
+    Parameters
+    ----------
+    list_wpt : list
+        List of carla waypoint.
+
+    Returns
+    -------
+    loc_array : np.array
+        Numpy array of shape (N, 3)
+    """
+    loc_array = np.zeros((len(list_wpt), 3))
+    for (i, carla_wpt) in enumerate(list_wpt):
+        loc_array[i, 0] = carla_wpt.transform.location.x
+        loc_array[i, 1] = carla_wpt.transform.location.y
+        loc_array[i, 2] = carla_wpt.transform.location.z
+
+    return loc_array
+
+
+def convert_tl_status(status):
+    """
+    Convert carla.TrafficLightState to str.
+    Parameters
+    ----------
+    status : carla.TrafficLightState
+
+    Returns
+    -------
+    status_str : str
+    """
+    if status == carla.TrafficLightState.Red:
+        return 'red'
+    elif status == carla.TrafficLightState.Green:
+        return 'green'
+    elif status == carla.TrafficLightState.Yellow:
+        return 'yellow'
+    else:
+        return 'normal'
+
+
+def x_to_world_transformation(transform):
+    """
+    Get the transformation matrix from x(it can be vehicle or sensor)
+    coordinates to world coordinate.
+
+    Parameters
+    ----------
+    transform : carla.Transform
+        The transform that contains location and rotation
+
+    Returns
+    -------
+    matrix : np.ndarray
+        The transformation matrx.
+
+    """
+    rotation = transform.rotation
+    location = transform.location
+
+    # used for rotation matrix
+    c_y = np.cos(np.radians(rotation.yaw))
+    s_y = np.sin(np.radians(rotation.yaw))
+    c_r = np.cos(np.radians(rotation.roll))
+    s_r = np.sin(np.radians(rotation.roll))
+    c_p = np.cos(np.radians(rotation.pitch))
+    s_p = np.sin(np.radians(rotation.pitch))
+
+    matrix = np.identity(4)
+    # translation matrix
+    matrix[0, 3] = location.x
+    matrix[1, 3] = location.y
+    matrix[2, 3] = location.z
+
+    # rotation matrix
+    matrix[0, 0] = c_p * c_y
+    matrix[0, 1] = c_y * s_p * s_r - s_y * c_r
+    matrix[0, 2] = -c_y * s_p * c_r - s_y * s_r
+    matrix[1, 0] = s_y * c_p
+    matrix[1, 1] = s_y * s_p * s_r + c_y * c_r
+    matrix[1, 2] = -s_y * s_p * c_r + c_y * s_r
+    matrix[2, 0] = s_p
+    matrix[2, 1] = -c_p * s_r
+    matrix[2, 2] = c_p * c_r
+
+    return matrix
+
+
+def world_to_sensor(cords, sensor_transform):
+    """
+    Transform coordinates from world reference to sensor reference.
+
+    Parameters
+    ----------
+    cords : np.ndarray
+        Coordinates under world reference, shape: (4, n).
+
+    sensor_transform : carla.Transform
+        Sensor position in the world.
+
+    Returns
+    -------
+    sensor_cords : np.ndarray
+        Coordinates in the sensor reference.
+
+    """
+    sensor_world_matrix = x_to_world_transformation(sensor_transform)
+    world_sensor_matrix = np.linalg.inv(sensor_world_matrix)
+    sensor_cords = np.dot(world_sensor_matrix, cords)
+
+    return sensor_cords
+
+
+def exclude_off_road_agents(static_bev, dynamic_bev):
+    dynamic_bev[static_bev == 0] = 0
+    return dynamic_bev
+
+
+def retrieve_city_object_info(world, label_list):
+    """
+    A general function to retrieve object bbx in carla world except vehicle,
+    lane, crosswalk and road.
+
+    Parameters
+    ----------
+    world : carla.World
+        Carla world object
+    label_list : list of str
+        The label that users want to retrieve.
+
+    Returns
+    -------
+    A dictionary with information of the retrieved objects.
+    """
+    city_object_info = {}
+    for label_name in label_list:
+        object_ins = world.get_level_bbs(LABEL_TO_CARLA[label_name])
+
+        obj_info = {}
+
+        for obj in object_ins:
+            obj_id = uuid.uuid4().hex[:6].upper()
+
+            obj_transform = carla.Transform(obj.location,
+                                            obj.rotation)
+            obj_loc = [obj.location.x,
+                       obj.location.y,
+                       obj.location.z, ]
+            obj_yaw = obj.rotation.yaw
+
+            # calculate 4 corners
+            bb = obj.extent
+            corners = [
+                carla.Location(x=-bb.x, y=-bb.y),
+                carla.Location(x=-bb.x, y=bb.y),
+                carla.Location(x=bb.x, y=bb.y),
+                carla.Location(x=bb.x, y=-bb.y)
+            ]
+
+            obj_transform.transform(corners)
+            corners_reformat = [[x.x, x.y, x.z] for x in corners]
+
+            obj_info[obj_id] = {'location': obj_loc,
+                                'yaw': obj_yaw,
+                                'corners': corners_reformat}
+
+        city_object_info.update({label_name: obj_info})
+
+    return city_object_info
+
+
+def obj_in_range(center, radius, obj_info_dict):
+    """
+    Retrieve the object in range.
+
+    Parameters
+    ----------
+    center : carla.Transform
+        The ego position
+
+    radius : float
+        Valid radius.
+
+    obj_info_dict : dict
+
+    Returns
+    -------
+    A dictionary that contains objects in range.
+    """
+    final_objs = {}
+
+    for obj_category, obj_contents in obj_info_dict.items():
+        cur_objs = {}
+        for obj_id, obj_info in obj_contents.items():
+            corners = obj_info['corners']
+            for corner in corners:
+                distance = math.sqrt((corner[0] - center.location.x) ** 2 + \
+                                     (corner[1] - center.location.y) ** 2)
+                if distance < radius:
+                    cur_objs.update({obj_id: obj_info})
+                    break
+        final_objs.update({obj_category: cur_objs})
+
+    return final_objs
diff --git a/opencood/logreplay/scenario/__init__.py b/opencood/logreplay/scenario/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/opencood/logreplay/scenario/scenarios_manager.py b/opencood/logreplay/scenario/scenarios_manager.py
new file mode 100644
index 00000000..f1886db5
--- /dev/null
+++ b/opencood/logreplay/scenario/scenarios_manager.py
@@ -0,0 +1,79 @@
+import os
+from collections import OrderedDict
+
+from opencood.hypes_yaml.yaml_utils import load_yaml
+from logreplay.scenario.scene_manager import SceneManager
+
+
+class ScenariosManager:
+    """
+    Format all scenes in a structured way.
+
+    Parameters
+    ----------
+    scenario_params: dict
+        Overall parameters for the replayed scenes.
+
+    Attributes
+    ----------
+
+    """
+
+    def __init__(self, scenario_params):
+        # this defines carla world sync mode, weather, town name, and seed.
+        self.scene_params = scenario_params
+
+        # e.g. /opv2v/data/train
+        root_dir = self.scene_params['root_dir']
+
+        # first load all paths of different scenarios
+        scenario_folders = sorted([os.path.join(root_dir, x)
+                                   for x in os.listdir(root_dir) if
+                                   os.path.isdir(os.path.join(root_dir, x))])
+        self.scenario_database = OrderedDict()
+
+        # loop over all scenarios
+        for (i, scenario_folder) in enumerate(scenario_folders):
+            scene_name = os.path.split(scenario_folder)[-1]
+            self.scenario_database.update({scene_name: OrderedDict()})
+
+            # load the collection yaml file
+            protocol_yml = [x for x in os.listdir(scenario_folder)
+                            if x.endswith('.yaml')]
+            collection_params = load_yaml(os.path.join(scenario_folder,
+                                                       protocol_yml[0]))
+
+            # create the corresponding scene manager
+            cur_sg = SceneManager(scenario_folder,
+                                  scene_name,
+                                  collection_params,
+                                  scenario_params)
+            self.scenario_database[scene_name].update({'scene_manager':
+                                                       cur_sg})
+
+    def tick(self):
+        """
+        Tick for every scene manager to do the log replay.
+        """
+        for scene_name, scene_content in self.scenario_database.items():
+            print('log replay %s' % scene_name)
+            scene_manager = scene_content['scene_manager']
+            run_flag = True
+
+            scene_manager.start_simulator()
+
+            while run_flag:
+                run_flag = scene_manager.tick()
+
+            scene_manager.close()
+
+
+if __name__ == '__main__':
+    from opencood.hypes_yaml.yaml_utils import load_yaml
+    scene_params = load_yaml('../hypes_yaml/replay.yaml')
+    scenarion_manager = ScenariosManager(scenario_params=scene_params)
+    scenarion_manager.tick()
+    print('test passed')
+
+
+
diff --git a/opencood/logreplay/scenario/scene_manager.py b/opencood/logreplay/scenario/scene_manager.py
new file mode 100644
index 00000000..e5fac07a
--- /dev/null
+++ b/opencood/logreplay/scenario/scene_manager.py
@@ -0,0 +1,465 @@
+import os
+import random
+import sys
+from collections import OrderedDict
+
+import carla
+import numpy as np
+
+from logreplay.assets.utils import find_town, find_blue_print
+from logreplay.assets.presave_lib import bcolors
+from logreplay.map.map_manager import MapManager
+from logreplay.sensors.sensor_manager import SensorManager
+from opencood.hypes_yaml.yaml_utils import load_yaml
+
+
+class SceneManager:
+    """
+    Manager for each scene for spawning, moving and destroying.
+
+    Parameters
+    ----------
+    folder : str
+        The folder to the current scene.
+
+    scene_name : str
+        The scene's name.
+
+    collection_params : dict
+        The collecting protocol information.
+
+    scenario_params : dict
+        The replay params.
+    """
+
+    def __init__(self, folder, scene_name, collection_params, scenario_params):
+        self.scene_name = scene_name
+        self.town_name = find_town(scene_name)
+        self.collection_params = collection_params
+        self.scenario_params = scenario_params
+        self.cav_id_list = []
+
+        # dumping related
+        self.output_root = os.path.join(scenario_params['output_dir'],
+                                        scene_name)
+
+        if 'seed' in collection_params['world']:
+            np.random.seed(collection_params['world']['seed'])
+            random.seed(collection_params['world']['seed'])
+
+        # at least 1 cav should show up
+        cav_list = sorted([x for x in os.listdir(folder)
+                           if os.path.isdir(
+                os.path.join(folder, x))])
+        assert len(cav_list) > 0
+
+        self.database = OrderedDict()
+        # we want to save timestamp as the parent keys for cavs
+        cav_sample = os.path.join(folder, cav_list[0])
+
+        yaml_files = \
+            sorted([os.path.join(cav_sample, x)
+                    for x in os.listdir(cav_sample) if
+                    x.endswith('.yaml') and 'additional' not in x])
+        self.timestamps = self.extract_timestamps(yaml_files)
+
+        # loop over all timestamps
+        for timestamp in self.timestamps:
+            self.database[timestamp] = OrderedDict()
+            # loop over all cavs
+            for (j, cav_id) in enumerate(cav_list):
+                if cav_id not in self.cav_id_list:
+                    self.cav_id_list.append(str(cav_id))
+
+                self.database[timestamp][cav_id] = OrderedDict()
+                cav_path = os.path.join(folder, cav_id)
+
+                yaml_file = os.path.join(cav_path,
+                                         timestamp + '.yaml')
+                self.database[timestamp][cav_id]['yaml'] = \
+                    yaml_file
+
+        # this is used to dynamically save all information of the objects
+        self.veh_dict = OrderedDict()
+        # used to count timestamp
+        self.cur_count = 0
+        # used for HDMap
+        self.map_manager = None
+
+    def start_simulator(self):
+        """
+        Connect to the carla simulator for log replay.
+        """
+        simulation_config = self.collection_params['world']
+
+        # simulation sync mode time step
+        fixed_delta_seconds = simulation_config['fixed_delta_seconds']
+        weather_config = simulation_config[
+            'weather'] if "weather" in simulation_config else None
+
+        # setup the carla client
+        self.client = \
+            carla.Client('localhost', simulation_config['client_port'])
+        self.client.set_timeout(10.0)
+
+        # load the map
+        if self.town_name != 'Culver_City':
+            try:
+                self.world = self.client.load_world(self.town_name)
+            except RuntimeError:
+                print(
+                    f"{bcolors.FAIL} %s is not found in your CARLA repo! "
+                    f"Please download all town maps to your CARLA "
+                    f"repo!{bcolors.ENDC}" % self.town_name)
+        else:
+            self.world = self.client.get_world()
+
+        if not self.world:
+            sys.exit('World loading failed')
+
+        # setup the new setting
+        self.origin_settings = self.world.get_settings()
+        new_settings = self.world.get_settings()
+
+        new_settings.synchronous_mode = True
+        new_settings.fixed_delta_seconds = fixed_delta_seconds
+
+        self.world.apply_settings(new_settings)
+        # set weather if needed
+        if weather_config is not None:
+            weather = self.set_weather(weather_config)
+            self.world.set_weather(weather)
+        # get map
+        self.carla_map = self.world.get_map()
+        # spectator
+        self.spectator = self.world.get_spectator()
+        # hd map manager per scene
+        self.map_manager = MapManager(self.world,
+                                      self.scenario_params['map'],
+                                      self.output_root,
+                                      self.scene_name)
+
+    def tick(self):
+        """
+        Spawn the vehicle to the correct position
+        """
+        if self.cur_count >= len(self.timestamps):
+            return False
+
+        cur_timestamp = self.timestamps[self.cur_count]
+        cur_database = self.database[cur_timestamp]
+
+        for i, (cav_id, cav_yml) in enumerate(cur_database.items()):
+            cav_content = load_yaml(cav_yml['yaml'])
+            if cav_id not in self.veh_dict:
+                self.spawn_cav(cav_id, cav_content, cur_timestamp)
+            else:
+                self.move_vehicle(cav_id,
+                                  cur_timestamp,
+                                  self.structure_transform_cav(
+                                      (cav_content['true_ego_pos'])))
+
+            self.veh_dict[cav_id]['cav'] = True
+            # spawn the sensor on each cav
+            if 'sensor_manager' not in self.veh_dict[cav_id]:
+                self.veh_dict[cav_id]['sensor_manager'] = \
+                    SensorManager(cav_id, self.veh_dict[cav_id],
+                                  self.world, self.scenario_params['sensor'],
+                                  self.output_root)
+
+            # set the spectator to the first cav
+            if i == 0:
+                transform = self.structure_transform_cav(
+                    cav_content['true_ego_pos'])
+                self.spectator.set_transform(
+                    carla.Transform(transform.location +
+                                    carla.Location(
+                                        z=70),
+                                    carla.Rotation(
+                                        pitch=-90
+                                    )))
+
+            for bg_veh_id, bg_veh_content in cav_content['vehicles'].items():
+                if str(bg_veh_id) not in self.veh_dict:
+                    self.spawn_bg_vehicles(bg_veh_id,
+                                           bg_veh_content,
+                                           cur_timestamp)
+                else:
+                    self.move_vehicle(str(bg_veh_id),
+                                      cur_timestamp,
+                                      self.structure_transform_bg_veh(
+                                          bg_veh_content['location'],
+                                          bg_veh_content['angle']))
+        # remove the vehicles that are not in any cav's scope
+        self.destroy_vehicle(cur_timestamp)
+
+        self.cur_count += 1
+        self.world.tick()
+
+        # we dump data after tick() so the agent can retrieve the newest info
+        self.sensor_dumping(cur_timestamp)
+        self.map_dumping()
+
+        return True
+
+    def map_dumping(self):
+        """
+        Dump bev map related.
+
+        Parameters
+        ----------
+        """
+        for veh_id, veh_content in self.veh_dict.items():
+            if 'cav' in veh_content:
+                self.map_manager.run_step(veh_id, veh_content, self.veh_dict)
+
+    def sensor_dumping(self, cur_timestamp):
+        for veh_id, veh_content in self.veh_dict.items():
+            if 'sensor_manager' in veh_content:
+                veh_content['sensor_manager'].run_step(cur_timestamp)
+
+    def spawn_cav(self, cav_id, cav_content, cur_timestamp):
+        """
+        Spawn the cav based on current content.
+
+        Parameters
+        ----------
+        cav_id : str
+            The saved cav_id.
+
+        cav_content : dict
+            The information in the cav's folder.
+
+        cur_timestamp : str
+            This is used to judge whether this vehicle has been already
+            called in this timestamp.
+        """
+
+        # cav always use lincoln
+        model = 'vehicle.lincoln.mkz_2017'
+
+        # retrive the blueprint library
+        blueprint_library = self.world.get_blueprint_library()
+        cav_bp = blueprint_library.find(model)
+        # cav is always green
+        color = '0, 0, 255'
+        cav_bp.set_attribute('color', color)
+
+        cur_pose = cav_content['true_ego_pos']
+        # convert to carla needed format
+        cur_pose = self.structure_transform_cav(cur_pose)
+
+        # spawn the vehicle
+        vehicle = \
+            self.world.try_spawn_actor(cav_bp, cur_pose)
+
+        while not vehicle:
+            cur_pose.location.z += 0.01
+            vehicle = \
+                self.world.try_spawn_actor(cav_bp, cur_pose)
+
+        self.veh_dict.update({str(cav_id): {
+            'cur_pose': cur_pose,
+            'model': model,
+            'color': color,
+            'actor_id': vehicle.id,
+            'actor': vehicle,
+            'cur_count': cur_timestamp
+        }})
+
+    def spawn_bg_vehicles(self, bg_veh_id, bg_veh_content, cur_timestamp):
+        """
+        Spawn the background vehicle.
+
+        Parameters
+        ----------
+        bg_veh_id : str
+            The id of the bg vehicle.
+        bg_veh_content : dict
+            The contents of the bg vehicle
+        cur_timestamp : str
+            This is used to judge whether this vehicle has been already
+            called in this timestamp.
+        """
+        # retrieve the blueprint library
+        blueprint_library = self.world.get_blueprint_library()
+
+        cur_pose = self.structure_transform_bg_veh(bg_veh_content['location'],
+                                                   bg_veh_content['angle'])
+        if str(bg_veh_id) in self.cav_id_list:
+            model = 'vehicle.lincoln.mkz_2017'
+            veh_bp = blueprint_library.find(model)
+            color = '0, 0, 255'
+        else:
+            model = find_blue_print(bg_veh_content['extent'])
+            if not model:
+                print('model net found for %s' % bg_veh_id)
+            veh_bp = blueprint_library.find(model)
+
+            color = random.choice(
+                veh_bp.get_attribute('color').recommended_values)
+
+        veh_bp.set_attribute('color', color)
+
+        # spawn the vehicle
+        vehicle = \
+            self.world.try_spawn_actor(veh_bp, cur_pose)
+
+        while not vehicle:
+            cur_pose.location.z += 0.01
+            vehicle = \
+                self.world.try_spawn_actor(veh_bp, cur_pose)
+
+        self.veh_dict.update({str(bg_veh_id): {
+            'cur_pose': cur_pose,
+            'model': model,
+            'color': color,
+            'actor_id': vehicle.id,
+            'actor': vehicle,
+            'cur_count': cur_timestamp
+        }})
+
+    def move_vehicle(self, veh_id, cur_timestamp, transform):
+        """
+        Move the
+
+        Parameters
+        ----------
+        veh_id : str
+            Vehicle's id
+
+        cur_timestamp : str
+            Current timestamp
+
+        transform : carla.Transform
+            Current pose/
+        """
+        # this represent this vehicle is already moved in this round
+        if self.veh_dict[veh_id]['cur_count'] == cur_timestamp:
+            return
+
+        self.veh_dict[veh_id]['actor'].set_transform(transform)
+        self.veh_dict[veh_id]['cur_count'] = cur_timestamp
+        self.veh_dict[veh_id]['cur_pose'] = transform
+
+    def close(self):
+        self.world.apply_settings(self.origin_settings)
+        actor_list = self.world.get_actors()
+        for actor in actor_list:
+            actor.destroy()
+        self.map_manager.destroy()
+        self.sensor_destory()
+
+    def sensor_destory(self):
+        for veh_id, veh_content in self.veh_dict.items():
+            if 'sensor_manager' in veh_content:
+                veh_content['sensor_manager'].destroy()
+
+    def destroy_vehicle(self, cur_timestamp):
+        """
+        This is used to destroy the vehicles that are out of any cav's scope.
+        """
+        destroy_list = []
+        for veh_id, veh_content in self.veh_dict.items():
+            if veh_content['cur_count'] != cur_timestamp:
+                veh_content['actor'].destroy()
+                destroy_list.append(veh_id)
+
+        for veh_id in destroy_list:
+            self.veh_dict.pop(veh_id)
+
+    def structure_transform_cav(self, pose):
+        """
+        Convert the pose saved in list to transform format.
+
+        Parameters
+        ----------
+        pose : list
+            x, y, z, roll, yaw, pitch
+
+        Returns
+        -------
+        carla.Transform
+        """
+        cur_pose = carla.Transform(carla.Location(x=pose[0],
+                                                  y=pose[1],
+                                                  z=pose[2]),
+                                   carla.Rotation(roll=pose[3],
+                                                  yaw=pose[4],
+                                                  pitch=pose[5]))
+
+        return cur_pose
+
+    @staticmethod
+    def structure_transform_bg_veh(location, rotation):
+        """
+        Convert the location and rotation in list to carla transform format.
+
+        Parameters
+        ----------
+        location : list
+        rotation : list
+        Returns
+        -------
+        carla.Transform
+        """
+        cur_pose = carla.Transform(carla.Location(x=location[0],
+                                                  y=location[1],
+                                                  z=location[2]),
+                                   carla.Rotation(roll=rotation[0],
+                                                  yaw=rotation[1],
+                                                  pitch=rotation[2]))
+
+        return cur_pose
+
+    @staticmethod
+    def extract_timestamps(yaml_files):
+        """
+        Given the list of the yaml files, extract the mocked timestamps.
+
+        Parameters
+        ----------
+        yaml_files : list
+            The full path of all yaml files of ego vehicle
+
+        Returns
+        -------
+        timestamps : list
+            The list containing timestamps only.
+        """
+        timestamps = []
+
+        for file in yaml_files:
+            res = file.split('/')[-1]
+
+            timestamp = res.replace('.yaml', '')
+            timestamps.append(timestamp)
+
+        return timestamps
+
+    @staticmethod
+    def set_weather(weather_settings):
+        """
+        Set CARLA weather params.
+
+        Parameters
+        ----------
+        weather_settings : dict
+            The dictionary that contains all parameters of weather.
+
+        Returns
+        -------
+        The CARLA weather setting.
+        """
+        weather = carla.WeatherParameters(
+            sun_altitude_angle=weather_settings['sun_altitude_angle'],
+            cloudiness=weather_settings['cloudiness'],
+            precipitation=weather_settings['precipitation'],
+            precipitation_deposits=weather_settings['precipitation_deposits'],
+            wind_intensity=weather_settings['wind_intensity'],
+            fog_density=weather_settings['fog_density'],
+            fog_distance=weather_settings['fog_distance'],
+            fog_falloff=weather_settings['fog_falloff'],
+            wetness=weather_settings['wetness']
+        )
+        return weather
diff --git a/opencood/logreplay/sensors/__init__.py b/opencood/logreplay/sensors/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/opencood/logreplay/sensors/base_sensor.py b/opencood/logreplay/sensors/base_sensor.py
new file mode 100644
index 00000000..bc2bb849
--- /dev/null
+++ b/opencood/logreplay/sensors/base_sensor.py
@@ -0,0 +1,22 @@
+"""
+Base Class for sensors
+"""
+import cv2
+
+
+class BaseSensor:
+    def __init__(self, agent_id, vehicle, world, config, global_position):
+        return
+
+    def visualize_data(self):
+        return
+
+    def data_dump(self, output_folder, cur_timestamp):
+        return
+
+    def tick(self):
+        return None
+
+    def destroy(self):
+        self.sensor.destroy()
+        cv2.destroyAllWindows()
diff --git a/opencood/logreplay/sensors/bev_semantic_camera.py b/opencood/logreplay/sensors/bev_semantic_camera.py
new file mode 100644
index 00000000..57ccfad4
--- /dev/null
+++ b/opencood/logreplay/sensors/bev_semantic_camera.py
@@ -0,0 +1,216 @@
+# -*- coding: utf-8 -*-
+"""
+CARLA Semantic Camera Sensor
+"""
+# Author: Runsheng Xu <rxx3386@ucla.edu>
+# License: TDG-Attribution-NonCommercial-NoDistrib
+import os.path
+import weakref
+
+import carla
+import cv2
+import numpy as np
+
+from opencood.hypes_yaml.yaml_utils import save_yaml_wo_overwriting
+from logreplay.sensors.utils import get_camera_intrinsic
+from logreplay.sensors.base_sensor import BaseSensor
+
+
+class BEVSemanticCamera(BaseSensor):
+    """
+    BEV camera for semantic segmentation.
+
+    Parameters
+    ----------
+    vehicle : carla.Vehicle
+        The carla.Vehicle, this is for cav.
+
+    world : carla.World
+        The carla world object, this is for rsu.
+
+    config : dict
+        Semantic camera configuration.
+
+    global_position : list
+        Global position of the infrastructure, [x, y, z]
+
+    Attributes
+    ----------
+    image : np.ndarray
+        Current received rgb image.
+    sensor : carla.sensor
+        The carla sensor that mounts at the vehicle.
+
+    """
+
+    def __init__(self, agent_id, vehicle, world, config, global_position):
+        super().__init__(agent_id, vehicle, world, config, global_position)
+        if vehicle is not None:
+            world = vehicle.get_world()
+
+        self.agent_id = agent_id
+        self.name = 'bev_semantic_camera'
+
+        blueprint = world.get_blueprint_library(). \
+            find('sensor.camera.semantic_segmentation')
+        blueprint.set_attribute('fov', str(config['fov']))
+        blueprint.set_attribute('image_size_x', str(config['image_size_x']))
+        blueprint.set_attribute('image_size_y', str(config['image_size_y']))
+        self.height = config['height']
+        self.visualize = config['visualize']
+
+        spawn_point = self.spawn_point_estimation(global_position)
+
+        if vehicle is not None:
+            self.sensor = world.spawn_actor(
+                blueprint, spawn_point, attach_to=vehicle)
+        else:
+            self.sensor = world.spawn_actor(blueprint, spawn_point)
+
+        self.image = None
+        self.timstamp = None
+        self.frame = 0
+        weak_self = weakref.ref(self)
+        self.sensor.listen(
+            lambda event: BEVSemanticCamera._on_rgb_image_event(
+                weak_self, event))
+
+        # camera attributes
+        self.image_width = int(self.sensor.attributes['image_size_x'])
+        self.image_height = int(self.sensor.attributes['image_size_y'])
+
+    def spawn_point_estimation(self, global_position):
+
+        pitch = -90
+        carla_location = carla.Location(x=0, y=0, z=self.height)
+
+        if global_position is not None:
+            carla_location = carla.Location(
+                x=global_position[0],
+                y=global_position[1],
+                z=self.height)
+
+        carla_rotation = carla.Rotation(roll=0, yaw=0, pitch=pitch)
+        spawn_point = carla.Transform(carla_location, carla_rotation)
+
+        return spawn_point
+
+    @staticmethod
+    def labels_to_array(bgr_image):
+        """
+        Convert an image containing CARLA semantic segmentation labels to a
+        2D array containing the label of each pixel.
+
+        Parameters
+        ----------
+        bgr_image : np.ndarray
+            BGR image
+        """
+        return bgr_image[:, :, 2]
+
+    @staticmethod
+    def labels_to_cityscapes_palette(label):
+        """
+        Convert an image containing CARLA semantic segmentation labels to
+        Cityscapes palette.
+
+        Parameters
+        ----------
+        label : np.ndarray
+           label image, (h, w)
+
+        Returns
+        -------
+        Converted BGR image.
+        """
+        classes = {
+            0: [0, 0, 0],  # None
+            1: [70, 70, 70],  # Buildings
+            2: [190, 153, 153],  # Fences
+            3: [72, 0, 90],  # Other
+            4: [220, 20, 60],  # Pedestrians
+            5: [153, 153, 153],  # Poles
+            6: [157, 234, 50],  # RoadLines
+            7: [128, 64, 128],  # Roads
+            8: [244, 35, 232],  # Sidewalks
+            9: [107, 142, 35],  # Vegetation
+            10: [0, 0, 255],  # Vehicles
+            11: [102, 102, 156],  # Walls
+            12: [220, 220, 0],  # TrafficSigns
+            13: [70, 130, 180],  # Sky
+            14: [81, 0, 81],  # Ground
+            15: [150, 100, 100],  # Bridge
+            16: [230, 150, 140],  # RailTrack
+            17: [180, 165, 180],  # All types of guard rails/crash barriers.
+            18: [250, 170, 30],  # Traffic Light
+            19: [110, 190, 160],  # Static
+            20: [170, 120, 50],  # Dynamic
+            21: [45, 60, 150],  # Water
+            22: [145, 170, 100]  # Terrain
+        }
+        result = np.zeros((label.shape[0], label.shape[1], 3), dtype=np.uint8)
+        for key, value in classes.items():
+            result[np.where(label == key)] = value
+        return result
+
+    @staticmethod
+    def _on_rgb_image_event(weak_self, event):
+        """CAMERA  method"""
+        self = weak_self()
+        if not self:
+            return
+        image = np.array(event.raw_data)
+        image = image.reshape((self.image_height, self.image_width, 4))
+        # we need to remove the alpha channel
+        image = image[:, :, :3]
+        # self.image is the dumped data
+        self.image = np.array(self.labels_to_array(image), dtype=np.int)
+        self.vis_image = \
+            cv2.cvtColor(self.labels_to_cityscapes_palette(self.image),
+                         cv2.COLOR_BGR2RGB)
+
+        self.frame = event.frame
+        self.timestamp = event.timestamp
+
+    def visualize_data(self):
+        if self.visualize:
+            while not hasattr(self, 'vis_image') or self.vis_image is None:
+                continue
+            cv2.imshow('bev seg camera agent %s' % self.agent_id,
+                       self.vis_image)
+            cv2.waitKey(1)
+
+    def data_dump(self, output_root, cur_timestamp):
+        while not hasattr(self, 'vis_image') or self.vis_image is None:
+            continue
+        # dump visualization
+        output_vis_name = os.path.join(output_root,
+                                       cur_timestamp + '_bev_sem_vis.png')
+        cv2.imwrite(output_vis_name, self.vis_image)
+
+        # dump the label
+        output_label_name = os.path.join(output_root,
+                                         cur_timestamp + '_bev_sem_label.png')
+        cv2.imwrite(output_label_name, self.image)
+
+        # dump the yaml
+        save_yaml_name = os.path.join(output_root,
+                                      cur_timestamp +
+                                      '_additional.yaml')
+        # intrinsic
+        camera_intrinsic = get_camera_intrinsic(self.sensor)
+        # pose under world coordinate system
+        camera_transformation = self.sensor.get_transform()
+        cords = [camera_transformation.location.x,
+                 camera_transformation.location.y,
+                 camera_transformation.location.z,
+                 camera_transformation.rotation.roll,
+                 camera_transformation.rotation.yaw,
+                 camera_transformation.rotation.pitch]
+
+        bev_sem_cam_info = {'bev_sem_cam':
+                                {'intrinsic': camera_intrinsic,
+                                 'cords': cords
+                                 }}
+        save_yaml_wo_overwriting(bev_sem_cam_info,
+                                 save_yaml_name)
diff --git a/opencood/logreplay/sensors/semantic_lidar.py b/opencood/logreplay/sensors/semantic_lidar.py
new file mode 100644
index 00000000..9d8c39a1
--- /dev/null
+++ b/opencood/logreplay/sensors/semantic_lidar.py
@@ -0,0 +1,140 @@
+"""
+This is mainly used to filter out objects that is not in the sight
+of cameras.
+"""
+import weakref
+
+import carla
+import cv2
+import numpy as np
+from logreplay.sensors.base_sensor import BaseSensor
+
+
+class SemanticLidar(BaseSensor):
+    def __init__(self, agent_id, vehicle, world, config, global_position):
+        super().__init__(agent_id, vehicle, world, config, global_position)
+
+        if vehicle is not None:
+            world = vehicle.get_world()
+
+        self.agent_id = agent_id
+
+        blueprint = world.get_blueprint_library(). \
+            find('sensor.lidar.ray_cast_semantic')
+        # set attribute based on the configuration
+        blueprint.set_attribute('upper_fov', str(config['upper_fov']))
+        blueprint.set_attribute('lower_fov', str(config['lower_fov']))
+        blueprint.set_attribute('channels', str(config['channels']))
+        blueprint.set_attribute('range', str(config['range']))
+        blueprint.set_attribute(
+            'points_per_second', str(
+                config['points_per_second']))
+        blueprint.set_attribute(
+            'rotation_frequency', str(
+                config['rotation_frequency']))
+
+        relative_position = config['relative_pose']
+        spawn_point = self.spawn_point_estimation(relative_position,
+                                                  global_position)
+        self.name = 'semantic_lidar' + str(relative_position)
+        self.thresh = config['thresh']
+
+        if vehicle is not None:
+            self.sensor = world.spawn_actor(
+                blueprint, spawn_point, attach_to=vehicle)
+        else:
+            self.sensor = world.spawn_actor(blueprint, spawn_point)
+
+        # lidar data
+        self.points = None
+        self.obj_idx = None
+        self.obj_tag = None
+
+        self.timestamp = None
+        self.frame = 0
+
+        weak_self = weakref.ref(self)
+        self.sensor.listen(
+            lambda event: SemanticLidar._on_data_event(
+                weak_self, event))
+
+    @staticmethod
+    def _on_data_event(weak_self, event):
+        """Semantic Lidar  method"""
+        self = weak_self()
+        if not self:
+            return
+
+        # shape:(n, 6)
+        data = np.frombuffer(event.raw_data, dtype=np.dtype([
+            ('x', np.float32), ('y', np.float32), ('z', np.float32),
+            ('CosAngle', np.float32), ('ObjIdx', np.uint32),
+            ('ObjTag', np.uint32)]))
+
+        # (x, y, z, intensity)
+        self.points = np.array([data['x'], data['y'], data['z']]).T
+        self.obj_tag = np.array(data['ObjTag'])
+        self.obj_idx = np.array(data['ObjIdx'])
+
+        self.data = data
+        self.frame = event.frame
+        self.timestamp = event.timestamp
+
+    @staticmethod
+    def spawn_point_estimation(relative_position, global_position):
+
+        pitch = 0
+        carla_location = carla.Location(x=0, y=0, z=0)
+
+        if global_position is not None:
+            carla_location = carla.Location(
+                x=global_position[0],
+                y=global_position[1],
+                z=global_position[2])
+            pitch = -35
+
+        if relative_position == 'front':
+            carla_location = carla.Location(x=carla_location.x + 2.5,
+                                            y=carla_location.y,
+                                            z=carla_location.z + 1.0)
+            yaw = 0
+
+        elif relative_position == 'right':
+            carla_location = carla.Location(x=carla_location.x + 0.0,
+                                            y=carla_location.y + 0.3,
+                                            z=carla_location.z + 1.8)
+            yaw = 100
+
+        elif relative_position == 'left':
+            carla_location = carla.Location(x=carla_location.x + 0.0,
+                                            y=carla_location.y - 0.3,
+                                            z=carla_location.z + 1.8)
+            yaw = -100
+        else:
+            carla_location = carla.Location(x=carla_location.x - 2.0,
+                                            y=carla_location.y,
+                                            z=carla_location.z + 1.5)
+            yaw = 180
+
+        carla_rotation = carla.Rotation(roll=0, yaw=yaw, pitch=pitch)
+        spawn_point = carla.Transform(carla_location, carla_rotation)
+
+        return spawn_point
+
+    def tick(self):
+        while self.obj_idx is None or self.obj_tag is None or \
+                self.obj_idx.shape[0] != self.obj_tag.shape[0]:
+            continue
+
+        # label 10 is the vehicle
+        vehicle_idx = self.obj_idx[self.obj_tag == 10]
+        # each individual instance id
+        vehicle_unique_id = list(np.unique(vehicle_idx))
+        vehicle_id_filter = []
+
+        for veh_id in vehicle_unique_id:
+            if vehicle_idx[vehicle_idx == veh_id].shape[0] > self.thresh:
+                vehicle_id_filter.append(veh_id)
+
+        # these are the ids that are visible
+        return vehicle_id_filter
diff --git a/opencood/logreplay/sensors/sensor_manager.py b/opencood/logreplay/sensors/sensor_manager.py
new file mode 100644
index 00000000..226c6eba
--- /dev/null
+++ b/opencood/logreplay/sensors/sensor_manager.py
@@ -0,0 +1,96 @@
+# -*- coding: utf-8 -*-
+"""
+Sensor Manager for each cav
+"""
+# Author: Runsheng Xu <rxx3386@ucla.edu>
+# License: TDG-Attribution-NonCommercial-NoDistrib
+import importlib
+import os
+from collections import OrderedDict
+
+
+class SensorManager:
+    """
+    The manager controls all sensor data streaming and dumping for each cav.
+    todo: rsu not considered yet.
+    Parameters
+    ----------
+    agent_id : str
+        The cav's original id.
+
+    vehicle_content : dict
+        The content of the cav.
+
+    world : carla.World
+        Carla simulation server object.
+
+    config_yaml : dict
+        Configurations for the sensor manager.
+
+    output_root : str
+        Output directory for data dumping.
+
+    Attributes
+    ----------
+    sensor_list : list
+        A list of sensors to dump/visualize data.
+    """
+    def __init__(self, agent_id,
+                 vehicle_content,
+                 world, config_yaml, output_root):
+
+        self.agent_id = agent_id
+        self.output_root = output_root
+        self.vehicle = vehicle_content['actor']
+        self.world = world
+        self.sensor_list = []
+        # this is used to gather the meta information return from sensors
+        self.sensor_meta = OrderedDict()
+
+        for sensor_content in config_yaml['sensor_list']:
+            sensor = None
+            sensor_name = sensor_content['name']
+
+            # find the relative path
+            sensor_filename = "logreplay.sensors." + sensor_name
+            sensor_lib = importlib.import_module(sensor_filename)
+            target_sensor_name = sensor_name.replace('_', '')
+
+            # the sensor corresponding class has to have the same
+            # name pattern as the file
+            for name, cls in sensor_lib.__dict__.items():
+                if name.lower() == target_sensor_name.lower():
+                    sensor = cls
+
+            assert sensor is not None, 'The sensor class name has to be the' \
+                                       'same as the file name. e.g. ' \
+                                       'bev_semantic_camera -> ' \
+                                       'BevSemanticCamera'
+            # todo: rsu is not considered yet
+            sensor_instance = sensor(self.agent_id,
+                                     self.vehicle,
+                                     self.world,
+                                     sensor_content['args'],
+                                     None)
+            self.sensor_list.append(sensor_instance)
+
+    def run_step(self, cur_timestamp):
+        for sensor_instance in self.sensor_list:
+            sensor_name = sensor_instance.name
+            sensor_instance.visualize_data()
+
+            meta_info = sensor_instance.tick()
+            self.sensor_meta.update({sensor_name: meta_info})
+
+            # for data dumping
+            output_folder = os.path.join(self.output_root,
+                                         self.agent_id)
+            if not os.path.exists(output_folder):
+                os.makedirs(output_folder)
+            sensor_instance.data_dump(output_folder,
+                                      cur_timestamp)
+
+    def destroy(self):
+        for sensor_instance in self.sensor_list:
+            sensor_instance.destroy()
+
diff --git a/opencood/logreplay/sensors/utils.py b/opencood/logreplay/sensors/utils.py
new file mode 100644
index 00000000..62250a77
--- /dev/null
+++ b/opencood/logreplay/sensors/utils.py
@@ -0,0 +1,29 @@
+import numpy as np
+
+
+def get_camera_intrinsic(sensor):
+    """
+    Retrieve the camera intrinsic matrix.
+
+    Parameters
+    ----------
+    sensor : carla.sensor
+        Carla rgb camera object.
+
+    Returns
+    -------
+    matrix_x : np.ndarray
+        The 2d intrinsic matrix.
+
+    """
+    VIEW_WIDTH = int(sensor.attributes['image_size_x'])
+    VIEW_HEIGHT = int(sensor.attributes['image_size_y'])
+    VIEW_FOV = int(float(sensor.attributes['fov']))
+
+    matrix_k = np.identity(3)
+    matrix_k[0, 2] = VIEW_WIDTH / 2.0
+    matrix_k[1, 2] = VIEW_HEIGHT / 2.0
+    matrix_k[0, 0] = matrix_k[1, 1] = VIEW_WIDTH / \
+        (2.0 * np.tan(VIEW_FOV * np.pi / 360.0))
+
+    return matrix_k
diff --git a/opencood/opencood/__init__.py b/opencood/opencood/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/opencood/opencood/data_utils/__init__.py b/opencood/opencood/data_utils/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/opencood/opencood/data_utils/augmentor/__init__.py b/opencood/opencood/data_utils/augmentor/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/opencood/opencood/data_utils/augmentor/augment_utils.py b/opencood/opencood/data_utils/augmentor/augment_utils.py
new file mode 100644
index 00000000..af12bedc
--- /dev/null
+++ b/opencood/opencood/data_utils/augmentor/augment_utils.py
@@ -0,0 +1,88 @@
+# -*- coding: utf-8 -*-
+# Author: OpenPCDet
+
+import numpy as np
+
+from opencood.utils import common_utils
+
+
+def random_flip_along_x(gt_boxes, points):
+    """
+    Args:
+        gt_boxes: (N, 7 + C), [x, y, z, dx, dy, dz, heading, [vx], [vy]]
+        points: (M, 3 + C)
+    Returns:
+    """
+    enable = np.random.choice([False, True], replace=False, p=[0.5, 0.5])
+    if enable:
+        gt_boxes[:, 1] = -gt_boxes[:, 1]
+        gt_boxes[:, 6] = -gt_boxes[:, 6]
+        points[:, 1] = -points[:, 1]
+
+        if gt_boxes.shape[1] > 7:
+            gt_boxes[:, 8] = -gt_boxes[:, 8]
+
+    return gt_boxes, points
+
+
+def random_flip_along_y(gt_boxes, points):
+    """
+    Args:
+        gt_boxes: (N, 7 + C), [x, y, z, dx, dy, dz, heading, [vx], [vy]]
+        points: (M, 3 + C)
+    Returns:
+    """
+    enable = np.random.choice([False, True], replace=False, p=[0.5, 0.5])
+    if enable:
+        gt_boxes[:, 0] = -gt_boxes[:, 0]
+        gt_boxes[:, 6] = -(gt_boxes[:, 6] + np.pi)
+        points[:, 0] = -points[:, 0]
+
+        if gt_boxes.shape[1] > 7:
+            gt_boxes[:, 7] = -gt_boxes[:, 7]
+
+    return gt_boxes, points
+
+
+def global_rotation(gt_boxes, points, rot_range):
+    """
+    Args:
+        gt_boxes: (N, 7 + C), [x, y, z, dx, dy, dz, heading, [vx], [vy]]
+        points: (M, 3 + C),
+        rot_range: [min, max]
+    Returns:
+    """
+    noise_rotation = np.random.uniform(rot_range[0],
+                                       rot_range[1])
+    points = common_utils.rotate_points_along_z(points[np.newaxis, :, :],
+                                                np.array([noise_rotation]))[0]
+
+    gt_boxes[:, 0:3] = \
+        common_utils.rotate_points_along_z(gt_boxes[np.newaxis, :, 0:3],
+                                           np.array([noise_rotation]))[0]
+    gt_boxes[:, 6] += noise_rotation
+
+    if gt_boxes.shape[1] > 7:
+        gt_boxes[:, 7:9] = common_utils.rotate_points_along_z(
+            np.hstack((gt_boxes[:, 7:9], np.zeros((gt_boxes.shape[0], 1))))[
+            np.newaxis, :, :],
+            np.array([noise_rotation]))[0][:, 0:2]
+
+    return gt_boxes, points
+
+
+def global_scaling(gt_boxes, points, scale_range):
+    """
+    Args:
+        gt_boxes: (N, 7), [x, y, z, dx, dy, dz, heading]
+        points: (M, 3 + C),
+        scale_range: [min, max]
+    Returns:
+    """
+    if scale_range[1] - scale_range[0] < 1e-3:
+        return gt_boxes, points
+    noise_scale = np.random.uniform(scale_range[0], scale_range[1])
+    points[:, :3] *= noise_scale
+    gt_boxes[:, :6] *= noise_scale
+
+    return gt_boxes, points
diff --git a/opencood/opencood/data_utils/augmentor/data_augmentor.py b/opencood/opencood/data_utils/augmentor/data_augmentor.py
new file mode 100644
index 00000000..82e5533c
--- /dev/null
+++ b/opencood/opencood/data_utils/augmentor/data_augmentor.py
@@ -0,0 +1,120 @@
+# -*- coding: utf-8 -*-
+"""
+Class for data augmentation
+"""
+# Author: Runsheng Xu <rxx3386@ucla.edu>
+# License: TDG-Attribution-NonCommercial-NoDistrib
+
+from functools import partial
+
+import numpy as np
+
+from opencood.data_utils.augmentor import augment_utils
+
+
+class DataAugmentor(object):
+    """
+    Data Augmentor.
+
+    Parameters
+    ----------
+    augment_config : list
+        A list of augmentation configuration.
+
+    Attributes
+    ----------
+    data_augmentor_queue : list
+        The list of data augmented functions.
+    """
+
+    def __init__(self, augment_config, train=True):
+        self.data_augmentor_queue = []
+        self.train = train
+
+        for cur_cfg in augment_config:
+            cur_augmentor = getattr(self, cur_cfg['NAME'])(config=cur_cfg)
+            self.data_augmentor_queue.append(cur_augmentor)
+
+    def random_world_flip(self, data_dict=None, config=None):
+        if data_dict is None:
+            return partial(self.random_world_flip, config=config)
+
+        gt_boxes, gt_mask, points = data_dict['object_bbx_center'], \
+                                    data_dict['object_bbx_mask'], \
+                                    data_dict['lidar_np']
+        gt_boxes_valid = gt_boxes[gt_mask == 1]
+
+        for cur_axis in config['ALONG_AXIS_LIST']:
+            assert cur_axis in ['x', 'y']
+            gt_boxes_valid, points = getattr(augment_utils,
+                                             'random_flip_along_%s' % cur_axis)(
+                gt_boxes_valid, points,
+            )
+
+        gt_boxes[:gt_boxes_valid.shape[0], :] = gt_boxes_valid
+
+        data_dict['object_bbx_center'] = gt_boxes
+        data_dict['object_bbx_mask'] = gt_mask
+        data_dict['lidar_np'] = points
+
+        return data_dict
+
+    def random_world_rotation(self, data_dict=None, config=None):
+        if data_dict is None:
+            return partial(self.random_world_rotation, config=config)
+
+        rot_range = config['WORLD_ROT_ANGLE']
+        if not isinstance(rot_range, list):
+            rot_range = [-rot_range, rot_range]
+
+        gt_boxes, gt_mask, points = data_dict['object_bbx_center'], \
+                                    data_dict['object_bbx_mask'], \
+                                    data_dict['lidar_np']
+        gt_boxes_valid = gt_boxes[gt_mask == 1]
+        gt_boxes_valid, points = augment_utils.global_rotation(
+            gt_boxes_valid, points, rot_range=rot_range
+        )
+        gt_boxes[:gt_boxes_valid.shape[0], :] = gt_boxes_valid
+
+        data_dict['object_bbx_center'] = gt_boxes
+        data_dict['object_bbx_mask'] = gt_mask
+        data_dict['lidar_np'] = points
+
+        return data_dict
+
+    def random_world_scaling(self, data_dict=None, config=None):
+        if data_dict is None:
+            return partial(self.random_world_scaling, config=config)
+
+        gt_boxes, gt_mask, points = data_dict['object_bbx_center'], \
+                                    data_dict['object_bbx_mask'], \
+                                    data_dict['lidar_np']
+        gt_boxes_valid = gt_boxes[gt_mask == 1]
+
+        gt_boxes_valid, points = augment_utils.global_scaling(
+            gt_boxes_valid, points, config['WORLD_SCALE_RANGE']
+        )
+        gt_boxes[:gt_boxes_valid.shape[0], :] = gt_boxes_valid
+
+        data_dict['object_bbx_center'] = gt_boxes
+        data_dict['object_bbx_mask'] = gt_mask
+        data_dict['lidar_np'] = points
+
+        return data_dict
+
+    def forward(self, data_dict):
+        """
+        Args:
+            data_dict:
+                points: (N, 3 + C_in)
+                gt_boxes: optional, (N, 7) [x, y, z, dx, dy, dz, heading]
+                gt_names: optional, (N), string
+                ...
+
+        Returns:
+        """
+        if self.train:
+            for cur_augmentor in self.data_augmentor_queue:
+                data_dict = cur_augmentor(data_dict=data_dict)
+
+        return data_dict
diff --git a/opencood/opencood/data_utils/datasets/__init__.py b/opencood/opencood/data_utils/datasets/__init__.py
new file mode 100644
index 00000000..1ec93be5
--- /dev/null
+++ b/opencood/opencood/data_utils/datasets/__init__.py
@@ -0,0 +1,37 @@
+# -*- coding: utf-8 -*-
+# Author: Runsheng Xu <rxx3386@ucla.edu>
+# License: TDG-Attribution-NonCommercial-NoDistrib
+
+from opencood.data_utils.datasets.late_fusion_dataset import LateFusionDataset
+from opencood.data_utils.datasets.early_fusion_dataset import EarlyFusionDataset
+from opencood.data_utils.datasets.intermediate_fusion_dataset import IntermediateFusionDataset
+from opencood.data_utils.datasets.intermediate_fusion_dataset_v2 import IntermediateFusionDatasetV2
+
+__all__ = {
+    'LateFusionDataset': LateFusionDataset,
+    'EarlyFusionDataset': EarlyFusionDataset,
+    'IntermediateFusionDataset': IntermediateFusionDataset,
+    'IntermediateFusionDatasetV2': IntermediateFusionDatasetV2
+}
+
+# the final range for evaluation
+GT_RANGE = [-140, -40, -3, 140, 40, 1]
+# The communication range for cavs
+COM_RANGE = 70
+
+
+def build_dataset(dataset_cfg, visualize=False, train=True):
+    dataset_name = dataset_cfg['fusion']['core_method']
+    error_message = f"{dataset_name} is not found. " \
+                    f"Please add your processor file's name in opencood/" \
+                    f"data_utils/datasets/init.py"
+    assert dataset_name in ['LateFusionDataset', 'EarlyFusionDataset',
+                            'IntermediateFusionDataset', 'IntermediateFusionDatasetV2'], error_message
+
+    dataset = __all__[dataset_name](
+        params=dataset_cfg,
+        visualize=visualize,
+        train=train
+    )
+
+    return dataset
diff --git a/opencood/opencood/data_utils/datasets/basedataset.py b/opencood/opencood/data_utils/datasets/basedataset.py
new file mode 100644
index 00000000..8b3bb0b7
--- /dev/null
+++ b/opencood/opencood/data_utils/datasets/basedataset.py
@@ -0,0 +1,619 @@
+# -*- coding: utf-8 -*-
+# Author: Runsheng Xu <rxx3386@ucla.edu>
+# License: TDG-Attribution-NonCommercial-NoDistrib
+
+"""
+Basedataset class for all kinds of fusion.
+"""
+
+import os
+import math
+from collections import OrderedDict
+
+import torch
+import numpy as np
+from torch.utils.data import Dataset
+
+import opencood.utils.pcd_utils as pcd_utils
+from opencood.data_utils.augmentor.data_augmentor import DataAugmentor
+from opencood.hypes_yaml.yaml_utils import load_yaml
+from opencood.utils.pcd_utils import downsample_lidar_minimum
+from opencood.utils.transformation_utils import x1_to_x2
+
+
+class BaseDataset(Dataset):
+    """
+    Base dataset for all kinds of fusion. Mainly used to initialize the
+    database and associate the __get_item__ index with the correct timestamp
+    and scenario.
+
+    Parameters
+    __________
+    params : dict
+        The dictionary contains all parameters for training/testing.
+
+    visualize : false
+        If set to true, the raw point cloud will be saved in the memory
+        for visualization.
+
+    Attributes
+    ----------
+    scenario_database : OrderedDict
+        A structured dictionary contains all file information.
+
+    len_record : list
+        The list to record each scenario's data length. This is used to
+        retrieve the correct index during training.
+
+    pre_processor : opencood.pre_processor
+        Used to preprocess the raw data.
+
+    post_processor : opencood.post_processor
+        Used to generate training labels and convert the model outputs to
+        bbx formats.
+
+    data_augmentor : opencood.data_augmentor
+        Used to augment data.
+
+    """
+
+    def __init__(self, params, visualize, train=True):
+        self.params = params
+        self.visualize = visualize
+        self.train = train
+
+        self.pre_processor = None
+        self.post_processor = None
+        self.data_augmentor = DataAugmentor(params['data_augment'],
+                                            train)
+
+        # if the training/testing include noisy setting
+        if 'wild_setting' in params:
+            self.seed = params['wild_setting']['seed']
+            # whether to add time delay
+            self.async_flag = params['wild_setting']['async']
+            self.async_mode = \
+                'sim' if 'async_mode' not in params['wild_setting'] \
+                    else params['wild_setting']['async_mode']
+            self.async_overhead = params['wild_setting']['async_overhead']
+
+            # localization error
+            self.loc_err_flag = params['wild_setting']['loc_err']
+            self.xyz_noise_std = params['wild_setting']['xyz_std']
+            self.ryp_noise_std = params['wild_setting']['ryp_std']
+
+            # transmission data size
+            self.data_size = \
+                params['wild_setting']['data_size'] \
+                    if 'data_size' in params['wild_setting'] else 0
+            self.transmission_speed = \
+                params['wild_setting']['transmission_speed'] \
+                    if 'transmission_speed' in params['wild_setting'] else 27
+            self.backbone_delay = \
+                params['wild_setting']['backbone_delay'] \
+                    if 'backbone_delay' in params['wild_setting'] else 0
+
+        else:
+            self.async_flag = False
+            self.async_overhead = 0  # ms
+            self.async_mode = 'sim'
+            self.loc_err_flag = False
+            self.xyz_noise_std = 0
+            self.ryp_noise_std = 0
+            self.data_size = 0  # Mb (Megabits)
+            self.transmission_speed = 27  # Mbps
+            self.backbone_delay = 0  # ms
+
+        if self.train:
+            root_dir = params['root_dir']
+        else:
+            root_dir = params['validate_dir']
+
+        if 'train_params' not in params or\
+                'max_cav' not in params['train_params']:
+            self.max_cav = 7
+        else:
+            self.max_cav = params['train_params']['max_cav']
+
+        # first load all paths of different scenarios
+        scenario_folders = sorted([os.path.join(root_dir, x)
+                                   for x in os.listdir(root_dir) if
+                                   os.path.isdir(os.path.join(root_dir, x))])
+        # Structure: {scenario_id : {cav_1 : {timestamp1 : {yaml: path,
+        # lidar: path, cameras:list of path}}}}
+        self.scenario_database = OrderedDict()
+        self.len_record = []
+
+        # loop over all scenarios
+        for (i, scenario_folder) in enumerate(scenario_folders):
+            self.scenario_database.update({i: OrderedDict()})
+
+            # at least 1 cav should show up
+            cav_list = sorted([x for x in os.listdir(scenario_folder)
+                               if os.path.isdir(
+                    os.path.join(scenario_folder, x))])
+            assert len(cav_list) > 0
+
+            # roadside unit data's id is always negative, so here we want to
+            # make sure they will be in the end of the list as they shouldn't
+            # be ego vehicle.
+            if int(cav_list[0]) < 0:
+                cav_list = cav_list[1:] + [cav_list[0]]
+
+            # loop over all CAV data
+            for (j, cav_id) in enumerate(cav_list):
+                if j > self.max_cav - 1:
+                    print('too many cavs')
+                    break
+                self.scenario_database[i][cav_id] = OrderedDict()
+
+                # save all yaml files to the dictionary
+                cav_path = os.path.join(scenario_folder, cav_id)
+
+                # use the frame number as key, the full path as the values
+                yaml_files = \
+                    sorted([os.path.join(cav_path, x)
+                            for x in os.listdir(cav_path) if
+                            x.endswith('.yaml') and 'additional' not in x])
+                timestamps = self.extract_timestamps(yaml_files)
+
+                for timestamp in timestamps:
+                    self.scenario_database[i][cav_id][timestamp] = \
+                        OrderedDict()
+
+                    yaml_file = os.path.join(cav_path,
+                                             timestamp + '.yaml')
+                    lidar_file = os.path.join(cav_path,
+                                              timestamp + '.pcd')
+                    camera_files = self.load_camera_files(cav_path, timestamp)
+
+                    self.scenario_database[i][cav_id][timestamp]['yaml'] = \
+                        yaml_file
+                    self.scenario_database[i][cav_id][timestamp]['lidar'] = \
+                        lidar_file
+                    self.scenario_database[i][cav_id][timestamp]['camera0'] = \
+                        camera_files
+                # Assume all cavs will have the same timestamps length. Thus
+                # we only need to calculate for the first vehicle in the
+                # scene.
+                if j == 0:
+                    # we regard the agent with the minimum id as the ego
+                    self.scenario_database[i][cav_id]['ego'] = True
+                    if not self.len_record:
+                        self.len_record.append(len(timestamps))
+                    else:
+                        prev_last = self.len_record[-1]
+                        self.len_record.append(prev_last + len(timestamps))
+                else:
+                    self.scenario_database[i][cav_id]['ego'] = False
+
+    def __len__(self):
+        return self.len_record[-1]
+
+    def __getitem__(self, idx):
+        """
+        Abstract method, needs to be define by the children class.
+        """
+        pass
+
+    def retrieve_base_data(self, idx, cur_ego_pose_flag=True):
+        """
+        Given the index, return the corresponding data.
+
+        Parameters
+        ----------
+        idx : int
+            Index given by dataloader.
+
+        cur_ego_pose_flag : bool
+            Indicate whether to use current timestamp ego pose to calculate
+            transformation matrix. If set to false, meaning when other cavs
+            project their LiDAR point cloud to ego, they are projecting to
+            past ego pose.
+
+        Returns
+        -------
+        data : dict
+            The dictionary contains loaded yaml params and lidar data for
+            each cav.
+        """
+        # we loop the accumulated length list to see get the scenario index
+        scenario_index = 0
+        for i, ele in enumerate(self.len_record):
+            if idx < ele:
+                scenario_index = i
+                break
+        scenario_database = self.scenario_database[scenario_index]
+
+        # check the timestamp index
+        timestamp_index = idx if scenario_index == 0 else \
+            idx - self.len_record[scenario_index - 1]
+        # retrieve the corresponding timestamp key
+        timestamp_key = self.return_timestamp_key(scenario_database,
+                                                  timestamp_index)
+        # calculate distance to ego for each cav
+        ego_cav_content = \
+            self.calc_dist_to_ego(scenario_database, timestamp_key)
+
+        data = OrderedDict()
+        # load files for all CAVs
+        for cav_id, cav_content in scenario_database.items():
+            data[cav_id] = OrderedDict()
+            data[cav_id]['ego'] = cav_content['ego']
+
+            # calculate delay for this vehicle
+            timestamp_delay = \
+                self.time_delay_calculation(cav_content['ego'])
+
+            if timestamp_index - timestamp_delay <= 0:
+                timestamp_delay = timestamp_index
+            timestamp_index_delay = max(0, timestamp_index - timestamp_delay)
+            timestamp_key_delay = self.return_timestamp_key(scenario_database,
+                                                            timestamp_index_delay)
+            # add time delay to vehicle parameters
+            data[cav_id]['time_delay'] = timestamp_delay
+            # load the corresponding data into the dictionary
+            data[cav_id]['params'] = self.reform_param(cav_content,
+                                                       ego_cav_content,
+                                                       timestamp_key,
+                                                       timestamp_key_delay,
+                                                       cur_ego_pose_flag)
+            data[cav_id]['lidar_np'] = \
+                pcd_utils.pcd_to_np(cav_content[timestamp_key_delay]['lidar'])
+        return data
+
+    @staticmethod
+    def extract_timestamps(yaml_files):
+        """
+        Given the list of the yaml files, extract the mocked timestamps.
+
+        Parameters
+        ----------
+        yaml_files : list
+            The full path of all yaml files of ego vehicle
+
+        Returns
+        -------
+        timestamps : list
+            The list containing timestamps only.
+        """
+        timestamps = []
+
+        for file in yaml_files:
+            res = file.split('/')[-1]
+
+            timestamp = res.replace('.yaml', '')
+            timestamps.append(timestamp)
+
+        return timestamps
+
+    @staticmethod
+    def return_timestamp_key(scenario_database, timestamp_index):
+        """
+        Given the timestamp index, return the correct timestamp key, e.g.
+        2 --> '000078'.
+
+        Parameters
+        ----------
+        scenario_database : OrderedDict
+            The dictionary contains all contents in the current scenario.
+
+        timestamp_index : int
+            The index for timestamp.
+
+        Returns
+        -------
+        timestamp_key : str
+            The timestamp key saved in the cav dictionary.
+        """
+        # get all timestamp keys
+        timestamp_keys = list(scenario_database.items())[0][1]
+        # retrieve the correct index
+        timestamp_key = list(timestamp_keys.items())[timestamp_index][0]
+
+        return timestamp_key
+
+    def calc_dist_to_ego(self, scenario_database, timestamp_key):
+        """
+        Calculate the distance to ego for each cav.
+        """
+        ego_lidar_pose = None
+        ego_cav_content = None
+        # Find ego pose first
+        for cav_id, cav_content in scenario_database.items():
+            if cav_content['ego']:
+                ego_cav_content = cav_content
+                ego_lidar_pose = \
+                    load_yaml(cav_content[timestamp_key]['yaml'])['lidar_pose']
+                break
+
+        assert ego_lidar_pose is not None
+
+        # calculate the distance
+        for cav_id, cav_content in scenario_database.items():
+            cur_lidar_pose = \
+                load_yaml(cav_content[timestamp_key]['yaml'])['lidar_pose']
+            distance = \
+                math.sqrt((cur_lidar_pose[0] -
+                           ego_lidar_pose[0]) ** 2 +
+                          (cur_lidar_pose[1] - ego_lidar_pose[1]) ** 2)
+            cav_content['distance_to_ego'] = distance
+            scenario_database.update({cav_id: cav_content})
+
+        return ego_cav_content
+
+    def time_delay_calculation(self, ego_flag):
+        """
+        Calculate the time delay for a certain vehicle.
+
+        Parameters
+        ----------
+        ego_flag : boolean
+            Whether the current cav is ego.
+
+        Return
+        ------
+        time_delay : int
+            The time delay quantization.
+        """
+        # there is not time delay for ego vehicle
+        if ego_flag:
+            return 0
+        # time delay real mode
+        if self.async_mode == 'real':
+            # in the real mode, time delay = systematic async time + data
+            # transmission time + backbone computation time
+            overhead_noise = np.random.uniform(0, self.async_overhead)
+            tc = self.data_size / self.transmission_speed * 1000
+            time_delay = int(overhead_noise + tc + self.backbone_delay)
+        elif self.async_mode == 'sim':
+            # in the simulation mode, the time delay is constant
+            time_delay = np.abs(self.async_overhead)
+
+        # the data is 10 hz for both opv2v and v2x-set
+        # todo: it may not be true for other dataset like DAIR-V2X and V2X-Sim
+        time_delay = time_delay // 100
+        return time_delay if self.async_flag else 0
+
+    def add_loc_noise(self, pose, xyz_std, ryp_std):
+        """
+        Add localization noise to the pose.
+
+        Parameters
+        ----------
+        pose : list
+            x,y,z,roll,yaw,pitch
+
+        xyz_std : float
+            std of the gaussian noise on xyz
+
+        ryp_std : float
+            std of the gaussian noise
+        """
+        np.random.seed(self.seed)
+        xyz_noise = np.random.normal(0, xyz_std, 3)
+        ryp_std = np.random.normal(0, ryp_std, 3)
+        noise_pose = [pose[0] + xyz_noise[0],
+                      pose[1] + xyz_noise[1],
+                      pose[2] + xyz_noise[2],
+                      pose[3],
+                      pose[4] + ryp_std[1],
+                      pose[5]]
+        return noise_pose
+
+    def reform_param(self, cav_content, ego_content, timestamp_cur,
+                     timestamp_delay, cur_ego_pose_flag):
+        """
+        Reform the data params with current timestamp object groundtruth and
+        delay timestamp LiDAR pose for other CAVs.
+
+        Parameters
+        ----------
+        cav_content : dict
+            Dictionary that contains all file paths in the current cav/rsu.
+
+        ego_content : dict
+            Ego vehicle content.
+
+        timestamp_cur : str
+            The current timestamp.
+
+        timestamp_delay : str
+            The delayed timestamp.
+
+        cur_ego_pose_flag : bool
+            Whether use current ego pose to calculate transformation matrix.
+
+        Return
+        ------
+        The merged parameters.
+        """
+        cur_params = load_yaml(cav_content[timestamp_cur]['yaml'])
+        delay_params = load_yaml(cav_content[timestamp_delay]['yaml'])
+
+        cur_ego_params = load_yaml(ego_content[timestamp_cur]['yaml'])
+        delay_ego_params = load_yaml(ego_content[timestamp_delay]['yaml'])
+
+        # we need to calculate the transformation matrix from cav to ego
+        # at the delayed timestamp
+        delay_cav_lidar_pose = delay_params['lidar_pose']
+        delay_ego_lidar_pose = delay_ego_params["lidar_pose"]
+
+        cur_ego_lidar_pose = cur_ego_params['lidar_pose']
+        cur_cav_lidar_pose = cur_params['lidar_pose']
+
+        if not cav_content['ego'] and self.loc_err_flag:
+            delay_cav_lidar_pose = self.add_loc_noise(delay_cav_lidar_pose,
+                                                      self.xyz_noise_std,
+                                                      self.ryp_noise_std)
+            cur_cav_lidar_pose = self.add_loc_noise(cur_cav_lidar_pose,
+                                                    self.xyz_noise_std,
+                                                    self.ryp_noise_std)
+
+        if cur_ego_pose_flag:
+            transformation_matrix = x1_to_x2(delay_cav_lidar_pose,
+                                             cur_ego_lidar_pose)
+            spatial_correction_matrix = np.eye(4)
+        else:
+            transformation_matrix = x1_to_x2(delay_cav_lidar_pose,
+                                             delay_ego_lidar_pose)
+            spatial_correction_matrix = x1_to_x2(delay_ego_lidar_pose,
+                                                 cur_ego_lidar_pose)
+        # This is only used for late fusion, as it did the transformation
+        # in the postprocess, so we want the gt object transformation use
+        # the correct one
+        gt_transformation_matrix = x1_to_x2(cur_cav_lidar_pose,
+                                            cur_ego_lidar_pose)
+
+        # we always use current timestamp's gt bbx to gain a fair evaluation
+        delay_params['vehicles'] = cur_params['vehicles']
+        delay_params['transformation_matrix'] = transformation_matrix
+        delay_params['gt_transformation_matrix'] = \
+            gt_transformation_matrix
+        delay_params['spatial_correction_matrix'] = spatial_correction_matrix
+
+        return delay_params
+
+    @staticmethod
+    def load_camera_files(cav_path, timestamp):
+        """
+        Retrieve the paths to all camera files.
+
+        Parameters
+        ----------
+        cav_path : str
+            The full file path of current cav.
+
+        timestamp : str
+            Current timestamp
+
+        Returns
+        -------
+        camera_files : list
+            The list containing all camera png file paths.
+        """
+        camera0_file = os.path.join(cav_path,
+                                    timestamp + '_camera0.png')
+        camera1_file = os.path.join(cav_path,
+                                    timestamp + '_camera1.png')
+        camera2_file = os.path.join(cav_path,
+                                    timestamp + '_camera2.png')
+        camera3_file = os.path.join(cav_path,
+                                    timestamp + '_camera3.png')
+        return [camera0_file, camera1_file, camera2_file, camera3_file]
+
+    def project_points_to_bev_map(self, points, ratio=0.1):
+        """
+        Project points to BEV occupancy map with default ratio=0.1.
+
+        Parameters
+        ----------
+        points : np.ndarray
+            (N, 3) / (N, 4)
+
+        ratio : float
+            Discretization parameters. Default is 0.1.
+
+        Returns
+        -------
+        bev_map : np.ndarray
+            BEV occupancy map including projected points
+            with shape (img_row, img_col).
+
+        """
+        return self.pre_processor.project_points_to_bev_map(points, ratio)
+
+    def augment(self, lidar_np, object_bbx_center, object_bbx_mask):
+        """
+        Given the raw point cloud, augment by flipping and rotation.
+
+        Parameters
+        ----------
+        lidar_np : np.ndarray
+            (n, 4) shape
+
+        object_bbx_center : np.ndarray
+            (n, 7) shape to represent bbx's x, y, z, h, w, l, yaw
+
+        object_bbx_mask : np.ndarray
+            Indicate which elements in object_bbx_center are padded.
+        """
+        tmp_dict = {'lidar_np': lidar_np,
+                    'object_bbx_center': object_bbx_center,
+                    'object_bbx_mask': object_bbx_mask}
+        tmp_dict = self.data_augmentor.forward(tmp_dict)
+
+        lidar_np = tmp_dict['lidar_np']
+        object_bbx_center = tmp_dict['object_bbx_center']
+        object_bbx_mask = tmp_dict['object_bbx_mask']
+
+        return lidar_np, object_bbx_center, object_bbx_mask
+
+    def collate_batch_train(self, batch):
+        """
+        Customized collate function for pytorch dataloader during training
+        for early and late fusion dataset.
+
+        Parameters
+        ----------
+        batch : dict
+
+        Returns
+        -------
+        batch : dict
+            Reformatted batch.
+        """
+        # during training, we only care about ego.
+        output_dict = {'ego': {}}
+
+        object_bbx_center = []
+        object_bbx_mask = []
+        processed_lidar_list = []
+        label_dict_list = []
+
+        if self.visualize:
+            origin_lidar = []
+
+        for i in range(len(batch)):
+            ego_dict = batch[i]['ego']
+            object_bbx_center.append(ego_dict['object_bbx_center'])
+            object_bbx_mask.append(ego_dict['object_bbx_mask'])
+            processed_lidar_list.append(ego_dict['processed_lidar'])
+            label_dict_list.append(ego_dict['label_dict'])
+
+            if self.visualize:
+                origin_lidar.append(ego_dict['origin_lidar'])
+
+        # convert to numpy, (B, max_num, 7)
+        object_bbx_center = torch.from_numpy(np.array(object_bbx_center))
+        object_bbx_mask = torch.from_numpy(np.array(object_bbx_mask))
+
+        processed_lidar_torch_dict = \
+            self.pre_processor.collate_batch(processed_lidar_list)
+        label_torch_dict = \
+            self.post_processor.collate_batch(label_dict_list)
+        output_dict['ego'].update({'object_bbx_center': object_bbx_center,
+                                   'object_bbx_mask': object_bbx_mask,
+                                   'processed_lidar': processed_lidar_torch_dict,
+                                   'label_dict': label_torch_dict})
+        if self.visualize:
+            origin_lidar = \
+                np.array(downsample_lidar_minimum(pcd_np_list=origin_lidar))
+            origin_lidar = torch.from_numpy(origin_lidar)
+            output_dict['ego'].update({'origin_lidar': origin_lidar})
+
+        return output_dict
+
+    def visualize_result(self, pred_box_tensor,
+                         gt_tensor,
+                         pcd,
+                         show_vis,
+                         save_path,
+                         dataset=None):
+        # visualize the model output
+        self.post_processor.visualize(pred_box_tensor,
+                                      gt_tensor,
+                                      pcd,
+                                      show_vis,
+                                      save_path,
+                                      dataset=dataset)
diff --git a/opencood/opencood/data_utils/datasets/early_fusion_dataset.py b/opencood/opencood/data_utils/datasets/early_fusion_dataset.py
new file mode 100644
index 00000000..8c768baf
--- /dev/null
+++ b/opencood/opencood/data_utils/datasets/early_fusion_dataset.py
@@ -0,0 +1,394 @@
+# -*- coding: utf-8 -*-
+# Author: Runsheng Xu <rxx3386@ucla.edu>
+# License: TDG-Attribution-NonCommercial-NoDistrib
+
+"""
+Dataset class for early fusion
+"""
+import random
+import math
+from collections import OrderedDict
+
+import numpy as np
+import torch
+
+import opencood.data_utils.datasets
+from opencood.utils import box_utils
+from opencood.data_utils.post_processor import build_postprocessor
+from opencood.data_utils.datasets import basedataset
+from opencood.data_utils.pre_processor import build_preprocessor
+from opencood.utils.pcd_utils import \
+    mask_points_by_range, mask_ego_points, shuffle_points, \
+    downsample_lidar_minimum
+from opencood.utils.transformation_utils import x1_to_x2
+
+
+class EarlyFusionDataset(basedataset.BaseDataset):
+    """
+    This dataset is used for early fusion, where each CAV transmit the raw
+    point cloud to the ego vehicle.
+    """
+    def __init__(self, params, visualize, train=True):
+        super(EarlyFusionDataset, self).__init__(params, visualize, train)
+        self.pre_processor = build_preprocessor(params['preprocess'],
+                                                train)
+        self.post_processor = build_postprocessor(params['postprocess'], train)
+
+    def __getitem__(self, idx):
+        base_data_dict = self.retrieve_base_data(idx)
+
+        processed_data_dict = OrderedDict()
+        processed_data_dict['ego'] = {}
+
+        ego_id = -1
+        ego_lidar_pose = []
+
+        # first find the ego vehicle's lidar pose
+        for cav_id, cav_content in base_data_dict.items():
+            if cav_content['ego']:
+                ego_id = cav_id
+                ego_lidar_pose = cav_content['params']['lidar_pose']
+                break
+
+        assert ego_id != -1
+        assert len(ego_lidar_pose) > 0
+
+        projected_lidar_stack = []
+        object_stack = []
+        object_id_stack = []
+
+        # loop over all CAVs to process information
+        for cav_id, selected_cav_base in base_data_dict.items():
+            # check if the cav is within the communication range with ego
+            distance = \
+                math.sqrt((selected_cav_base['params']['lidar_pose'][0] -
+                           ego_lidar_pose[0]) ** 2 + (
+                                  selected_cav_base['params'][
+                                      'lidar_pose'][1] - ego_lidar_pose[
+                                      1]) ** 2)
+            if distance > opencood.data_utils.datasets.COM_RANGE:
+                continue
+
+            selected_cav_processed = self.get_item_single_car(
+                selected_cav_base,
+                ego_lidar_pose)
+            # all these lidar and object coordinates are projected to ego
+            # already.
+            projected_lidar_stack.append(
+                selected_cav_processed['projected_lidar'])
+            object_stack.append(selected_cav_processed['object_bbx_center'])
+            object_id_stack += selected_cav_processed['object_ids']
+
+        # exclude all repetitive objects
+        unique_indices = \
+            [object_id_stack.index(x) for x in set(object_id_stack)]
+        object_stack = np.vstack(object_stack)
+        object_stack = object_stack[unique_indices]
+
+        # make sure bounding boxes across all frames have the same number
+        object_bbx_center = \
+            np.zeros((self.params['postprocess']['max_num'], 7))
+        mask = np.zeros(self.params['postprocess']['max_num'])
+        object_bbx_center[:object_stack.shape[0], :] = object_stack
+        mask[:object_stack.shape[0]] = 1
+
+        # convert list to numpy array, (N, 4)
+        projected_lidar_stack = np.vstack(projected_lidar_stack)
+
+        # data augmentation
+        projected_lidar_stack, object_bbx_center, mask = \
+            self.augment(projected_lidar_stack, object_bbx_center, mask)
+
+        # we do lidar filtering in the stacked lidar
+        projected_lidar_stack = mask_points_by_range(projected_lidar_stack,
+                                                     self.params['preprocess'][
+                                                         'cav_lidar_range'])
+        # augmentation may remove some of the bbx out of range
+        object_bbx_center_valid = object_bbx_center[mask == 1]
+        object_bbx_center_valid, range_mask = \
+            box_utils.mask_boxes_outside_range_numpy(object_bbx_center_valid,
+                                                     self.params['preprocess'][
+                                                         'cav_lidar_range'],
+                                                     self.params['postprocess'][
+                                                         'order'],
+                                                     return_mask=True
+                                                     )
+        mask[object_bbx_center_valid.shape[0]:] = 0
+        object_bbx_center[:object_bbx_center_valid.shape[0]] = \
+            object_bbx_center_valid
+        object_bbx_center[object_bbx_center_valid.shape[0]:] = 0
+        unique_indices = list(np.array(unique_indices)[range_mask])
+
+        # pre-process the lidar to voxel/bev/downsampled lidar
+        lidar_dict = self.pre_processor.preprocess(projected_lidar_stack)
+
+        # generate the anchor boxes
+        anchor_box = self.post_processor.generate_anchor_box()
+
+        # generate targets label
+        label_dict = \
+            self.post_processor.generate_label(
+                gt_box_center=object_bbx_center,
+                anchors=anchor_box,
+                mask=mask)
+
+        processed_data_dict['ego'].update(
+            {'object_bbx_center': object_bbx_center,
+             'object_bbx_mask': mask,
+             'object_ids': [object_id_stack[i] for i in unique_indices],
+             'anchor_box': anchor_box,
+             'processed_lidar': lidar_dict,
+             'label_dict': label_dict})
+
+        if self.visualize:
+            processed_data_dict['ego'].update({'origin_lidar':
+                                                   projected_lidar_stack})
+
+        return processed_data_dict
+
+    def get_item_test(self, base_data_dict):
+        processed_data_dict = OrderedDict()
+        processed_data_dict['ego'] = {}
+
+        ego_id = -1
+        ego_lidar_pose = []
+
+        # first find the ego vehicle's lidar pose
+        for cav_id, cav_content in base_data_dict.items():
+            if cav_content['ego']:
+                ego_id = cav_id
+                ego_lidar_pose = cav_content['params']['lidar_pose']
+                break
+
+        assert ego_id != -1
+        assert len(ego_lidar_pose) > 0
+
+        projected_lidar_stack = []
+        object_stack = []
+        object_id_stack = []
+
+        # loop over all CAVs to process information
+        for cav_id, selected_cav_base in base_data_dict.items():
+            # check if the cav is within the communication range with ego
+            distance = \
+                math.sqrt((selected_cav_base['params']['lidar_pose'][0] -
+                           ego_lidar_pose[0]) ** 2 + (
+                                  selected_cav_base['params'][
+                                      'lidar_pose'][1] - ego_lidar_pose[
+                                      1]) ** 2)
+            if distance > opencood.data_utils.datasets.COM_RANGE:
+                continue
+
+            selected_cav_processed = self.get_item_single_car(
+                selected_cav_base,
+                ego_lidar_pose)
+            # all these lidar and object coordinates are projected to ego
+            # already.
+            projected_lidar_stack.append(
+                selected_cav_processed['projected_lidar'])
+            object_stack.append(selected_cav_processed['object_bbx_center'])
+            object_id_stack += selected_cav_processed['object_ids']
+
+        # exclude all repetitive objects
+        unique_indices = \
+            [object_id_stack.index(x) for x in set(object_id_stack)]
+        object_stack = np.vstack(object_stack)
+        object_stack = object_stack[unique_indices]
+
+        # make sure bounding boxes across all frames have the same number
+        object_bbx_center = \
+            np.zeros((self.params['postprocess']['max_num'], 7))
+        mask = np.zeros(self.params['postprocess']['max_num'])
+        object_bbx_center[:object_stack.shape[0], :] = object_stack
+        mask[:object_stack.shape[0]] = 1
+
+        # convert list to numpy array, (N, 4)
+        projected_lidar_stack = np.vstack(projected_lidar_stack)
+
+        # data augmentation
+        projected_lidar_stack, object_bbx_center, mask = \
+            self.augment(projected_lidar_stack, object_bbx_center, mask)
+
+        # we do lidar filtering in the stacked lidar
+        projected_lidar_stack = mask_points_by_range(projected_lidar_stack,
+                                                     self.params['preprocess'][
+                                                         'cav_lidar_range'])
+        # augmentation may remove some bbx out of range
+        object_bbx_center_valid = object_bbx_center[mask == 1]
+        object_bbx_center_valid, range_mask = \
+            box_utils.mask_boxes_outside_range_numpy(object_bbx_center_valid,
+                                                     self.params['preprocess'][
+                                                         'cav_lidar_range'],
+                                                     self.params['postprocess'][
+                                                         'order'],
+                                                     return_mask=True
+                                                     )
+        mask[object_bbx_center_valid.shape[0]:] = 0
+        object_bbx_center[:object_bbx_center_valid.shape[0]] = \
+            object_bbx_center_valid
+        object_bbx_center[object_bbx_center_valid.shape[0]:] = 0
+        unique_indices = list(np.array(unique_indices)[range_mask])
+
+        # pre-process the lidar to voxel/bev/downsampled lidar
+        lidar_dict = self.pre_processor.preprocess(projected_lidar_stack)
+
+        # generate the anchor boxes
+        anchor_box = self.post_processor.generate_anchor_box()
+
+        # generate targets label
+        label_dict = \
+            self.post_processor.generate_label(
+                gt_box_center=object_bbx_center,
+                anchors=anchor_box,
+                mask=mask)
+
+        processed_data_dict['ego'].update(
+            {'object_bbx_center': object_bbx_center,
+             'object_bbx_mask': mask,
+             'object_ids': [object_id_stack[i] for i in unique_indices],
+             'anchor_box': anchor_box,
+             'processed_lidar': lidar_dict,
+             'label_dict': label_dict})
+
+        if self.visualize:
+            processed_data_dict['ego'].update({'origin_lidar':
+                                                   projected_lidar_stack})
+
+        return processed_data_dict
+
+    def get_item_single_car(self, selected_cav_base, ego_pose):
+        """
+        Project the lidar and bbx to ego space first, and then do clipping.
+
+        Parameters
+        ----------
+        selected_cav_base : dict
+            The dictionary contains a single CAV's raw information.
+        ego_pose : list
+            The ego vehicle lidar pose under world coordinate.
+
+        Returns
+        -------
+        selected_cav_processed : dict
+            The dictionary contains the cav's processed information.
+        """
+        selected_cav_processed = {}
+
+        # calculate the transformation matrix
+        transformation_matrix = \
+            x1_to_x2(selected_cav_base['params']['lidar_pose'],
+                     ego_pose)
+
+        # retrieve objects under ego coordinates
+        object_bbx_center, object_bbx_mask, object_ids = \
+            self.post_processor.generate_object_center([selected_cav_base],
+                                                       ego_pose)
+
+        # filter lidar
+        lidar_np = selected_cav_base['lidar_np']
+        lidar_np = shuffle_points(lidar_np)
+        # remove points that hit itself
+        lidar_np = mask_ego_points(lidar_np)
+        # project the lidar to ego space
+        lidar_np[:, :3] = \
+            box_utils.project_points_by_matrix_torch(lidar_np[:, :3],
+                                                     transformation_matrix)
+
+        selected_cav_processed.update(
+            {'object_bbx_center': object_bbx_center[object_bbx_mask == 1],
+             'object_ids': object_ids,
+             'projected_lidar': lidar_np})
+
+        return selected_cav_processed
+
+    def collate_batch_test(self, batch):
+        """
+        Customized collate function for pytorch dataloader during testing
+        for late fusion dataset.
+
+        Parameters
+        ----------
+        batch : dict
+
+        Returns
+        -------
+        batch : dict
+            Reformatted batch.
+        """
+        # currently, we only support batch size of 1 during testing
+        assert len(batch) <= 1, "Batch size 1 is required during testing!"
+        batch = batch[0]
+
+        output_dict = {}
+
+        for cav_id, cav_content in batch.items():
+            output_dict.update({cav_id: {}})
+            # shape: (1, max_num, 7)
+            object_bbx_center = \
+                torch.from_numpy(np.array([cav_content['object_bbx_center']]))
+            object_bbx_mask = \
+                torch.from_numpy(np.array([cav_content['object_bbx_mask']]))
+            object_ids = cav_content['object_ids']
+
+            # the anchor box is the same for all bounding boxes usually, thus
+            # we don't need the batch dimension.
+            if cav_content['anchor_box'] is not None:
+                output_dict[cav_id].update({'anchor_box':
+                    torch.from_numpy(np.array(
+                        cav_content[
+                            'anchor_box']))})
+            if self.visualize:
+                origin_lidar = [cav_content['origin_lidar']]
+
+            # processed lidar dictionary
+            processed_lidar_torch_dict = \
+                self.pre_processor.collate_batch(
+                    [cav_content['processed_lidar']])
+            # label dictionary
+            label_torch_dict = \
+                self.post_processor.collate_batch([cav_content['label_dict']])
+
+            # save the transformation matrix (4, 4) to ego vehicle
+            transformation_matrix_torch = \
+                torch.from_numpy(np.identity(4)).float()
+
+            output_dict[cav_id].update({'object_bbx_center': object_bbx_center,
+                                        'object_bbx_mask': object_bbx_mask,
+                                        'processed_lidar': processed_lidar_torch_dict,
+                                        'label_dict': label_torch_dict,
+                                        'object_ids': object_ids,
+                                        'transformation_matrix': transformation_matrix_torch})
+
+            if self.visualize:
+                origin_lidar = \
+                    np.array(
+                        downsample_lidar_minimum(pcd_np_list=origin_lidar))
+                origin_lidar = torch.from_numpy(origin_lidar)
+                output_dict[cav_id].update({'origin_lidar': origin_lidar})
+
+        return output_dict
+
+    def post_process(self, data_dict, output_dict):
+        """
+        Process the outputs of the model to 2D/3D bounding box.
+
+        Parameters
+        ----------
+        data_dict : dict
+            The dictionary containing the origin input data of model.
+
+        output_dict :dict
+            The dictionary containing the output of the model.
+
+        Returns
+        -------
+        pred_box_tensor : torch.Tensor
+            The tensor of prediction bounding box after NMS.
+        gt_box_tensor : torch.Tensor
+            The tensor of gt bounding box.
+        """
+        pred_box_tensor, pred_score = \
+            self.post_processor.post_process(data_dict, output_dict)
+        gt_box_tensor = self.post_processor.generate_gt_bbx(data_dict)
+
+        return pred_box_tensor, pred_score, gt_box_tensor
diff --git a/opencood/opencood/data_utils/datasets/early_fusion_vis_dataset.py b/opencood/opencood/data_utils/datasets/early_fusion_vis_dataset.py
new file mode 100644
index 00000000..1e7accf6
--- /dev/null
+++ b/opencood/opencood/data_utils/datasets/early_fusion_vis_dataset.py
@@ -0,0 +1,198 @@
+# -*- coding: utf-8 -*-
+# Author: Runsheng Xu <rxx3386@ucla.edu>
+# License: TDG-Attribution-NonCommercial-NoDistrib
+
+"""
+This is a dataset for early fusion visualization only.
+"""
+import random
+from collections import OrderedDict
+
+import numpy as np
+import torch
+from torch.utils.data import DataLoader
+
+from opencood.utils import box_utils
+from opencood.data_utils.post_processor import build_postprocessor
+from opencood.data_utils.datasets import basedataset
+from opencood.data_utils.pre_processor import build_preprocessor
+from opencood.hypes_yaml.yaml_utils import load_yaml
+from opencood.utils.pcd_utils import \
+    mask_points_by_range, mask_ego_points, shuffle_points, \
+    downsample_lidar_minimum
+from opencood.utils.transformation_utils import x1_to_x2
+
+
+class EarlyFusionVisDataset(basedataset.BaseDataset):
+    def __init__(self, params, visualize, train=True):
+        super(EarlyFusionVisDataset, self).__init__(params, visualize, train)
+        self.pre_processor = build_preprocessor(params['preprocess'],
+                                                train)
+        self.post_processor = build_postprocessor(params['postprocess'], train)
+
+    def __getitem__(self, idx):
+        base_data_dict = self.retrieve_base_data(idx)
+
+        processed_data_dict = OrderedDict()
+        processed_data_dict['ego'] = {}
+
+        ego_id = -1
+        ego_lidar_pose = []
+
+        # first find the ego vehicle's lidar pose
+        for cav_id, cav_content in base_data_dict.items():
+            if cav_content['ego']:
+                ego_id = cav_id
+                ego_lidar_pose = cav_content['params']['lidar_pose']
+                break
+
+        assert ego_id != -1
+        assert len(ego_lidar_pose) > 0
+
+        projected_lidar_stack = []
+        object_stack = []
+        object_id_stack = []
+
+        # loop over all CAVs to process information
+        for cav_id, selected_cav_base in base_data_dict.items():
+            selected_cav_processed = self.get_item_single_car(
+                selected_cav_base,
+                ego_lidar_pose)
+            # all these lidar and object coordinates are projected to ego
+            # already.
+            projected_lidar_stack.append(
+                selected_cav_processed['projected_lidar'])
+            object_stack.append(selected_cav_processed['object_bbx_center'])
+            object_id_stack += selected_cav_processed['object_ids']
+
+        # exclude all repetitive objects
+        unique_indices = \
+            [object_id_stack.index(x) for x in set(object_id_stack)]
+        object_stack = np.vstack(object_stack)
+        object_stack = object_stack[unique_indices]
+
+        # make sure bounding boxes across all frames have the same number
+        object_bbx_center = \
+            np.zeros((self.params['postprocess']['max_num'], 7))
+        mask = np.zeros(self.params['postprocess']['max_num'])
+        object_bbx_center[:object_stack.shape[0], :] = object_stack
+        mask[:object_stack.shape[0]] = 1
+
+        # convert list to numpy array, (N, 4)
+        projected_lidar_stack = np.vstack(projected_lidar_stack)
+
+        # data augmentation
+        projected_lidar_stack, object_bbx_center, mask = \
+            self.augment(projected_lidar_stack, object_bbx_center, mask)
+
+        # we do lidar filtering in the stacked lidar
+        projected_lidar_stack = mask_points_by_range(projected_lidar_stack,
+                                                     self.params['preprocess'][
+                                                         'cav_lidar_range'])
+        # augmentation may remove some of the bbx out of range
+        object_bbx_center_valid = object_bbx_center[mask == 1]
+        object_bbx_center_valid = \
+            box_utils.mask_boxes_outside_range_numpy(object_bbx_center_valid,
+                                                     self.params['preprocess'][
+                                                         'cav_lidar_range'],
+                                                     self.params['postprocess'][
+                                                         'order']
+                                                     )
+        mask[object_bbx_center_valid.shape[0]:] = 0
+        object_bbx_center[:object_bbx_center_valid.shape[0]] = \
+            object_bbx_center_valid
+        object_bbx_center[object_bbx_center_valid.shape[0]:] = 0
+
+        processed_data_dict['ego'].update(
+            {'object_bbx_center': object_bbx_center,
+             'object_bbx_mask': mask,
+             'object_ids': [object_id_stack[i] for i in unique_indices],
+             'origin_lidar': projected_lidar_stack
+             })
+
+        return processed_data_dict
+
+    def get_item_single_car(self, selected_cav_base, ego_pose):
+        """
+        Project the lidar and bbx to ego space first, and then do clipping.
+
+        Parameters
+        ----------
+        selected_cav_base : dict
+            The dictionary contains a single CAV's raw information.
+        ego_pose : list
+            The ego vehicle lidar pose under world coordinate.
+
+        Returns
+        -------
+        selected_cav_processed : dict
+            The dictionary contains the cav's processed information.
+        """
+        selected_cav_processed = {}
+
+        # calculate the transformation matrix
+        transformation_matrix = \
+            x1_to_x2(selected_cav_base['params']['lidar_pose'],
+                     ego_pose)
+
+        # retrieve objects under ego coordinates
+        object_bbx_center, object_bbx_mask, object_ids = \
+            self.post_processor.generate_object_center([selected_cav_base],
+                                                       ego_pose)
+
+        # filter lidar
+        lidar_np = selected_cav_base['lidar_np']
+        lidar_np = shuffle_points(lidar_np)
+        # remove points that hit itself
+        lidar_np = mask_ego_points(lidar_np)
+        # project the lidar to ego space
+        lidar_np[:, :3] = \
+            box_utils.project_points_by_matrix_torch(lidar_np[:, :3],
+                                                     transformation_matrix)
+
+        selected_cav_processed.update(
+            {'object_bbx_center': object_bbx_center[object_bbx_mask == 1],
+             'object_ids': object_ids,
+             'projected_lidar': lidar_np})
+
+        return selected_cav_processed
+
+    def collate_batch_train(self, batch):
+        """
+        Customized collate function for pytorch dataloader during training
+        for late fusion dataset.
+
+        Parameters
+        ----------
+        batch : dict
+
+        Returns
+        -------
+        batch : dict
+            Reformatted batch.
+        """
+        # during training, we only care about ego.
+        output_dict = {'ego': {}}
+
+        object_bbx_center = []
+        object_bbx_mask = []
+        origin_lidar = []
+
+        for i in range(len(batch)):
+            ego_dict = batch[i]['ego']
+            object_bbx_center.append(ego_dict['object_bbx_center'])
+            object_bbx_mask.append(ego_dict['object_bbx_mask'])
+            origin_lidar.append(ego_dict['origin_lidar'])
+
+        # convert to numpy, (B, max_num, 7)
+        object_bbx_center = torch.from_numpy(np.array(object_bbx_center))
+        object_bbx_mask = torch.from_numpy(np.array(object_bbx_mask))
+        output_dict['ego'].update({'object_bbx_center': object_bbx_center,
+                                   'object_bbx_mask': object_bbx_mask})
+
+        origin_lidar = \
+            np.array(downsample_lidar_minimum(pcd_np_list=origin_lidar))
+        origin_lidar = torch.from_numpy(origin_lidar)
+        output_dict['ego'].update({'origin_lidar': origin_lidar})
+
+        return output_dict
diff --git a/opencood/opencood/data_utils/datasets/intermediate_fusion_dataset.py b/opencood/opencood/data_utils/datasets/intermediate_fusion_dataset.py
new file mode 100644
index 00000000..d7dc69ed
--- /dev/null
+++ b/opencood/opencood/data_utils/datasets/intermediate_fusion_dataset.py
@@ -0,0 +1,585 @@
+# -*- coding: utf-8 -*-
+# Author: Runsheng Xu <rxx3386@ucla.edu>
+# License: TDG-Attribution-NonCommercial-NoDistrib
+
+"""
+Dataset class for intermediate fusion
+"""
+import random
+import math
+import warnings
+from collections import OrderedDict
+
+import numpy as np
+import torch
+
+import opencood.data_utils.datasets
+import opencood.data_utils.post_processor as post_processor
+from opencood.utils import box_utils
+from opencood.data_utils.datasets import basedataset
+from opencood.data_utils.pre_processor import build_preprocessor
+from opencood.utils.pcd_utils import \
+    mask_points_by_range, mask_ego_points, shuffle_points, \
+    downsample_lidar_minimum
+from opencood.utils.transformation_utils import x1_to_x2
+
+
+class IntermediateFusionDataset(basedataset.BaseDataset):
+    """
+    This class is for intermediate fusion where each vehicle transmit the
+    deep features to ego.
+    """
+    def __init__(self, params, visualize, train=True):
+        super(IntermediateFusionDataset, self). \
+            __init__(params, visualize, train)
+
+        # if project first, cav's lidar will first be projected to
+        # the ego's coordinate frame. otherwise, the feature will be
+        # projected instead.
+        self.proj_first = True
+        if 'proj_first' in params['fusion']['args'] and \
+            not params['fusion']['args']['proj_first']:
+            self.proj_first = False
+
+        # whether there is a time delay between the time that cav project
+        # lidar to ego and the ego receive the delivered feature
+        self.cur_ego_pose_flag = True if 'cur_ego_pose_flag' not in \
+            params['fusion']['args'] else \
+            params['fusion']['args']['cur_ego_pose_flag']
+
+        self.pre_processor = build_preprocessor(params['preprocess'],
+                                                train)
+        self.post_processor = post_processor.build_postprocessor(
+            params['postprocess'],
+            train)
+
+    def __getitem__(self, idx):
+        base_data_dict = self.retrieve_base_data(idx,
+                                                 cur_ego_pose_flag=self.cur_ego_pose_flag)
+
+        processed_data_dict = OrderedDict()
+        processed_data_dict['ego'] = {}
+
+        ego_id = -1
+        ego_lidar_pose = []
+
+        # first find the ego vehicle's lidar pose
+        for cav_id, cav_content in base_data_dict.items():
+            if cav_content['ego']:
+                ego_id = cav_id
+                ego_lidar_pose = cav_content['params']['lidar_pose']
+                break
+        assert cav_id == list(base_data_dict.keys())[
+            0], "The first element in the OrderedDict must be ego"
+        assert ego_id != -1
+        assert len(ego_lidar_pose) > 0
+
+        pairwise_t_matrix = \
+            self.get_pairwise_transformation(base_data_dict,
+                                             self.max_cav)
+
+        processed_features = []
+        object_stack = []
+        object_id_stack = []
+
+        # prior knowledge for time delay correction and indicating data type
+        # (V2V vs V2i)
+        velocity = []
+        time_delay = []
+        infra = []
+        spatial_correction_matrix = []
+
+        if self.visualize:
+            projected_lidar_stack = []
+
+        # loop over all CAVs to process information
+        for cav_id, selected_cav_base in base_data_dict.items():
+            # check if the cav is within the communication range with ego
+            distance = \
+                math.sqrt((selected_cav_base['params']['lidar_pose'][0] -
+                           ego_lidar_pose[0]) ** 2 + (
+                                  selected_cav_base['params'][
+                                      'lidar_pose'][1] - ego_lidar_pose[
+                                      1]) ** 2)
+            if distance > opencood.data_utils.datasets.COM_RANGE:
+                continue
+
+            selected_cav_processed = self.get_item_single_car(
+                selected_cav_base,
+                ego_lidar_pose)
+
+            object_stack.append(selected_cav_processed['object_bbx_center'])
+            object_id_stack += selected_cav_processed['object_ids']
+            processed_features.append(
+                selected_cav_processed['processed_features'])
+
+            velocity.append(selected_cav_processed['velocity'])
+            time_delay.append(float(selected_cav_base['time_delay']))
+            # this is only useful when proj_first = True, and communication
+            # delay is considered. Right now only V2X-ViT utilizes the
+            # spatial_correction. There is a time delay when the cavs project
+            # their lidar to ego and when the ego receives the feature, and
+            # this variable is used to correct such pose difference (ego_t-1 to
+            # ego_t)
+            spatial_correction_matrix.append(
+                selected_cav_base['params']['spatial_correction_matrix'])
+            infra.append(1 if int(cav_id) < 0 else 0)
+
+            if self.visualize:
+                projected_lidar_stack.append(
+                    selected_cav_processed['projected_lidar'])
+
+        # exclude all repetitive objects
+        unique_indices = \
+            [object_id_stack.index(x) for x in set(object_id_stack)]
+        object_stack = np.vstack(object_stack)
+        object_stack = object_stack[unique_indices]
+
+        # make sure bounding boxes across all frames have the same number
+        object_bbx_center = \
+            np.zeros((self.params['postprocess']['max_num'], 7))
+        mask = np.zeros(self.params['postprocess']['max_num'])
+        object_bbx_center[:object_stack.shape[0], :] = object_stack
+        mask[:object_stack.shape[0]] = 1
+
+        # merge preprocessed features from different cavs into the same dict
+        cav_num = len(processed_features)
+        merged_feature_dict = self.merge_features_to_dict(processed_features)
+
+        # generate the anchor boxes
+        anchor_box = self.post_processor.generate_anchor_box()
+
+        # generate targets label
+        label_dict = \
+            self.post_processor.generate_label(
+                gt_box_center=object_bbx_center,
+                anchors=anchor_box,
+                mask=mask)
+
+        # pad dv, dt, infra to max_cav
+        velocity = velocity + (self.max_cav - len(velocity)) * [0.]
+        time_delay = time_delay + (self.max_cav - len(time_delay)) * [0.]
+        infra = infra + (self.max_cav - len(infra)) * [0.]
+        spatial_correction_matrix = np.stack(spatial_correction_matrix)
+        padding_eye = np.tile(np.eye(4)[None],(self.max_cav - len(
+                                               spatial_correction_matrix),1,1))
+        spatial_correction_matrix = np.concatenate([spatial_correction_matrix,
+                                                   padding_eye], axis=0)
+
+        processed_data_dict['ego'].update(
+            {'object_bbx_center': object_bbx_center,
+             'object_bbx_mask': mask,
+             'object_ids': [object_id_stack[i] for i in unique_indices],
+             'anchor_box': anchor_box,
+             'processed_lidar': merged_feature_dict,
+             'label_dict': label_dict,
+             'cav_num': cav_num,
+             'velocity': velocity,
+             'time_delay': time_delay,
+             'infra': infra,
+             'spatial_correction_matrix': spatial_correction_matrix,
+             'pairwise_t_matrix': pairwise_t_matrix})
+
+        if self.visualize:
+            processed_data_dict['ego'].update({'origin_lidar':
+                np.vstack(
+                    projected_lidar_stack)})
+        return processed_data_dict
+
+    def get_item_test(self, base_data_dict):
+        processed_data_dict = OrderedDict()
+        processed_data_dict['ego'] = {}
+
+        ego_id = -1
+        ego_lidar_pose = []
+
+        # first find the ego vehicle's lidar pose
+        for cav_id, cav_content in base_data_dict.items():
+            if cav_content['ego']:
+                ego_id = cav_id
+                ego_lidar_pose = cav_content['params']['lidar_pose']
+                break
+        assert cav_id == list(base_data_dict.keys())[
+            0], "The first element in the OrderedDict must be ego"
+        assert ego_id != -1
+        assert len(ego_lidar_pose) > 0
+
+        pairwise_t_matrix = \
+            self.get_pairwise_transformation(base_data_dict,
+                                             self.max_cav)
+
+        processed_features = []
+        object_stack = []
+        object_id_stack = []
+
+        # prior knowledge for time delay correction and indicating data type
+        # (V2V vs V2i)
+        velocity = []
+        time_delay = []
+        infra = []
+        spatial_correction_matrix = []
+
+        if self.visualize:
+            projected_lidar_stack = []
+
+        # loop over all CAVs to process information
+        for cav_id, selected_cav_base in base_data_dict.items():
+            # check if the cav is within the communication range with ego
+            distance = \
+                math.sqrt((selected_cav_base['params']['lidar_pose'][0] -
+                           ego_lidar_pose[0]) ** 2 + (
+                                  selected_cav_base['params'][
+                                      'lidar_pose'][1] - ego_lidar_pose[
+                                      1]) ** 2)
+            if distance > opencood.data_utils.datasets.COM_RANGE:
+                continue
+
+            selected_cav_processed = self.get_item_single_car(
+                selected_cav_base,
+                ego_lidar_pose)
+
+            object_stack.append(selected_cav_processed['object_bbx_center'])
+            object_id_stack += selected_cav_processed['object_ids']
+            processed_features.append(
+                selected_cav_processed['processed_features'])
+
+            velocity.append(selected_cav_processed['velocity'])
+            time_delay.append(float(selected_cav_base['time_delay']))
+            # this is only useful when proj_first = True, and communication
+            # delay is considered. Right now only V2X-ViT utilizes the
+            # spatial_correction. There is a time delay when the cavs project
+            # their lidar to ego and when the ego receives the feature, and
+            # this variable is used to correct such pose difference (ego_t-1 to
+            # ego_t)
+            spatial_correction_matrix.append(
+                selected_cav_base['params']['spatial_correction_matrix'])
+            infra.append(1 if int(cav_id) < 0 else 0)
+
+            if self.visualize:
+                projected_lidar_stack.append(
+                    selected_cav_processed['projected_lidar'])
+
+        # exclude all repetitive objects
+        unique_indices = \
+            [object_id_stack.index(x) for x in set(object_id_stack)]
+        object_stack = np.vstack(object_stack)
+        object_stack = object_stack[unique_indices]
+
+        # make sure bounding boxes across all frames have the same number
+        object_bbx_center = \
+            np.zeros((self.params['postprocess']['max_num'], 7))
+        mask = np.zeros(self.params['postprocess']['max_num'])
+        object_bbx_center[:object_stack.shape[0], :] = object_stack
+        mask[:object_stack.shape[0]] = 1
+
+        # merge preprocessed features from different cavs into the same dict
+        cav_num = len(processed_features)
+        merged_feature_dict = self.merge_features_to_dict(processed_features)
+
+        # generate the anchor boxes
+        anchor_box = self.post_processor.generate_anchor_box()
+
+        # generate targets label
+        label_dict = \
+            self.post_processor.generate_label(
+                gt_box_center=object_bbx_center,
+                anchors=anchor_box,
+                mask=mask)
+
+        # pad dv, dt, infra to max_cav
+        velocity = velocity + (self.max_cav - len(velocity)) * [0.]
+        time_delay = time_delay + (self.max_cav - len(time_delay)) * [0.]
+        infra = infra + (self.max_cav - len(infra)) * [0.]
+        spatial_correction_matrix = np.stack(spatial_correction_matrix)
+        padding_eye = np.tile(np.eye(4)[None],(self.max_cav - len(
+                                               spatial_correction_matrix),1,1))
+        spatial_correction_matrix = np.concatenate([spatial_correction_matrix,
+                                                   padding_eye], axis=0)
+
+        processed_data_dict['ego'].update(
+            {'object_bbx_center': object_bbx_center,
+             'object_bbx_mask': mask,
+             'object_ids': [object_id_stack[i] for i in unique_indices],
+             'anchor_box': anchor_box,
+             'processed_lidar': merged_feature_dict,
+             'label_dict': label_dict,
+             'cav_num': cav_num,
+             'velocity': velocity,
+             'time_delay': time_delay,
+             'infra': infra,
+             'spatial_correction_matrix': spatial_correction_matrix,
+             'pairwise_t_matrix': pairwise_t_matrix})
+
+        if self.visualize:
+            processed_data_dict['ego'].update({'origin_lidar':
+                np.vstack(
+                    projected_lidar_stack)})
+        return processed_data_dict
+
+    def get_item_single_car(self, selected_cav_base, ego_pose):
+        """
+        Project the lidar and bbx to ego space first, and then do clipping.
+
+        Parameters
+        ----------
+        selected_cav_base : dict
+            The dictionary contains a single CAV's raw information.
+        ego_pose : list
+            The ego vehicle lidar pose under world coordinate.
+
+        Returns
+        -------
+        selected_cav_processed : dict
+            The dictionary contains the cav's processed information.
+        """
+        selected_cav_processed = {}
+
+        # calculate the transformation matrix
+        transformation_matrix = \
+            selected_cav_base['params']['transformation_matrix']
+
+        # retrieve objects under ego coordinates
+        object_bbx_center, object_bbx_mask, object_ids = \
+            self.post_processor.generate_object_center([selected_cav_base],
+                                                       ego_pose)
+
+        # filter lidar
+        lidar_np = selected_cav_base['lidar_np']
+        lidar_np = shuffle_points(lidar_np)
+        # remove points that hit itself
+        lidar_np = mask_ego_points(lidar_np)
+        # project the lidar to ego space
+        if self.proj_first:
+            lidar_np[:, :3] = \
+                box_utils.project_points_by_matrix_torch(lidar_np[:, :3],
+                                                         transformation_matrix)
+        lidar_np = mask_points_by_range(lidar_np,
+                                        self.params['preprocess'][
+                                            'cav_lidar_range'])
+        processed_lidar = self.pre_processor.preprocess(lidar_np)
+
+        # velocity
+        velocity = selected_cav_base['params']['ego_speed']
+        # normalize veloccity by average speed 30 km/h
+        velocity = velocity / 30
+
+        selected_cav_processed.update(
+            {'object_bbx_center': object_bbx_center[object_bbx_mask == 1],
+             'object_ids': object_ids,
+             'projected_lidar': lidar_np,
+             'processed_features': processed_lidar,
+             'velocity': velocity})
+
+        return selected_cav_processed
+
+    @staticmethod
+    def merge_features_to_dict(processed_feature_list):
+        """
+        Merge the preprocessed features from different cavs to the same
+        dictionary.
+
+        Parameters
+        ----------
+        processed_feature_list : list
+            A list of dictionary containing all processed features from
+            different cavs.
+
+        Returns
+        -------
+        merged_feature_dict: dict
+            key: feature names, value: list of features.
+        """
+
+        merged_feature_dict = OrderedDict()
+
+        for i in range(len(processed_feature_list)):
+            for feature_name, feature in processed_feature_list[i].items():
+                if feature_name not in merged_feature_dict:
+                    merged_feature_dict[feature_name] = []
+                if isinstance(feature, list):
+                    merged_feature_dict[feature_name] += feature
+                else:
+                    merged_feature_dict[feature_name].append(feature)
+
+        return merged_feature_dict
+
+    def collate_batch_train(self, batch):
+        # Intermediate fusion is different the other two
+        output_dict = {'ego': {}}
+
+        object_bbx_center = []
+        object_bbx_mask = []
+        object_ids = []
+        processed_lidar_list = []
+        # used to record different scenario
+        record_len = []
+        label_dict_list = []
+
+        # used for PriorEncoding for models
+        velocity = []
+        time_delay = []
+        infra = []
+
+        # pairwise transformation matrix
+        pairwise_t_matrix_list = []
+
+        # used for correcting the spatial transformation between delayed timestamp
+        # and current timestamp
+        spatial_correction_matrix_list = []
+
+        if self.visualize:
+            origin_lidar = []
+
+        for i in range(len(batch)):
+            ego_dict = batch[i]['ego']
+            object_bbx_center.append(ego_dict['object_bbx_center'])
+            object_bbx_mask.append(ego_dict['object_bbx_mask'])
+            object_ids.append(ego_dict['object_ids'])
+
+            processed_lidar_list.append(ego_dict['processed_lidar'])
+            record_len.append(ego_dict['cav_num'])
+            label_dict_list.append(ego_dict['label_dict'])
+            pairwise_t_matrix_list.append(ego_dict['pairwise_t_matrix'])
+
+            velocity.append(ego_dict['velocity'])
+            time_delay.append(ego_dict['time_delay'])
+            infra.append(ego_dict['infra'])
+            spatial_correction_matrix_list.append(
+                ego_dict['spatial_correction_matrix'])
+
+            if self.visualize:
+                origin_lidar.append(ego_dict['origin_lidar'])
+        # convert to numpy, (B, max_num, 7)
+        object_bbx_center = torch.from_numpy(np.array(object_bbx_center))
+        object_bbx_mask = torch.from_numpy(np.array(object_bbx_mask))
+
+        # example: {'voxel_features':[np.array([1,2,3]]),
+        # np.array([3,5,6]), ...]}
+        merged_feature_dict = self.merge_features_to_dict(processed_lidar_list)
+        processed_lidar_torch_dict = \
+            self.pre_processor.collate_batch(merged_feature_dict)
+        # [2, 3, 4, ..., M], M <= max_cav
+        record_len = torch.from_numpy(np.array(record_len, dtype=int))
+        label_torch_dict = \
+            self.post_processor.collate_batch(label_dict_list)
+
+        # (B, max_cav)
+        velocity = torch.from_numpy(np.array(velocity))
+        time_delay = torch.from_numpy(np.array(time_delay))
+        infra = torch.from_numpy(np.array(infra))
+        spatial_correction_matrix_list = \
+            torch.from_numpy(np.array(spatial_correction_matrix_list))
+        # (B, max_cav, 3)
+        prior_encoding = \
+            torch.stack([velocity, time_delay, infra], dim=-1).float()
+        # (B, max_cav)
+        pairwise_t_matrix = torch.from_numpy(np.array(pairwise_t_matrix_list))
+
+        # object id is only used during inference, where batch size is 1.
+        # so here we only get the first element.
+        output_dict['ego'].update({'object_bbx_center': object_bbx_center,
+                                   'object_bbx_mask': object_bbx_mask,
+                                   'processed_lidar': processed_lidar_torch_dict,
+                                   'record_len': record_len,
+                                   'label_dict': label_torch_dict,
+                                   'object_ids': object_ids[0],
+                                   'prior_encoding': prior_encoding,
+                                   'spatial_correction_matrix': spatial_correction_matrix_list,
+                                   'pairwise_t_matrix': pairwise_t_matrix})
+
+        if self.visualize:
+            origin_lidar = \
+                np.array(downsample_lidar_minimum(pcd_np_list=origin_lidar))
+            origin_lidar = torch.from_numpy(origin_lidar)
+            output_dict['ego'].update({'origin_lidar': origin_lidar})
+
+        return output_dict
+
+    def collate_batch_test(self, batch):
+        assert len(batch) <= 1, "Batch size 1 is required during testing!"
+        output_dict = self.collate_batch_train(batch)
+
+        # check if anchor box in the batch
+        if batch[0]['ego']['anchor_box'] is not None:
+            output_dict['ego'].update({'anchor_box':
+                torch.from_numpy(np.array(
+                    batch[0]['ego'][
+                        'anchor_box']))})
+
+        # save the transformation matrix (4, 4) to ego vehicle
+        transformation_matrix_torch = \
+            torch.from_numpy(np.identity(4)).float()
+        output_dict['ego'].update({'transformation_matrix':
+                                       transformation_matrix_torch})
+
+        return output_dict
+
+    def post_process(self, data_dict, output_dict):
+        """
+        Process the outputs of the model to 2D/3D bounding box.
+
+        Parameters
+        ----------
+        data_dict : dict
+            The dictionary containing the origin input data of model.
+
+        output_dict :dict
+            The dictionary containing the output of the model.
+
+        Returns
+        -------
+        pred_box_tensor : torch.Tensor
+            The tensor of prediction bounding box after NMS.
+        gt_box_tensor : torch.Tensor
+            The tensor of gt bounding box.
+        """
+        pred_box_tensor, pred_score = \
+            self.post_processor.post_process(data_dict, output_dict)
+        gt_box_tensor = self.post_processor.generate_gt_bbx(data_dict)
+
+        return pred_box_tensor, pred_score, gt_box_tensor
+
+    def get_pairwise_transformation(self, base_data_dict, max_cav):
+        """
+        Get pair-wise transformation matrix accross different agents.
+
+        Parameters
+        ----------
+        base_data_dict : dict
+            Key : cav id, item: transformation matrix to ego, lidar points.
+
+        max_cav : int
+            The maximum number of cav, default 5
+
+        Return
+        ------
+        pairwise_t_matrix : np.array
+            The pairwise transformation matrix across each cav.
+            shape: (L, L, 4, 4)
+        """
+        pairwise_t_matrix = np.zeros((max_cav, max_cav, 4, 4))
+
+        if self.proj_first:
+            # if lidar projected to ego first, then the pairwise matrix
+            # becomes identity
+            pairwise_t_matrix[:, :] = np.identity(4)
+        else:
+            warnings.warn("Projection later is not supported in "
+                          "the current version. Using it will throw"
+                          "an error.")
+            t_list = []
+
+            # save all transformation matrix in a list in order first.
+            for cav_id, cav_content in base_data_dict.items():
+                t_list.append(cav_content['params']['transformation_matrix'])
+
+            for i in range(len(t_list)):
+                for j in range(len(t_list)):
+                    # identity matrix to self
+                    if i == j:
+                        continue
+                    # i->j: TiPi=TjPj, Tj^(-1)TiPi = Pj
+                    t_matrix = np.dot(np.linalg.inv(t_list[j]), t_list[i])
+                    pairwise_t_matrix[i, j] = t_matrix
+
+        return pairwise_t_matrix
diff --git a/opencood/opencood/data_utils/datasets/intermediate_fusion_dataset_v2.py b/opencood/opencood/data_utils/datasets/intermediate_fusion_dataset_v2.py
new file mode 100644
index 00000000..700c3404
--- /dev/null
+++ b/opencood/opencood/data_utils/datasets/intermediate_fusion_dataset_v2.py
@@ -0,0 +1,390 @@
+# -*- coding: utf-8 -*-
+# Author: Yunshuang Yuan <yunshuang.yuan@ikg.uni-hannover.de>
+# Modified by: Runsheng Xu <rxx3386@ucla.edu>
+# License: TDG-Attribution-NonCommercial-NoDistrib
+
+"""
+Dataset class for 2-stage backbone intermediate fusion
+"""
+import math
+from collections import OrderedDict
+
+import numpy as np
+import torch
+
+import opencood.data_utils.datasets
+import opencood.data_utils.post_processor as post_processor
+from opencood.utils import box_utils
+from opencood.data_utils.datasets import basedataset
+from opencood.data_utils.pre_processor import build_preprocessor
+from opencood.utils.pcd_utils import \
+    mask_points_by_range, mask_ego_points, shuffle_points
+from opencood.utils.transformation_utils import x1_to_x2
+
+
+class IntermediateFusionDatasetV2(basedataset.BaseDataset):
+    """
+    This class is for intermediate fusion where each vehicle transmit the
+    deep features to ego.
+    """
+
+    def __init__(self, params, visualize, train=True):
+        super(IntermediateFusionDatasetV2, self). \
+            __init__(params, visualize, train)
+        self.pre_processor = \
+            build_preprocessor(params['preprocess'], train)
+        self.post_processor = \
+            post_processor.build_postprocessor(params['postprocess'], train)
+
+    def __getitem__(self, idx):
+        # put here to avoid initialization error
+        from opencood.pcdet_utils.roiaware_pool3d.roiaware_pool3d_utils \
+            import points_in_boxes_cpu
+        base_data_dict = self.retrieve_base_data(idx)
+
+        processed_data_dict = OrderedDict()
+        processed_data_dict['ego'] = {}
+
+        ego_id = -1
+        ego_lidar_pose = []
+
+        # first find the ego vehicle's lidar pose
+        for cav_id, cav_content in base_data_dict.items():
+            if cav_content['ego']:
+                ego_id = cav_id
+                ego_lidar_pose = cav_content['params']['lidar_pose']
+                break
+        assert cav_id == list(base_data_dict.keys())[
+            0], "The first element in the OrderedDict must be ego"
+        assert ego_id != -1
+        assert len(ego_lidar_pose) > 0
+
+        processed_features = []
+        object_stack = []
+        object_id_stack = []
+
+        projected_lidar_stack = []
+
+        # loop over all CAVs to process information
+        for cav_id, selected_cav_base in base_data_dict.items():
+            # check if the cav is within the communication range with ego
+            distance = \
+                math.sqrt((selected_cav_base['params']['lidar_pose'][0] -
+                           ego_lidar_pose[0]) ** 2 + (
+                                  selected_cav_base['params'][
+                                      'lidar_pose'][1] - ego_lidar_pose[
+                                      1]) ** 2)
+            if distance > opencood.data_utils.datasets.COM_RANGE:
+                continue
+
+            selected_cav_processed = self.get_item_single_car(
+                selected_cav_base,
+                ego_lidar_pose)
+            if len(selected_cav_processed['projected_lidar']) > 10:
+                object_stack.append(
+                    selected_cav_processed['object_bbx_center'])
+                object_id_stack += selected_cav_processed['object_ids']
+                processed_features.append(
+                    selected_cav_processed['processed_features'])
+
+                projected_lidar_stack.append(
+                    selected_cav_processed['projected_lidar'])
+
+        # exclude all repetitive objects
+        unique_indices = \
+            [object_id_stack.index(x) for x in set(object_id_stack)]
+        object_stack_all = np.vstack(object_stack)
+        object_stack_all = object_stack_all[unique_indices]
+
+        # make sure bounding boxes across all frames have the same number
+        object_bbx_center = \
+            np.zeros((self.params['postprocess']['max_num'], 7))
+        mask = np.zeros(self.params['postprocess']['max_num'])
+        object_bbx_center[:object_stack_all.shape[0], :] = object_stack_all
+        mask[:object_stack_all.shape[0]] = 1
+
+        # merge preprocessed features from different cavs into the same dict
+        cav_num = len(processed_features)
+        merged_feature_dict = self.merge_features_to_dict(processed_features)
+
+        # generate the anchor boxes
+        anchor_box = self.post_processor.generate_anchor_box()
+
+        # generate targets label
+        label_dict = \
+            self.post_processor.generate_label(
+                gt_box_center=object_bbx_center, # hwl
+                anchors=anchor_box,
+                mask=mask)
+
+        # Filter empty boxes
+        object_stack_filtered = []
+        label_dict_no_coop = []
+        for boxes, points in zip(object_stack, projected_lidar_stack):
+            point_indices = points_in_boxes_cpu(points[:, :3], boxes[:,
+                                                               [0, 1, 2, 5, 4,
+                                                                3, 6]])
+            cur_mask = point_indices.sum(axis=1) > 0
+            if cur_mask.sum() == 0:
+                label_dict_no_coop.append({
+                    'pos_equal_one': np.zeros((*anchor_box.shape[:2],
+                                               self.post_processor.anchor_num)),
+                    'neg_equal_one': np.ones((*anchor_box.shape[:2],
+                                              self.post_processor.anchor_num)),
+                    'targets': np.zeros((*anchor_box.shape[:2],
+                                         self.post_processor.anchor_num * 7))
+                })
+                continue
+            object_stack_filtered.append(boxes[cur_mask])
+            bbx_center = \
+                np.zeros((self.params['postprocess']['max_num'], 7))
+            bbx_mask = np.zeros(self.params['postprocess']['max_num'])
+            bbx_center[:boxes[cur_mask].shape[0], :] = boxes[cur_mask]
+            bbx_mask[:boxes[cur_mask].shape[0]] = 1
+            label_dict_no_coop.append(
+                self.post_processor.generate_label(
+                    gt_box_center=bbx_center, # hwl
+                    anchors=anchor_box,
+                    mask=bbx_mask)
+            )
+        label_dict = {
+            'stage1': label_dict_no_coop,
+            'stage2': label_dict
+        }
+        processed_data_dict['ego'].update(
+            {'object_bbx_center': object_bbx_center,
+             'object_bbx_mask': mask,
+             'object_ids': [object_id_stack[i] for i in unique_indices],
+             'anchor_box': anchor_box,
+             'processed_lidar': merged_feature_dict,
+             'label_dict': label_dict,
+             'cav_num': cav_num})
+
+        processed_data_dict['ego'].update({'origin_lidar':
+                                               projected_lidar_stack})
+        return processed_data_dict
+
+    def get_item_single_car(self, selected_cav_base, ego_pose):
+        """
+        Project the lidar and bbx to ego space first, and then do clipping.
+
+        Parameters
+        ----------
+        selected_cav_base : dict
+            The dictionary contains a single CAV's raw information.
+        ego_pose : list
+            The ego vehicle lidar pose under world coordinate.
+
+        Returns
+        -------
+        selected_cav_processed : dict
+            The dictionary contains the cav's processed information.
+        """
+        selected_cav_processed = {}
+
+        # calculate the transformation matrix
+        transformation_matrix = \
+            x1_to_x2(selected_cav_base['params']['lidar_pose'],
+                     ego_pose)
+
+        # retrieve objects under ego coordinates
+        object_bbx_center, object_bbx_mask, object_ids = \
+            self.post_processor.generate_object_center([selected_cav_base],
+                                                       ego_pose)
+
+        # filter lidar
+        lidar_np = selected_cav_base['lidar_np']
+        lidar_np = shuffle_points(lidar_np)
+        # remove points that hit itself
+        lidar_np = mask_ego_points(lidar_np)
+        # project the lidar to ego space
+        lidar_np[:, :3] = \
+            box_utils.project_points_by_matrix_torch(lidar_np[:, :3],
+                                                     transformation_matrix)
+        lidar_np = mask_points_by_range(lidar_np,
+                                        self.params['preprocess'][
+                                            'cav_lidar_range'])
+        processed_lidar = self.pre_processor.preprocess(lidar_np)
+
+        selected_cav_processed.update(
+            {'object_bbx_center': object_bbx_center[object_bbx_mask == 1],
+             'object_ids': object_ids,
+             'projected_lidar': lidar_np,
+             'processed_features': processed_lidar})
+
+        return selected_cav_processed
+
+    @staticmethod
+    def merge_features_to_dict(processed_feature_list):
+        """
+        Merge the preprocessed features from different cavs to the same
+        dictionary.
+
+        Parameters
+        ----------
+        processed_feature_list : list
+            A list of dictionary containing all processed features from
+            different cavs.
+
+        Returns
+        -------
+        merged_feature_dict: dict
+            key: feature names, value: list of features.
+        """
+
+        merged_feature_dict = OrderedDict()
+
+        for i in range(len(processed_feature_list)):
+            for feature_name, feature in processed_feature_list[i].items():
+                if feature_name not in merged_feature_dict:
+                    merged_feature_dict[feature_name] = []
+                if isinstance(feature, list):
+                    merged_feature_dict[feature_name] += feature
+                else:
+                    merged_feature_dict[feature_name].append(feature)
+
+        return merged_feature_dict
+
+    def collate_batch_train(self, batch):
+        # Intermediate fusion is different the other two
+        output_dict = {'ego': {}}
+
+        object_bbx_center = []
+        object_bbx_mask = []
+        object_ids = []
+        processed_lidar_list = []
+        # used to record different scenario
+        record_len = []
+        label_dict_list = []
+        label_dict_no_coop_list = []
+
+        origin_lidar = []
+
+        # added by yys, fpvrcnn needs anchors for
+        # first stage proposal generation
+        if batch[0]['ego']['anchor_box'] is not None:
+            output_dict['ego'].update({'anchor_box':
+                torch.from_numpy(np.array(
+                    batch[0]['ego'][
+                        'anchor_box']))})
+
+        for i in range(len(batch)):
+            ego_dict = batch[i]['ego']
+            object_bbx_center.append(ego_dict['object_bbx_center'])
+            object_bbx_mask.append(ego_dict['object_bbx_mask'])
+            object_ids.append(ego_dict['object_ids'])
+
+            processed_lidar_list.append(ego_dict['processed_lidar'])
+            record_len.append(ego_dict['cav_num'])
+            label_dict_no_coop_list.append(ego_dict['label_dict']['stage1'])
+            label_dict_list.append(ego_dict['label_dict']['stage2'])
+
+            origin_lidar.append(ego_dict['origin_lidar'])
+
+        # convert to numpy, (B, max_num, 7)
+        object_bbx_center = torch.from_numpy(np.array(object_bbx_center))
+        object_bbx_mask = torch.from_numpy(np.array(object_bbx_mask))
+
+        # example: {'voxel_features':[np.array([1,2,3]]),
+        # np.array([3,5,6]), ...]}
+        merged_feature_dict = self.merge_features_to_dict(processed_lidar_list)
+        processed_lidar_torch_dict = \
+            self.pre_processor.collate_batch(merged_feature_dict)
+        # [2, 3, 4, ..., M], M <= 5
+        record_len = torch.from_numpy(np.array(record_len, dtype=int))
+        label_torch_dict = \
+            self.post_processor.collate_batch(label_dict_list)
+        label_dict_no_coop_list_ = [label_dict for label_list in
+                                    label_dict_no_coop_list for label_dict in
+                                    label_list]
+        for i in range(len(label_dict_no_coop_list_)):
+            if isinstance(label_dict_no_coop_list_[i], list):
+                print('debug')
+        label_no_coop_torch_dict = \
+            self.post_processor.collate_batch(label_dict_no_coop_list_)
+        # object id is only used during inference, where batch size is 1.
+        # so here we only get the first element.
+        output_dict['ego'].update({'object_bbx_center': object_bbx_center,
+                                   'object_bbx_mask': object_bbx_mask,
+                                   'processed_lidar': processed_lidar_torch_dict,
+                                   'record_len': record_len,
+                                   'label_dict': {
+                                       'stage1': label_no_coop_torch_dict,
+                                       'stage2': label_torch_dict},
+                                   'object_ids': object_ids[0]})
+
+        coords = []
+        idx = 0
+        for b in range(len(batch)):
+            for points in origin_lidar[b]:
+                assert len(points) != 0
+                coor_pad = np.pad(points, ((0, 0), (1, 0)),
+                                  mode="constant", constant_values=idx)
+                coords.append(coor_pad)
+                idx += 1
+        origin_lidar = np.concatenate(coords, axis=0)
+
+        origin_lidar = torch.from_numpy(origin_lidar)
+        output_dict['ego'].update({'origin_lidar': origin_lidar})
+
+        return output_dict
+
+    def collate_batch_test(self, batch):
+        assert len(batch) <= 1, "Batch size 1 is required during testing!"
+        output_dict = self.collate_batch_train(batch)
+
+        # check if anchor box in the batch
+        if batch[0]['ego']['anchor_box'] is not None:
+            output_dict['ego'].update({'anchor_box':
+                torch.from_numpy(np.array(
+                    batch[0]['ego'][
+                        'anchor_box']))})
+
+        # save the transformation matrix (4, 4) to ego vehicle
+        transformation_matrix_torch = \
+            torch.from_numpy(np.identity(4)).float()
+        output_dict['ego'].update({'transformation_matrix':
+                                       transformation_matrix_torch})
+
+        return output_dict
+
+    def post_process(self, data_dict, output_dict):
+        """
+        Process the outputs of the model to 2D/3D bounding box.
+
+        Parameters
+        ----------
+        data_dict : dict
+            The dictionary containing the origin input data of model.
+
+        output_dict :dict
+            The dictionary containing the output of the model.
+
+        Returns
+        -------
+        pred_box_tensor : torch.Tensor
+            The tensor of prediction bounding box after NMS.
+        gt_box_tensor : torch.Tensor
+            The tensor of gt bounding box.
+        """
+        pred_box_tensor, pred_score = \
+            self.post_processor.post_process(data_dict, output_dict)
+        gt_box_tensor = self.post_processor.generate_gt_bbx(data_dict)
+
+        return pred_box_tensor, pred_score, gt_box_tensor
+
+    def visualize_result(self, pred_box_tensor,
+                         gt_tensor,
+                         pcd,
+                         show_vis,
+                         save_path,
+                         dataset=None):
+        # we need to convert the pcd from [n, 5] -> [n, 4]
+        pcd = pcd[:, 1:]
+        # visualize the model output
+        self.post_processor.visualize(pred_box_tensor,
+                                      gt_tensor,
+                                      pcd,
+                                      show_vis,
+                                      save_path,
+                                      dataset=dataset)
diff --git a/opencood/opencood/data_utils/datasets/late_fusion_dataset.py b/opencood/opencood/data_utils/datasets/late_fusion_dataset.py
new file mode 100644
index 00000000..1644a0d2
--- /dev/null
+++ b/opencood/opencood/data_utils/datasets/late_fusion_dataset.py
@@ -0,0 +1,273 @@
+# -*- coding: utf-8 -*-
+# Author: Runsheng Xu <rxx3386@ucla.edu>
+# License: TDG-Attribution-NonCommercial-NoDistrib
+
+"""
+Dataset class for late fusion
+"""
+import random
+import math
+from collections import OrderedDict
+
+import numpy as np
+import torch
+from torch.utils.data import DataLoader
+
+import opencood.data_utils.datasets
+from opencood.data_utils.post_processor import build_postprocessor
+from opencood.data_utils.datasets import basedataset
+from opencood.data_utils.pre_processor import build_preprocessor
+from opencood.hypes_yaml.yaml_utils import load_yaml
+from opencood.utils import box_utils
+from opencood.utils.pcd_utils import \
+    mask_points_by_range, mask_ego_points, shuffle_points, \
+    downsample_lidar_minimum
+from opencood.utils.transformation_utils import x1_to_x2
+
+
+class LateFusionDataset(basedataset.BaseDataset):
+    """
+    This class is for intermediate fusion where each vehicle transmit the
+    detection outputs to ego.
+    """
+    def __init__(self, params, visualize, train=True):
+        super(LateFusionDataset, self).__init__(params, visualize, train)
+        self.pre_processor = build_preprocessor(params['preprocess'],
+                                                train)
+        self.post_processor = build_postprocessor(params['postprocess'], train)
+
+    def __getitem__(self, idx):
+        base_data_dict = self.retrieve_base_data(idx)
+        if self.train:
+            reformat_data_dict = self.get_item_train(base_data_dict)
+        else:
+            reformat_data_dict = self.get_item_test(base_data_dict)
+
+        return reformat_data_dict
+
+    def get_item_single_car(self, selected_cav_base):
+        """
+        Process a single CAV's information for the train/test pipeline.
+
+        Parameters
+        ----------
+        selected_cav_base : dict
+            The dictionary contains a single CAV's raw information.
+
+        Returns
+        -------
+        selected_cav_processed : dict
+            The dictionary contains the cav's processed information.
+        """
+        selected_cav_processed = {}
+
+        # filter lidar
+        lidar_np = selected_cav_base['lidar_np']
+        lidar_np = shuffle_points(lidar_np)
+        lidar_np = mask_points_by_range(lidar_np,
+                                        self.params['preprocess'][
+                                            'cav_lidar_range'])
+        # remove points that hit ego vehicle
+        lidar_np = mask_ego_points(lidar_np)
+
+        # generate the bounding box(n, 7) under the cav's space
+        object_bbx_center, object_bbx_mask, object_ids = \
+            self.post_processor.generate_object_center([selected_cav_base],
+                                                       selected_cav_base[
+                                                           'params'][
+                                                           'lidar_pose'])
+        # data augmentation
+        lidar_np, object_bbx_center, object_bbx_mask = \
+            self.augment(lidar_np, object_bbx_center, object_bbx_mask)
+
+        if self.visualize:
+            selected_cav_processed.update({'origin_lidar': lidar_np})
+
+        # pre-process the lidar to voxel/bev/downsampled lidar
+        lidar_dict = self.pre_processor.preprocess(lidar_np)
+        selected_cav_processed.update({'processed_lidar': lidar_dict})
+
+        # generate the anchor boxes
+        anchor_box = self.post_processor.generate_anchor_box()
+        selected_cav_processed.update({'anchor_box': anchor_box})
+
+        selected_cav_processed.update({'object_bbx_center': object_bbx_center,
+                                       'object_bbx_mask': object_bbx_mask,
+                                       'object_ids': object_ids})
+
+        # generate targets label
+        label_dict = \
+            self.post_processor.generate_label(
+                gt_box_center=object_bbx_center,
+                anchors=anchor_box,
+                mask=object_bbx_mask)
+        selected_cav_processed.update({'label_dict': label_dict})
+
+        return selected_cav_processed
+
+    def get_item_train(self, base_data_dict):
+        processed_data_dict = OrderedDict()
+
+        # during training, we return a random cav's data
+        if not self.visualize:
+            selected_cav_id, selected_cav_base = \
+                random.choice(list(base_data_dict.items()))
+        else:
+            selected_cav_id, selected_cav_base = \
+                list(base_data_dict.items())[0]
+
+        selected_cav_processed = self.get_item_single_car(selected_cav_base)
+        processed_data_dict.update({'ego': selected_cav_processed})
+
+        return processed_data_dict
+
+    def get_item_test(self, base_data_dict):
+        processed_data_dict = OrderedDict()
+        ego_id = -1
+        ego_lidar_pose = []
+
+        # first find the ego vehicle's lidar pose
+        for cav_id, cav_content in base_data_dict.items():
+            if cav_content['ego']:
+                ego_id = cav_id
+                ego_lidar_pose = cav_content['params']['lidar_pose']
+                break
+
+        assert ego_id != -1
+        assert len(ego_lidar_pose) > 0
+
+        # loop over all CAVs to process information
+        for cav_id, selected_cav_base in base_data_dict.items():
+            distance = \
+                math.sqrt((selected_cav_base['params']['lidar_pose'][0] -
+                           ego_lidar_pose[0])**2 + (
+                                      selected_cav_base['params'][
+                                          'lidar_pose'][1] - ego_lidar_pose[
+                                          1])**2)
+            if distance > opencood.data_utils.datasets.COM_RANGE:
+                continue
+
+            # find the transformation matrix from current cav to ego.
+            cav_lidar_pose = selected_cav_base['params']['lidar_pose']
+            transformation_matrix = x1_to_x2(cav_lidar_pose, ego_lidar_pose)
+
+            selected_cav_processed = \
+                self.get_item_single_car(selected_cav_base)
+            selected_cav_processed.update({'transformation_matrix':
+                                               transformation_matrix})
+            update_cav = "ego" if cav_id == ego_id else cav_id
+            processed_data_dict.update({update_cav: selected_cav_processed})
+
+        return processed_data_dict
+
+    def collate_batch_test(self, batch):
+        """
+        Customized collate function for pytorch dataloader during testing
+        for late fusion dataset.
+
+        Parameters
+        ----------
+        batch : dict
+
+        Returns
+        -------
+        batch : dict
+            Reformatted batch.
+        """
+        # currently, we only support batch size of 1 during testing
+        assert len(batch) <= 1, "Batch size 1 is required during testing!"
+        batch = batch[0]
+
+        output_dict = {}
+
+        # for late fusion, we also need to stack the lidar for better
+        # visualization
+        if self.visualize:
+            projected_lidar_list = []
+            origin_lidar = []
+
+        for cav_id, cav_content in batch.items():
+            output_dict.update({cav_id: {}})
+            # shape: (1, max_num, 7)
+            object_bbx_center = \
+                torch.from_numpy(np.array([cav_content['object_bbx_center']]))
+            object_bbx_mask = \
+                torch.from_numpy(np.array([cav_content['object_bbx_mask']]))
+            object_ids = cav_content['object_ids']
+
+            # the anchor box is the same for all bounding boxes usually, thus
+            # we don't need the batch dimension.
+            if cav_content['anchor_box'] is not None:
+                output_dict[cav_id].update({'anchor_box':
+                    torch.from_numpy(np.array(
+                        cav_content[
+                            'anchor_box']))})
+            if self.visualize:
+                transformation_matrix = cav_content['transformation_matrix']
+                origin_lidar = [cav_content['origin_lidar']]
+
+                projected_lidar = cav_content['origin_lidar']
+                projected_lidar[:, :3] = \
+                    box_utils.project_points_by_matrix_torch(
+                        projected_lidar[:, :3],
+                        transformation_matrix)
+                projected_lidar_list.append(projected_lidar)
+
+            # processed lidar dictionary
+            processed_lidar_torch_dict = \
+                self.pre_processor.collate_batch(
+                    [cav_content['processed_lidar']])
+            # label dictionary
+            label_torch_dict = \
+                self.post_processor.collate_batch([cav_content['label_dict']])
+
+            # save the transformation matrix (4, 4) to ego vehicle
+            transformation_matrix_torch = \
+                torch.from_numpy(
+                    np.array(cav_content['transformation_matrix'])).float()
+
+            output_dict[cav_id].update({'object_bbx_center': object_bbx_center,
+                                        'object_bbx_mask': object_bbx_mask,
+                                        'processed_lidar': processed_lidar_torch_dict,
+                                        'label_dict': label_torch_dict,
+                                        'object_ids': object_ids,
+                                        'transformation_matrix': transformation_matrix_torch})
+
+            if self.visualize:
+                origin_lidar = \
+                    np.array(
+                        downsample_lidar_minimum(pcd_np_list=origin_lidar))
+                origin_lidar = torch.from_numpy(origin_lidar)
+                output_dict[cav_id].update({'origin_lidar': origin_lidar})
+
+        if self.visualize:
+            projected_lidar_stack = torch.from_numpy(
+                np.vstack(projected_lidar_list))
+            output_dict['ego'].update({'origin_lidar': projected_lidar_stack})
+
+        return output_dict
+
+    def post_process(self, data_dict, output_dict):
+        """
+        Process the outputs of the model to 2D/3D bounding box.
+
+        Parameters
+        ----------
+        data_dict : dict
+            The dictionary containing the origin input data of model.
+
+        output_dict :dict
+            The dictionary containing the output of the model.
+
+        Returns
+        -------
+        pred_box_tensor : torch.Tensor
+            The tensor of prediction bounding box after NMS.
+        gt_box_tensor : torch.Tensor
+            The tensor of gt bounding box.
+        """
+        pred_box_tensor, pred_score = \
+            self.post_processor.post_process(data_dict, output_dict)
+        gt_box_tensor = self.post_processor.generate_gt_bbx(data_dict)
+
+        return pred_box_tensor, pred_score, gt_box_tensor
diff --git a/opencood/opencood/data_utils/post_processor/__init__.py b/opencood/opencood/data_utils/post_processor/__init__.py
new file mode 100644
index 00000000..b6b603a1
--- /dev/null
+++ b/opencood/opencood/data_utils/post_processor/__init__.py
@@ -0,0 +1,26 @@
+# -*- coding: utf-8 -*-
+# Author: Runsheng Xu <rxx3386@ucla.edu>
+# License: TDG-Attribution-NonCommercial-NoDistrib
+
+from opencood.data_utils.post_processor.voxel_postprocessor import VoxelPostprocessor
+from opencood.data_utils.post_processor.bev_postprocessor import BevPostprocessor
+from opencood.data_utils.post_processor.ciassd_postprocessor import CiassdPostprocessor
+from opencood.data_utils.post_processor.fpvrcnn_postprocessor import FpvrcnnPostprocessor
+
+__all__ = {
+    'VoxelPostprocessor': VoxelPostprocessor,
+    'BevPostprocessor': BevPostprocessor,
+    'CiassdPostprocessor': CiassdPostprocessor,
+    'FpvrcnnPostprocessor': FpvrcnnPostprocessor,
+}
+
+
+def build_postprocessor(anchor_cfg, train):
+    process_method_name = anchor_cfg['core_method']
+    assert process_method_name in ['VoxelPostprocessor', 'BevPostprocessor', 'CiassdPostprocessor', 'FpvrcnnPostprocessor']
+    anchor_generator = __all__[process_method_name](
+        anchor_params=anchor_cfg,
+        train=train
+    )
+
+    return anchor_generator
diff --git a/opencood/opencood/data_utils/post_processor/base_postprocessor.py b/opencood/opencood/data_utils/post_processor/base_postprocessor.py
new file mode 100644
index 00000000..278cecfd
--- /dev/null
+++ b/opencood/opencood/data_utils/post_processor/base_postprocessor.py
@@ -0,0 +1,147 @@
+# -*- coding: utf-8 -*-
+# Author: Runsheng Xu <rxx3386@ucla.edu>
+# License: TDG-Attribution-NonCommercial-NoDistrib
+
+"""
+Template for AnchorGenerator
+"""
+
+import numpy as np
+import torch
+
+from opencood.utils import box_utils
+
+
+class BasePostprocessor(object):
+    """
+    Template for Anchor generator.
+
+    Parameters
+    ----------
+    anchor_params : dict
+        The dictionary containing all anchor-related parameters.
+    train : bool
+        Indicate train or test mode.
+
+    Attributes
+    ----------
+    bbx_dict : dictionary
+        Contain all objects information across the cav, key: id, value: bbx
+        coordinates (1, 7)
+    """
+
+    def __init__(self, anchor_params, train=True):
+        self.params = anchor_params
+        self.bbx_dict = {}
+        self.train = train
+
+    def generate_anchor_box(self):
+        # needs to be overloaded
+        return None
+
+    def generate_label(self, *argv):
+        return None
+
+    def generate_gt_bbx(self, data_dict):
+        """
+        The base postprocessor will generate 3d groundtruth bounding box.
+
+        Parameters
+        ----------
+        data_dict : dict
+            The dictionary containing the origin input data of model.
+
+        Returns
+        -------
+        gt_box3d_tensor : torch.Tensor
+            The groundtruth bounding box tensor, shape (N, 8, 3).
+        """
+        gt_box3d_list = []
+        # used to avoid repetitive bounding box
+        object_id_list = []
+
+        for cav_id, cav_content in data_dict.items():
+            # used to project gt bounding box to ego space
+            transformation_matrix = cav_content['transformation_matrix']
+
+            object_bbx_center = cav_content['object_bbx_center']
+            object_bbx_mask = cav_content['object_bbx_mask']
+            object_ids = cav_content['object_ids']
+            object_bbx_center = object_bbx_center[object_bbx_mask == 1]
+
+            # convert center to corner
+            object_bbx_corner = \
+                box_utils.boxes_to_corners_3d(object_bbx_center,
+                                              self.params['order'])
+            projected_object_bbx_corner = \
+                box_utils.project_box3d(object_bbx_corner.float(),
+                                        transformation_matrix)
+            gt_box3d_list.append(projected_object_bbx_corner)
+
+            # append the corresponding ids
+            object_id_list += object_ids
+
+        # gt bbx 3d
+        gt_box3d_list = torch.vstack(gt_box3d_list)
+        # some of the bbx may be repetitive, use the id list to filter
+        gt_box3d_selected_indices = \
+            [object_id_list.index(x) for x in set(object_id_list)]
+        gt_box3d_tensor = gt_box3d_list[gt_box3d_selected_indices]
+
+        # filter the gt_box to make sure all bbx are in the range
+        mask = \
+            box_utils.get_mask_for_boxes_within_range_torch(gt_box3d_tensor)
+        gt_box3d_tensor = gt_box3d_tensor[mask, :, :]
+
+        return gt_box3d_tensor
+
+    def generate_object_center(self,
+                               cav_contents,
+                               reference_lidar_pose):
+        """
+        Retrieve all objects in a format of (n, 7), where 7 represents
+        x, y, z, l, w, h, yaw or x, y, z, h, w, l, yaw.
+
+        Parameters
+        ----------
+        cav_contents : list
+            List of dictionary, save all cavs' information.
+
+        reference_lidar_pose : list
+            The final target lidar pose with length 6.
+
+        Returns
+        -------
+        object_np : np.ndarray
+            Shape is (max_num, 7).
+        mask : np.ndarray
+            Shape is (max_num,).
+        object_ids : list
+            Length is number of bbx in current sample.
+        """
+        from opencood.data_utils.datasets import GT_RANGE
+
+        tmp_object_dict = {}
+        for cav_content in cav_contents:
+            tmp_object_dict.update(cav_content['params']['vehicles'])
+
+        output_dict = {}
+        filter_range = self.params['anchor_args']['cav_lidar_range'] \
+            if self.train else GT_RANGE
+
+        box_utils.project_world_objects(tmp_object_dict,
+                                        output_dict,
+                                        reference_lidar_pose,
+                                        filter_range,
+                                        self.params['order'])
+
+        object_np = np.zeros((self.params['max_num'], 7))
+        mask = np.zeros(self.params['max_num'])
+        object_ids = []
+
+        for i, (object_id, object_bbx) in enumerate(output_dict.items()):
+            object_np[i] = object_bbx[0, :]
+            mask[i] = 1
+            object_ids.append(object_id)
+
+        return object_np, mask, object_ids
diff --git a/opencood/opencood/data_utils/post_processor/bev_postprocessor.py b/opencood/opencood/data_utils/post_processor/bev_postprocessor.py
new file mode 100644
index 00000000..c104ec92
--- /dev/null
+++ b/opencood/opencood/data_utils/post_processor/bev_postprocessor.py
@@ -0,0 +1,449 @@
+# -*- coding: utf-8 -*-
+# Author: Hao Xiang haxiang@g.ucla.edu
+# License: TDG-Attribution-NonCommercial-NoDistrib
+
+"""
+Anchor-free 2d Generator
+"""
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+
+from opencood.utils.transformation_utils import dist_to_continuous
+from opencood.data_utils.post_processor.base_postprocessor \
+    import BasePostprocessor
+from opencood.utils import box_utils
+from opencood.visualization import vis_utils
+
+
+class BevPostprocessor(BasePostprocessor):
+    def __init__(self, anchor_params, train):
+        super(BevPostprocessor, self).__init__(anchor_params, train)
+        # self.geometry_param = anchor_params["geometry"]
+        self.geometry_param = anchor_params["geometry_param"]
+
+        # TODO
+        # Hard coded for now. Need to calculate for our own training dataset
+        self.target_mean = np.array([0.008, 0.001, 0.202, 0.2, 0.43, 1.368])
+        self.target_std_dev = np.array([0.866, 0.5, 0.954, 0.668, 0.09, 0.111])
+
+    def generate_anchor_box(self):
+        return None
+
+    def generate_label(self, **kwargs):
+        """
+        Generate targets for training.
+
+        Parameters
+        ----------
+        kwargs : list
+            gt_box_center:(max_num, 7)
+
+        Returns
+        -------
+        label_dict : dict
+            Dictionary that contains all target related info.
+        """
+        assert self.params['order'] == 'lwh', \
+            'Currently BEV only support lwh bbx order.'
+        # (max_num, 7)
+        gt_box_center = kwargs['gt_box_center']
+
+        # (max_num)
+        masks = kwargs['mask']
+
+        # (n, 7)
+        gt_box_center_valid = gt_box_center[masks == 1]
+        # (n, 4, 3)
+        bev_corners = box_utils.boxes_to_corners2d(gt_box_center_valid,
+                                                   self.params['order'])
+
+        n = gt_box_center_valid.shape[0]
+        # (n, 4, 2)
+        bev_corners = bev_corners[:, :, :2]
+        yaw = gt_box_center_valid[:, -1]
+        x, y = gt_box_center_valid[:, 0], gt_box_center_valid[:, 1]
+        dx, dy = gt_box_center_valid[:, 3], gt_box_center_valid[:, 4]
+        # (n, 6)
+        reg_targets = np.column_stack([np.cos(yaw), np.sin(yaw), x, y, dx, dy])
+
+        # target label map including classification and regression targets
+        label_map = np.zeros(self.geometry_param["label_shape"])
+        self.update_label_map(label_map, bev_corners, reg_targets)
+        label_map = self.normalize_targets(label_map)
+        label_dict = {
+            # (7, label_shape[0], label_shape[1])
+            "label_map": np.transpose(label_map, (2, 0, 1)).astype(np.float32),
+            "bev_corners": bev_corners
+        }
+        return label_dict
+
+    def update_label_map(self, label_map, bev_corners, reg_targets):
+        """
+        Update label_map based on bbx and regression targets.
+
+        Parameters
+        ----------
+        label_map : numpy.array
+            Targets array for classification and regression tasks with
+            the shape of label_shape.
+
+        bev_corners : numpy.array
+            The bbx corners in lidar frame with shape (n, 4, 2)
+
+        reg_targets : numpy.array
+            Array containing the regression targets information. It need to be
+            further processed.
+
+        """
+        res = self.geometry_param["res"]
+        downsample_rate = self.geometry_param["downsample_rate"]
+
+        bev_origin = np.array([self.geometry_param["L1"],
+                               self.geometry_param["W1"]]).reshape(1, -1)
+
+        # discretized bbx corner representations -- (n, 4, 2)
+        bev_corners_dist = (bev_corners - bev_origin) / res / downsample_rate
+        # generate the coordinates of m
+        x = np.arange(self.geometry_param["label_shape"][0])
+        y = np.arange(self.geometry_param["label_shape"][1])
+        xx, yy = np.meshgrid(x, y)
+
+        # (label_shape[0]*label_shape[1], 2)
+        points = np.concatenate([xx.reshape(-1, 1), yy.reshape(-1, 1)],
+                                axis=-1)
+        bev_origin_dist = bev_origin / res / downsample_rate
+
+        # loop over each bbx, find the points within the bbx.
+        for i in range(bev_corners.shape[0]):
+            reg_target = reg_targets[i, :]
+
+            # find discredited points in bbx
+            points_in_box = \
+                box_utils.get_points_in_rotated_box(points,
+                                                    bev_corners_dist[i, ...])
+            # convert points to continuous space
+            points_continuous = dist_to_continuous(points_in_box,
+                                                   bev_origin_dist,
+                                                   res,
+                                                   downsample_rate)
+            actual_reg_target = np.repeat(reg_target.reshape(1, -1),
+                                          points_continuous.shape[0],
+                                          axis=0)
+            # build learning targets
+            actual_reg_target[:, 2:4] = \
+                actual_reg_target[:, 2:4] - points_continuous
+            actual_reg_target[:, 4:] = np.log(actual_reg_target[:, 4:])
+
+            # update label map
+            label_map[points_in_box[:, 0], points_in_box[:, 1], 0] = 1.0
+            label_map[points_in_box[:, 0], points_in_box[:, 1], 1:] = \
+                actual_reg_target
+
+    def normalize_targets(self, label_map):
+        """
+        Normalize label_map
+
+        Parameters
+        ----------
+        label_map : numpy.array
+            Targets array for classification and regression tasks with the
+            shape of label_shape.
+
+        Returns
+        -------
+        label_map: numpy.array
+            Nromalized label_map.
+
+        """
+        label_map[..., 1:] = \
+            (label_map[..., 1:] - self.target_mean) / self.target_std_dev
+        return label_map
+
+    def denormalize_reg_map(self, reg_map):
+        """
+        Denormalize the regression map
+
+        Parameters
+        ----------
+        reg_map : np.ndarray / torch.Tensor
+            Regression output mapwith the shape of (label_shape[0],
+            label_shape[1], 6).
+
+        Returns
+        -------
+        reg_map : np.ndarray / torch.Tensor
+            Denormalized regression map.
+
+        """
+        if isinstance(reg_map, np.ndarray):
+            target_mean = self.target_mean
+            target_std_dev = self.target_std_dev
+
+        else:
+            target_mean = \
+                torch.from_numpy(self.target_mean).to(reg_map.device)
+            target_std_dev = \
+                torch.from_numpy(self.target_std_dev).to(reg_map.device)
+        reg_map = reg_map * target_std_dev + target_mean
+        return reg_map
+
+    @staticmethod
+    def collate_batch(label_batch_list):
+        """
+        Customized collate function for target label generation.
+
+        Parameters
+        ----------
+        label_batch_list : list
+            The list of dictionary  that contains all labels for several
+            frames.
+
+        Returns
+        -------
+        processed_batch : dict
+            Reformatted labels in torch tensor.
+        """
+        label_map_list = [x["label_map"][np.newaxis, ...] for x in
+                          label_batch_list]
+        processed_batch = {
+            # (batch_size, 7, label_shape[0], label_shape[1])
+            "label_map": torch.from_numpy(np.concatenate(label_map_list,
+                                                         axis=0)),
+            "bev_corners": [torch.from_numpy(x["bev_corners"]) for x in
+                            label_batch_list]
+        }
+        return processed_batch
+
+    def post_process(self, data_dict, output_dict):
+        """
+        Process the outputs of the model to 2D bounding box.
+        Step1: convert each cav's output to bounding box format
+        Step2: project the bounding boxes to ego space.
+        Step:3 NMS
+
+        Parameters
+        ----------
+        data_dict : dict
+            The dictionary containing the origin input data of model.
+
+        output_dict :dict
+            The dictionary containing the output of the model.
+
+        Returns
+        -------
+        pred_box2d_tensor : torch.Tensor
+            The prediction bounding box tensor after NMS.
+
+        gt_box2d_tensor : torch.Tensor
+            The groundtruth bounding box tensor.
+        """
+
+        # the final bounding box list
+        pred_box2d_list = []
+        pred_score_list = []
+
+        for cav_id, cav_content in data_dict.items():
+            assert cav_id in output_dict
+            # the transformation matrix to ego space
+            transformation_matrix = cav_content['transformation_matrix']
+
+            # classification probability -- (label_shape[0], label_shape[1])
+            prob = output_dict[cav_id]['cls'].squeeze(0).squeeze(0)
+            prob = torch.sigmoid(prob)
+            # regression map -- (label_shape[0], label_shape[1], 6)
+            reg_map = output_dict[cav_id]['reg'].squeeze(0).permute(1, 2, 0)
+            reg_map = self.denormalize_reg_map(reg_map)
+            threshold = self.params['target_args']['score_threshold']
+            mask = torch.gt(prob, threshold)
+
+            if mask.sum() > 0:
+                # (number of high confidence bbx, 4, 2)
+                corners2d = self.reg_map_to_bbx_corners(reg_map, mask)
+                # assume the z-diviation in transformation_matrix is small,
+                # thus we can pad zeros to simulate the 3d transformation.
+                # (number of high confidence bbx, 4, 3)
+                box3d = F.pad(corners2d, (0, 1))
+                # (number of high confidence bbx, 4, 2)
+                projected_boxes2d = \
+                    box_utils.project_points_by_matrix_torch(box3d.view(-1, 3),
+                                                             transformation_matrix)[
+                    :, :2]
+
+                projected_boxes2d = projected_boxes2d.view(-1, 4, 2)
+                scores = prob[mask]
+                pred_box2d_list.append(projected_boxes2d)
+                pred_score_list.append(scores)
+
+        if len(pred_box2d_list):
+            pred_box2ds = torch.cat(pred_box2d_list, dim=0)
+            pred_scores = torch.cat(pred_score_list, dim=0)
+        else:
+            return None, None
+
+        keep_index = box_utils.nms_rotated(pred_box2ds, pred_scores,
+                                           self.params['nms_thresh'])
+        if len(keep_index):
+            pred_box2ds = pred_box2ds[keep_index]
+            pred_scores = pred_scores[keep_index]
+
+        # filter out the prediction out of the range.
+        mask = box_utils.get_mask_for_boxes_within_range_torch(pred_box2ds)
+        pred_box2ds = pred_box2ds[mask, :, :]
+        pred_scores = pred_scores[mask]
+        assert pred_scores.shape[0] == pred_box2ds.shape[0]
+        return pred_box2ds, pred_scores
+
+    def reg_map_to_bbx_corners(self, reg_map, mask):
+        """
+        Construct bbx from the regression output of the model.
+
+        Parameters
+        ----------
+        reg_map : torch.Tensor
+            Regression output of neural networks.
+
+        mask : torch.Tensor
+            Masks used to filter bbx.
+
+        Returns
+        -------
+        corners : torch.Tensor
+            Bbx output with shape (N, 4, 2).
+
+        """
+
+        assert len(reg_map.shape) == 3, \
+            "only support shape of label_shape i.e. (*, *, 6)"
+        device = reg_map.device
+
+        cos_t, sin_t, x, y, log_dx, log_dy = \
+            [tt.squeeze(-1) for tt in torch.chunk(reg_map, 6, dim=-1)]
+        yaw = torch.atan2(sin_t, cos_t)
+        dx, dy = log_dx.exp(), log_dy.exp()
+
+        grid_size = self.geometry_param["res"] * \
+                    self.geometry_param["downsample_rate"]
+        grid_x = torch.arange(self.geometry_param["L1"],
+                              self.geometry_param["L2"],
+                              grid_size, dtype=torch.float32, device=device)
+        grid_y = torch.arange(self.geometry_param["W1"],
+                              self.geometry_param["W2"],
+                              grid_size,
+                              dtype=torch.float32,
+                              device=device)
+
+        xx, yy = torch.meshgrid([grid_x, grid_y])
+        center_x = xx + x
+        center_y = yy + y
+
+        bbx2d = torch.stack([center_x, center_y, dx, dy, yaw], dim=-1)
+        bbx2d = bbx2d[mask, :]
+        corners = box_utils.boxes2d_to_corners2d(bbx2d)
+
+        return corners
+
+    def post_process_debug(self, data_dict, output_dict):
+        """
+        Process the outputs of the model to 2D bounding box for debug purpose.
+        Step1: convert each cav's output to bounding box format
+        Step2: project the bounding boxes to ego space.
+        Step:3 NMS
+
+        Parameters
+        ----------
+        data_dict : dict
+            The dictionary containing the origin input data of model.
+
+        output_dict :dict
+            The dictionary containing the output of the model.
+
+        Returns
+        -------
+        pred_box2d_tensor : torch.Tensor
+            The prediction bounding box tensor after NMS.
+        gt_box2d_tensor : torch.Tensor
+            The groundtruth bounding box tensor.
+        """
+        # the final bounding box list
+        pred_box2d_list = []
+        pred_score_list = []
+
+        # the transformation matrix to ego space
+        transformation_matrix = data_dict['transformation_matrix']
+
+        # classification probability -- (label_shape[0], label_shape[1])
+        prob = output_dict['cls'].squeeze(0).squeeze(0)
+        prob = torch.sigmoid(prob)
+
+        # regression map -- (label_shape[0], label_shape[1], 6)
+        reg_map = output_dict['reg'].squeeze(0).permute(1, 2, 0)
+        reg_map = self.denormalize_reg_map(reg_map)
+
+        threshold = 0.5
+        mask = torch.gt(prob, threshold)
+
+        if mask.sum() > 0:
+            # (number of high confidence bbx, 4, 2)
+            corners2d = self.reg_map_to_bbx_corners(reg_map, mask)
+            # assume the z-diviation in transformation_matrix is small,
+            # thus we can pad zeros to simulate the 3d transformation.
+            # (number of high confidence bbx, 4, 3)
+            box3d = F.pad(corners2d, (0, 1))
+
+            # (number of high confidence bbx, 4, 2)
+            projected_boxes2d = \
+                box_utils.project_points_by_matrix_torch(box3d.view(-1, 3),
+                                                         transformation_matrix)[:, :2]
+            projected_boxes2d = projected_boxes2d.view(-1, 4, 2)
+            scores = prob[mask]
+            pred_box2d_list.append(projected_boxes2d)
+            pred_score_list.append(scores)
+
+        pred_box2ds = torch.cat(pred_box2d_list, dim=0)
+        pred_scores = torch.cat(pred_score_list, dim=0)
+
+        keep_index = box_utils.nms_rotated(pred_box2ds,
+                                           pred_scores,
+                                           self.params['nms_thresh'])
+        pred_box2ds = pred_box2ds[keep_index]
+
+        # filter out the prediction out of the range.
+        mask = box_utils.get_mask_for_boxes_within_range_torch(pred_box2ds)
+        pred_box2ds = pred_box2ds[mask, :, :]
+        return pred_box2ds
+
+    @staticmethod
+    def visualize(pred_box_tensor, gt_tensor, pcd, show_vis, save_path,
+                  dataset=None):
+        """
+        Visualize the BEV 2D prediction, ground truth with point cloud together.
+
+        Parameters
+        ----------
+        pred_box_tensor : torch.Tensor
+            (N, 8, 3) prediction.
+
+        gt_tensor : torch.Tensor
+            (N, 8, 3) groundtruth bbx
+
+        pcd : torch.Tensor
+            PointCloud, (N, 4).
+
+        show_vis : bool
+            Whether to show visualization.
+
+        save_path : str
+            Save the visualization results to given path.
+
+        dataset : BaseDataset
+            opencood dataset object.
+        """
+        assert dataset is not None, "dataset argument can't be None"
+        vis_utils.visualize_single_sample_output_bev(pred_box_tensor,
+                                                     gt_tensor,
+                                                     pcd,
+                                                     dataset,
+                                                     show_vis,
+                                                     save_path)
diff --git a/opencood/opencood/data_utils/post_processor/ciassd_postprocessor.py b/opencood/opencood/data_utils/post_processor/ciassd_postprocessor.py
new file mode 100644
index 00000000..8ae7507b
--- /dev/null
+++ b/opencood/opencood/data_utils/post_processor/ciassd_postprocessor.py
@@ -0,0 +1,169 @@
+"""
+3D Anchor Generator for Voxel
+"""
+import math
+import sys
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+
+from opencood.data_utils.post_processor.voxel_postprocessor \
+    import VoxelPostprocessor
+from opencood.utils import box_utils
+
+
+class CiassdPostprocessor(VoxelPostprocessor):
+    def __init__(self, anchor_params, train):
+        super(CiassdPostprocessor, self).__init__(anchor_params, train)
+        self.train = train
+        self.anchor_num = self.params['anchor_args']['num']
+
+    def post_process(self, data_dict, output_dict):
+        """
+        Process the outputs of the model to 2D/3D bounding box.
+        Step1: convert each cav's output to bounding box format
+        Step2: project the bounding boxes to ego space.
+        Step:3 NMS
+
+        Parameters
+        ----------
+        data_dict : dict
+            The dictionary containing the origin input data of model.
+
+        output_dict :dict
+            The dictionary containing the output of the model.
+
+        Returns
+        -------
+        pred_box3d_tensor : torch.Tensor
+            The prediction bounding box tensor after NMS.
+        gt_box3d_tensor : torch.Tensor
+            The groundtruth bounding box tensor.
+        """
+        # the final bounding box list
+        global batch_num_box_count
+        pred_box3d_original_list = []
+        pred_box3d_list = []
+        pred_box2d_list = []
+
+        for cav_id, cav_content in data_dict.items():
+            assert cav_id in output_dict
+            # the transformation matrix to ego space
+            if 'transformation_matrix' in cav_content:
+                transformation_matrix = cav_content['transformation_matrix']
+            else:
+                transformation_matrix = torch.from_numpy(np.identity(4)).float().\
+                    to(cav_content['anchor_box'].device)
+
+            # (H, W, anchor_num, 7)
+            anchor_box = cav_content['anchor_box']
+
+            # prediction result
+            preds_dict = output_dict[cav_id]['preds_dict_stage1']
+
+            # preds
+            prob = preds_dict['cls_preds']
+            prob = torch.sigmoid(prob.permute(0, 2, 3, 1).contiguous())
+            reg = preds_dict['box_preds'].permute(0, 2, 3, 1).contiguous()
+            iou = preds_dict['iou_preds'].permute(0, 2, 3, 1).contiguous().reshape(1, -1)
+            dir = preds_dict['dir_cls_preds'].permute(0, 2, 3, 1).contiguous().reshape(1, -1, 2)
+
+            # convert regression map back to bounding box
+            # (N, W*L*anchor_num, 7)
+            batch_box3d = self.delta_to_boxes3d(reg, anchor_box, False)
+            mask = torch.gt(prob, self.params['target_args']['score_threshold'])
+            batch_num_box_count = [int(m.sum()) for m in mask]
+            mask = mask.view(1, -1)
+            mask_reg = mask.unsqueeze(2).repeat(1, 1, 7)
+
+            # during validation/testing, the batch size should be 1
+            if not self.train:
+                assert batch_box3d.shape[0] == 1
+
+            boxes3d = torch.masked_select(batch_box3d.view(-1, 7), mask_reg[0]).view(-1, 7)
+            scores = torch.masked_select(prob.view(-1), mask[0])
+
+            dir_labels = torch.max(dir, dim=-1)[1]
+            dir_labels = dir_labels[mask]
+            # top_labels = torch.zeros([scores.shape[0]], dtype=torch.long).cuda()
+            if scores.shape[0] != 0:
+                iou = (iou + 1) * 0.5
+                scores = scores * torch.pow(iou.masked_select(mask), 4)
+                # correct_direction
+                top_labels = (boxes3d[..., -1] > 0) ^ (dir_labels.byte() == 1)
+                boxes3d[..., -1] += torch.where(top_labels, torch.tensor(np.pi).type_as(boxes3d),
+                                                  torch.tensor(0.0).type_as(boxes3d))
+                pred_box3d_original_list.append(boxes3d.detach())
+
+            # convert output to bounding box
+            if len(boxes3d) != 0:
+                # (N, 8, 3)
+                boxes3d_corner = box_utils.boxes_to_corners_3d(boxes3d, order=self.params['order'])
+                # (N, 8, 3)
+                projected_boxes3d = box_utils.project_box3d(boxes3d_corner, transformation_matrix)
+                # convert 3d bbx to 2d, (N,4)
+                projected_boxes2d = box_utils.corner_to_standup_box_torch(projected_boxes3d)
+                # (N, 5)
+                boxes2d_score = torch.cat((projected_boxes2d, scores.unsqueeze(1)), dim=1)
+
+                pred_box2d_list.append(boxes2d_score)
+                pred_box3d_list.append(projected_boxes3d)
+
+        if len(pred_box2d_list) ==0 or len(pred_box3d_list) == 0:
+            return None, None
+        # shape: (N, 5)
+        pred_box2d_list = torch.vstack(pred_box2d_list)
+        # scores
+        scores = pred_box2d_list[:, -1]
+        # predicted 3d bbx
+        pred_box3d_tensor = torch.vstack(pred_box3d_list)
+        pred_box3d_original = torch.vstack(pred_box3d_original_list)
+
+        if not self.train:
+            # remove large bbx
+            keep_index_1 = box_utils.remove_large_pred_bbx(pred_box3d_tensor)
+            keep_index_2 = box_utils.remove_bbx_abnormal_z(pred_box3d_tensor)
+            keep_index = torch.logical_and(keep_index_1, keep_index_2)
+
+            pred_box3d_tensor = pred_box3d_tensor[keep_index]
+            scores = scores[keep_index]
+
+            # nms
+            keep_index = box_utils.nms_rotated(pred_box3d_tensor,
+                                               scores,
+                                               self.params['nms_thresh']
+                                               )
+
+            pred_box3d_tensor = pred_box3d_tensor[keep_index]
+
+            # select cooresponding score
+            scores = scores[keep_index]
+
+            # filter out the prediction out of the range.
+            mask = \
+                box_utils.get_mask_for_boxes_within_range_torch(pred_box3d_tensor)
+            pred_box3d_tensor = pred_box3d_tensor[mask, :, :]
+            scores = scores[mask]
+
+            assert scores.shape[0] == pred_box3d_tensor.shape[0]
+            return pred_box3d_tensor, scores
+        else:
+            cur_idx = 0
+            batch_pred_boxes3d = []
+            batch_scores = []
+            for n in batch_num_box_count:
+                cur_boxes = pred_box3d_tensor[cur_idx:cur_idx+n]
+                cur_scores = scores[cur_idx:cur_idx+n]
+                # nms
+                keep_index = box_utils.nms_rotated(cur_boxes,
+                                                   cur_scores,
+                                                   self.params['nms_thresh']
+                                                   )
+                cur_boxes = pred_box3d_original[cur_idx:cur_idx+n] # [:, [0, 1, 2, 5, 4, 3, 6]] # hwl -> lwh
+                batch_pred_boxes3d.append(cur_boxes[keep_index])
+                batch_scores.append(cur_scores[keep_index])
+                cur_idx += n
+
+            return batch_pred_boxes3d, batch_scores
+
diff --git a/opencood/opencood/data_utils/post_processor/fpvrcnn_postprocessor.py b/opencood/opencood/data_utils/post_processor/fpvrcnn_postprocessor.py
new file mode 100644
index 00000000..ec3a1b3d
--- /dev/null
+++ b/opencood/opencood/data_utils/post_processor/fpvrcnn_postprocessor.py
@@ -0,0 +1,204 @@
+"""
+3D Anchor Generator for Voxel
+"""
+import numpy as np
+import torch
+
+from opencood.data_utils.post_processor.voxel_postprocessor \
+    import VoxelPostprocessor
+from opencood.utils import box_utils
+from opencood.utils import common_utils
+
+
+class FpvrcnnPostprocessor(VoxelPostprocessor):
+    def __init__(self, anchor_params, train):
+        super(FpvrcnnPostprocessor, self).__init__(anchor_params, train)
+
+    def post_process(self, data_dict, output_dict, stage1=False):
+        if stage1:
+            return self.post_process_stage1(data_dict, output_dict)
+        else:
+            return self.post_process_stage2(data_dict)
+
+    def post_process_stage1(self, data_dict, output_dict):
+        """
+        Process the outputs of the model to 2D/3D bounding box.
+        Step1: convert each cav's output to bounding box format
+        Step2: project the bounding boxes to ego space.
+        Step:3 NMS
+
+        Parameters
+        ----------
+        data_dict : dict
+            The dictionary containing the origin input data of model.
+
+        output_dict :dict
+            The dictionary containing the output of the model.
+
+        Returns
+        -------
+        pred_box3d_tensor : torch.Tensor
+            The prediction bounding box tensor after NMS.
+        gt_box3d_tensor : torch.Tensor
+            The groundtruth bounding box tensor.
+        """
+        # the final bounding box list
+        pred_box3d_original_list = []
+        pred_box3d_list = []
+        pred_box2d_list = []
+
+        for cav_id, cav_content in data_dict.items():
+            assert cav_id in output_dict
+            # the transformation matrix to ego space
+            if 'transformation_matrix' in cav_content:
+                transformation_matrix = cav_content['transformation_matrix']
+            else:
+                transformation_matrix = torch.from_numpy(np.identity(4)).float().\
+                    to(cav_content['anchor_box'].device)
+
+            # (H, W, anchor_num, 7)
+            anchor_box = cav_content['anchor_box']
+
+            # prediction result
+            preds_dict = output_dict[cav_id]['preds_dict_stage1']
+
+            # preds
+            prob = preds_dict['cls_preds']
+            prob = torch.sigmoid(prob.permute(0, 2, 3, 1).contiguous())
+            reg = preds_dict['box_preds'].permute(0, 2, 3, 1).contiguous()
+            iou = torch.sigmoid(preds_dict['iou_preds'].permute(0, 2, 3, 1).contiguous()).reshape(1, -1)
+            dir = preds_dict['dir_cls_preds'].permute(0, 2, 3, 1).contiguous().reshape(1, -1, 2)
+
+            # convert regression map back to bounding box
+            # (N, W*L*anchor_num, 7)
+            batch_box3d = self.delta_to_boxes3d(reg, anchor_box, False)
+            mask = torch.gt(prob, self.params['target_args']['score_threshold'])
+            batch_num_box_count = [int(m.sum()) for m in mask]
+            mask = mask.view(1, -1)
+            mask_reg = mask.unsqueeze(2).repeat(1, 1, 7)
+
+            # during validation/testing, the batch size should be 1
+            if not self.train:
+                assert batch_box3d.shape[0] == 1
+
+            boxes3d = torch.masked_select(batch_box3d.view(-1, 7), mask_reg[0]).view(-1, 7) # hwl
+            scores = torch.masked_select(prob.view(-1), mask[0])
+
+            dir_labels = torch.max(dir, dim=-1)[1]
+            dir_labels = dir_labels[mask]
+            # top_labels = torch.zeros([scores.shape[0]], dtype=torch.long).cuda()
+            if scores.shape[0] != 0:
+                iou = torch.clamp(iou, min=0.0, max=1.0)
+                iou = (iou + 1) * 0.5
+                scores = scores * torch.pow(iou.masked_select(mask), 4)
+                # correct_direction
+                top_labels = (boxes3d[..., -1] > 0) ^ (dir_labels.byte() == 1)
+                boxes3d[..., -1] += torch.where(top_labels, torch.tensor(np.pi).type_as(boxes3d),
+                                                  torch.tensor(0.0).type_as(boxes3d))
+
+                # filter invalid boxes
+                keep_idx = torch.logical_and((boxes3d[:, 3:6] > 1).all(dim=1), (boxes3d[:, 3:6] < 10).all(dim=1))
+                idx_start = 0
+                count = []
+                for i, n in enumerate(batch_num_box_count):
+                    count.append(int(keep_idx[idx_start:idx_start+n].sum()))
+                batch_num_box_count = count
+                boxes3d = boxes3d[keep_idx]
+                scores = scores[keep_idx]
+                
+                # if the number of boxes is too huge, this would consume a lot of memory in the second stage
+                # therefore, randomly select some boxes if the box number is too big at the beginning of the training
+                if len(boxes3d) > 300:
+                    keep_idx = torch.multinomial(scores, 300)
+                    idx_start = 0
+                    count = []
+                    for i, n in enumerate(batch_num_box_count):
+                        count.append(int(torch.logical_and(keep_idx>=idx_start, keep_idx<idx_start + n).sum()))
+                    batch_num_box_count = count
+                    boxes3d = boxes3d[keep_idx]
+                    scores = scores[keep_idx]
+
+                pred_box3d_original_list.append(boxes3d.detach())
+
+            # convert output to bounding box
+            if len(boxes3d) != 0:
+                # (N, 8, 3)
+                boxes3d_corner = box_utils.boxes_to_corners_3d(boxes3d, order=self.params['order'])
+                # (N, 8, 3)
+                projected_boxes3d = box_utils.project_box3d(boxes3d_corner, transformation_matrix)
+                # convert 3d bbx to 2d, (N,4)
+                projected_boxes2d = box_utils.corner_to_standup_box_torch(projected_boxes3d)
+                # (N, 5)
+                boxes2d_score = torch.cat((projected_boxes2d, scores.unsqueeze(1)), dim=1)
+
+                pred_box2d_list.append(boxes2d_score)
+                pred_box3d_list.append(projected_boxes3d)
+
+        if len(pred_box2d_list) ==0 or len(pred_box3d_list) == 0:
+            return None, None
+        # shape: (N, 5)
+        pred_box2d_list = torch.vstack(pred_box2d_list)
+        # scores
+        scores = pred_box2d_list[:, -1]
+        # predicted 3d bbx
+        pred_box3d_tensor = torch.vstack(pred_box3d_list)
+        pred_box3d_original = torch.vstack(pred_box3d_original_list)
+
+        cur_idx = 0
+        batch_pred_boxes3d = []
+        batch_scores = []
+        for n in batch_num_box_count:
+            cur_boxes = pred_box3d_tensor[cur_idx:cur_idx+n]
+            cur_scores = scores[cur_idx:cur_idx+n]
+            # nms
+            keep_index = box_utils.nms_rotated(cur_boxes,
+                                               cur_scores,
+                                               self.params['nms_thresh']
+                                               )
+            cur_boxes = pred_box3d_original[cur_idx:cur_idx+n][:, [0, 1, 2, 5, 4, 3, 6]] # hwl -> lwh
+            batch_pred_boxes3d.append(cur_boxes[keep_index])
+            batch_scores.append(cur_scores[keep_index])
+            cur_idx += n
+
+        return batch_pred_boxes3d, batch_scores
+
+    def post_process_stage2(self, data_dict):
+        from opencood.pcdet_utils.iou3d_nms.iou3d_nms_utils import nms_gpu
+
+        output_dict = data_dict['ego']['fpvrcnn_out']
+        label_dict = data_dict['ego']['rcnn_label_dict']
+        rcnn_cls = output_dict['rcnn_cls'].sigmoid().view(-1)
+        rcnn_iou = output_dict['rcnn_iou'].view(-1)
+        rcnn_score = rcnn_cls * rcnn_iou**4
+        rcnn_reg = output_dict['rcnn_reg'].view(-1, 7)
+        rois_anchor = label_dict['rois_anchor']
+        rois = label_dict['rois']
+        roi_center = rois[:, 0:3]
+        roi_ry = rois[:, 6] % (2 * np.pi)
+
+        boxes_local = box_utils.box_decode(rcnn_reg, rois_anchor)
+        # boxes_local = rcnn_reg + rois_anchor
+        detections = common_utils.rotate_points_along_z(
+            points=boxes_local.view(-1, 1, boxes_local.shape[-1]), angle=roi_ry.view(-1)
+        ).view(-1, boxes_local.shape[-1])
+        detections[:, :3] = detections[:, :3] + roi_center
+        detections[:, 6] = detections[:, 6] + roi_ry
+        mask = rcnn_score >= 0.01
+        detections = detections[mask]
+        scores = rcnn_score[mask]
+        # gt_boxes = label_dict['gt_of_rois_src'][mask]
+        mask = nms_gpu(detections, scores, thresh=0.01)[0]
+        boxes3d = detections[mask][:, [0, 1, 2, 5, 4, 3, 6]] # lwh -> hwl
+        projected_boxes3d = None
+        if len(boxes3d) != 0:
+            # (N, 8, 3)
+            boxes3d_corner = \
+                box_utils.boxes_to_corners_3d(boxes3d,
+                                              order=self.params['order'])
+            # (N, 8, 3)
+            projected_boxes3d = \
+                box_utils.project_box3d(boxes3d_corner,
+                                        data_dict['ego']['transformation_matrix'])
+
+        return projected_boxes3d, scores[mask]
+
diff --git a/opencood/opencood/data_utils/post_processor/voxel_postprocessor.py b/opencood/opencood/data_utils/post_processor/voxel_postprocessor.py
new file mode 100644
index 00000000..a936305b
--- /dev/null
+++ b/opencood/opencood/data_utils/post_processor/voxel_postprocessor.py
@@ -0,0 +1,433 @@
+# -*- coding: utf-8 -*-
+# Author: Runsheng Xu <rxx3386@ucla.edu>, OpenPCDet
+# License: TDG-Attribution-NonCommercial-NoDistrib
+
+
+"""
+3D Anchor Generator for Voxel
+"""
+import math
+import sys
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+
+from opencood.data_utils.post_processor.base_postprocessor \
+    import BasePostprocessor
+from opencood.utils import box_utils
+from opencood.utils.box_overlaps import bbox_overlaps
+from opencood.visualization import vis_utils
+
+
+class VoxelPostprocessor(BasePostprocessor):
+    def __init__(self, anchor_params, train):
+        super(VoxelPostprocessor, self).__init__(anchor_params, train)
+        self.anchor_num = self.params['anchor_args']['num']
+
+    def generate_anchor_box(self):
+        W = self.params['anchor_args']['W']
+        H = self.params['anchor_args']['H']
+
+        l = self.params['anchor_args']['l']
+        w = self.params['anchor_args']['w']
+        h = self.params['anchor_args']['h']
+        r = self.params['anchor_args']['r']
+
+        assert self.anchor_num == len(r)
+        r = [math.radians(ele) for ele in r]
+
+        vh = self.params['anchor_args']['vh']
+        vw = self.params['anchor_args']['vw']
+
+        xrange = [self.params['anchor_args']['cav_lidar_range'][0],
+                  self.params['anchor_args']['cav_lidar_range'][3]]
+        yrange = [self.params['anchor_args']['cav_lidar_range'][1],
+                  self.params['anchor_args']['cav_lidar_range'][4]]
+
+        if 'feature_stride' in self.params['anchor_args']:
+            feature_stride = self.params['anchor_args']['feature_stride']
+        else:
+            feature_stride = 2
+
+        x = np.linspace(xrange[0] + vw, xrange[1] - vw, W // feature_stride)
+        y = np.linspace(yrange[0] + vh, yrange[1] - vh, H // feature_stride)
+
+        cx, cy = np.meshgrid(x, y)
+        cx = np.tile(cx[..., np.newaxis], self.anchor_num)
+        cy = np.tile(cy[..., np.newaxis], self.anchor_num)
+        cz = np.ones_like(cx) * -1.0
+
+        w = np.ones_like(cx) * w
+        l = np.ones_like(cx) * l
+        h = np.ones_like(cx) * h
+
+        r_ = np.ones_like(cx)
+        for i in range(self.anchor_num):
+            r_[..., i] = r[i]
+
+        if self.params['order'] == 'hwl':
+            anchors = np.stack([cx, cy, cz, h, w, l, r_], axis=-1)
+        elif self.params['order'] == 'lhw':
+            anchors = np.stack([cx, cy, cz, l, h, w, r_], axis=-1)
+        else:
+            sys.exit('Unknown bbx order.')
+
+        return anchors
+
+    def generate_label(self, **kwargs):
+        """
+        Generate targets for training.
+
+        Parameters
+        ----------
+        argv : list
+            gt_box_center:(max_num, 7), anchor:(H, W, anchor_num, 7)
+
+        Returns
+        -------
+        label_dict : dict
+            Dictionary that contains all target related info.
+        """
+        assert self.params['order'] == 'hwl', 'Currently Voxel only support' \
+                                              'hwl bbx order.'
+        # (max_num, 7)
+        gt_box_center = kwargs['gt_box_center']
+        # (H, W, anchor_num, 7)
+        anchors = kwargs['anchors']
+        # (max_num)
+        masks = kwargs['mask']
+
+        # (H, W)
+        feature_map_shape = anchors.shape[:2]
+
+        # (H*W*anchor_num, 7)
+        anchors = anchors.reshape(-1, 7)
+        # normalization factor, (H * W * anchor_num)
+        anchors_d = np.sqrt(anchors[:, 4] ** 2 + anchors[:, 5] ** 2)
+
+        # (H, W, 2)
+        pos_equal_one = np.zeros((*feature_map_shape, self.anchor_num))
+        neg_equal_one = np.zeros((*feature_map_shape, self.anchor_num))
+        # (H, W, self.anchor_num * 7)
+        targets = np.zeros((*feature_map_shape, self.anchor_num * 7))
+
+        # (n, 7)
+        gt_box_center_valid = gt_box_center[masks == 1]
+        # (n, 8, 3)
+        gt_box_corner_valid = \
+            box_utils.boxes_to_corners_3d(gt_box_center_valid,
+                                          self.params['order'])
+        # (H*W*anchor_num, 8, 3)
+        anchors_corner = \
+            box_utils.boxes_to_corners_3d(anchors,
+                                          order=self.params['order'])
+        # (H*W*anchor_num, 4)
+        anchors_standup_2d = \
+            box_utils.corner2d_to_standup_box(anchors_corner)
+        # (n, 4)
+        gt_standup_2d = \
+            box_utils.corner2d_to_standup_box(gt_box_corner_valid)
+
+        # (H*W*anchor_n)
+        iou = bbox_overlaps(
+            np.ascontiguousarray(anchors_standup_2d).astype(np.float32),
+            np.ascontiguousarray(gt_standup_2d).astype(np.float32),
+        )
+
+        # the anchor boxes has the largest iou across
+        # shape: (n)
+        id_highest = np.argmax(iou.T, axis=1)
+        # [0, 1, 2, ..., n-1]
+        id_highest_gt = np.arange(iou.T.shape[0])
+        # make sure all highest iou is larger than 0
+        mask = iou.T[id_highest_gt, id_highest] > 0
+        id_highest, id_highest_gt = id_highest[mask], id_highest_gt[mask]
+
+        # find anchors iou > params['pos_iou']
+        id_pos, id_pos_gt = \
+            np.where(iou >
+                     self.params['target_args']['pos_threshold'])
+        #  find anchors iou < params['neg_iou']
+        id_neg = np.where(np.sum(iou <
+                                 self.params['target_args']['neg_threshold'],
+                                 axis=1) == iou.shape[1])[0]
+        id_pos = np.concatenate([id_pos, id_highest])
+        id_pos_gt = np.concatenate([id_pos_gt, id_highest_gt])
+        id_pos, index = np.unique(id_pos, return_index=True)
+        id_pos_gt = id_pos_gt[index]
+        id_neg.sort()
+
+        # cal the target and set the equal one
+        index_x, index_y, index_z = np.unravel_index(
+            id_pos, (*feature_map_shape, self.anchor_num))
+        pos_equal_one[index_x, index_y, index_z] = 1
+
+        # calculate the targets
+        targets[index_x, index_y, np.array(index_z) * 7] = \
+            (gt_box_center[id_pos_gt, 0] - anchors[id_pos, 0]) / anchors_d[
+                id_pos]
+        targets[index_x, index_y, np.array(index_z) * 7 + 1] = \
+            (gt_box_center[id_pos_gt, 1] - anchors[id_pos, 1]) / anchors_d[
+                id_pos]
+        targets[index_x, index_y, np.array(index_z) * 7 + 2] = \
+            (gt_box_center[id_pos_gt, 2] - anchors[id_pos, 2]) / anchors[
+                id_pos, 3]
+        targets[index_x, index_y, np.array(index_z) * 7 + 3] = np.log(
+            gt_box_center[id_pos_gt, 3] / anchors[id_pos, 3])
+        targets[index_x, index_y, np.array(index_z) * 7 + 4] = np.log(
+            gt_box_center[id_pos_gt, 4] / anchors[id_pos, 4])
+        targets[index_x, index_y, np.array(index_z) * 7 + 5] = np.log(
+            gt_box_center[id_pos_gt, 5] / anchors[id_pos, 5])
+        targets[index_x, index_y, np.array(index_z) * 7 + 6] = (
+                gt_box_center[id_pos_gt, 6] - anchors[id_pos, 6])
+
+        index_x, index_y, index_z = np.unravel_index(
+            id_neg, (*feature_map_shape, self.anchor_num))
+        neg_equal_one[index_x, index_y, index_z] = 1
+
+        # to avoid a box be pos/neg in the same time
+        index_x, index_y, index_z = np.unravel_index(
+            id_highest, (*feature_map_shape, self.anchor_num))
+        neg_equal_one[index_x, index_y, index_z] = 0
+
+        label_dict = {'pos_equal_one': pos_equal_one,
+                      'neg_equal_one': neg_equal_one,
+                      'targets': targets}
+
+        return label_dict
+
+    @staticmethod
+    def collate_batch(label_batch_list):
+        """
+        Customized collate function for target label generation.
+
+        Parameters
+        ----------
+        label_batch_list : list
+            The list of dictionary  that contains all labels for several
+            frames.
+
+        Returns
+        -------
+        target_batch : dict
+            Reformatted labels in torch tensor.
+        """
+        pos_equal_one = []
+        neg_equal_one = []
+        targets = []
+
+        for i in range(len(label_batch_list)):
+            pos_equal_one.append(label_batch_list[i]['pos_equal_one'])
+            neg_equal_one.append(label_batch_list[i]['neg_equal_one'])
+            targets.append(label_batch_list[i]['targets'])
+
+        pos_equal_one = \
+            torch.from_numpy(np.array(pos_equal_one))
+        neg_equal_one = \
+            torch.from_numpy(np.array(neg_equal_one))
+        targets = \
+            torch.from_numpy(np.array(targets))
+
+        return {'targets': targets,
+                'pos_equal_one': pos_equal_one,
+                'neg_equal_one': neg_equal_one}
+
+    def post_process(self, data_dict, output_dict):
+        """
+        Process the outputs of the model to 2D/3D bounding box.
+        Step1: convert each cav's output to bounding box format
+        Step2: project the bounding boxes to ego space.
+        Step:3 NMS
+
+        Parameters
+        ----------
+        data_dict : dict
+            The dictionary containing the origin input data of model.
+
+        output_dict :dict
+            The dictionary containing the output of the model.
+
+        Returns
+        -------
+        pred_box3d_tensor : torch.Tensor
+            The prediction bounding box tensor after NMS.
+        gt_box3d_tensor : torch.Tensor
+            The groundtruth bounding box tensor.
+        """
+        # the final bounding box list
+        pred_box3d_list = []
+        pred_box2d_list = []
+
+        for cav_id, cav_content in data_dict.items():
+            assert cav_id in output_dict
+            # the transformation matrix to ego space
+            transformation_matrix = cav_content['transformation_matrix']
+
+            # (H, W, anchor_num, 7)
+            anchor_box = cav_content['anchor_box']
+
+            # classification probability
+            prob = output_dict[cav_id]['psm']
+            prob = F.sigmoid(prob.permute(0, 2, 3, 1))
+            prob = prob.reshape(1, -1)
+
+            # regression map
+            reg = output_dict[cav_id]['rm']
+
+            # convert regression map back to bounding box
+            # (N, W*L*anchor_num, 7)
+            batch_box3d = self.delta_to_boxes3d(reg, anchor_box)
+            mask = \
+                torch.gt(prob, self.params['target_args']['score_threshold'])
+            mask = mask.view(1, -1)
+            mask_reg = mask.unsqueeze(2).repeat(1, 1, 7)
+
+            # during validation/testing, the batch size should be 1
+            assert batch_box3d.shape[0] == 1
+            boxes3d = torch.masked_select(batch_box3d[0],
+                                          mask_reg[0]).view(-1, 7)
+            scores = torch.masked_select(prob[0], mask[0])
+
+            # convert output to bounding box
+            if len(boxes3d) != 0:
+                # (N, 8, 3)
+                boxes3d_corner = \
+                    box_utils.boxes_to_corners_3d(boxes3d,
+                                                  order=self.params['order'])
+                # (N, 8, 3)
+                projected_boxes3d = \
+                    box_utils.project_box3d(boxes3d_corner,
+                                            transformation_matrix)
+                # convert 3d bbx to 2d, (N,4)
+                projected_boxes2d = \
+                    box_utils.corner_to_standup_box_torch(projected_boxes3d)
+                # (N, 5)
+                boxes2d_score = \
+                    torch.cat((projected_boxes2d, scores.unsqueeze(1)), dim=1)
+
+                pred_box2d_list.append(boxes2d_score)
+                pred_box3d_list.append(projected_boxes3d)
+
+        if len(pred_box2d_list) ==0 or len(pred_box3d_list) == 0:
+            return None, None
+        # shape: (N, 5)
+        pred_box2d_list = torch.vstack(pred_box2d_list)
+        # scores
+        scores = pred_box2d_list[:, -1]
+        # predicted 3d bbx
+        pred_box3d_tensor = torch.vstack(pred_box3d_list)
+        # remove large bbx
+        keep_index_1 = box_utils.remove_large_pred_bbx(pred_box3d_tensor)
+        keep_index_2 = box_utils.remove_bbx_abnormal_z(pred_box3d_tensor)
+        keep_index = torch.logical_and(keep_index_1, keep_index_2)
+
+        pred_box3d_tensor = pred_box3d_tensor[keep_index]
+        scores = scores[keep_index]
+
+        # nms
+        keep_index = box_utils.nms_rotated(pred_box3d_tensor,
+                                           scores,
+                                           self.params['nms_thresh']
+                                           )
+
+        pred_box3d_tensor = pred_box3d_tensor[keep_index]
+
+        # select cooresponding score
+        scores = scores[keep_index]
+
+        # filter out the prediction out of the range.
+        mask = \
+            box_utils.get_mask_for_boxes_within_range_torch(pred_box3d_tensor)
+        pred_box3d_tensor = pred_box3d_tensor[mask, :, :]
+        scores = scores[mask]
+
+        assert scores.shape[0] == pred_box3d_tensor.shape[0]
+
+        return pred_box3d_tensor, scores
+
+    @staticmethod
+    def delta_to_boxes3d(deltas, anchors, channel_swap=True):
+        """
+        Convert the output delta to 3d bbx.
+
+        Parameters
+        ----------
+        deltas : torch.Tensor
+            (N, W, L, 14)
+        anchors : torch.Tensor
+            (W, L, 2, 7) -> xyzhwlr
+        channel_swap : bool
+            Whether to swap the channel of deltas. It is only false when using
+            FPV-RCNN
+
+        Returns
+        -------
+        box3d : torch.Tensor
+            (N, W*L*2, 7)
+        """
+        # batch size
+        N = deltas.shape[0]
+        if channel_swap:
+            deltas = deltas.permute(0, 2, 3, 1).contiguous().view(N, -1, 7)
+        else:
+            deltas = deltas.contiguous().view(N, -1, 7)
+
+        boxes3d = torch.zeros_like(deltas)
+        if deltas.is_cuda:
+            anchors = anchors.cuda()
+            boxes3d = boxes3d.cuda()
+
+        # (W*L*2, 7)
+        anchors_reshaped = anchors.view(-1, 7).float()
+        # the diagonal of the anchor 2d box, (W*L*2)
+        anchors_d = torch.sqrt(
+            anchors_reshaped[:, 4] ** 2 + anchors_reshaped[:, 5] ** 2)
+        anchors_d = anchors_d.repeat(N, 2, 1).transpose(1, 2)
+        anchors_reshaped = anchors_reshaped.repeat(N, 1, 1)
+
+        # Inv-normalize to get xyz
+        boxes3d[..., [0, 1]] = torch.mul(deltas[..., [0, 1]], anchors_d) + \
+                               anchors_reshaped[..., [0, 1]]
+        boxes3d[..., [2]] = torch.mul(deltas[..., [2]],
+                                      anchors_reshaped[..., [3]]) + \
+                            anchors_reshaped[..., [2]]
+        # hwl
+        boxes3d[..., [3, 4, 5]] = torch.exp(
+            deltas[..., [3, 4, 5]]) * anchors_reshaped[..., [3, 4, 5]]
+        # yaw angle
+        boxes3d[..., 6] = deltas[..., 6] + anchors_reshaped[..., 6]
+
+        return boxes3d
+
+    @staticmethod
+    def visualize(pred_box_tensor, gt_tensor, pcd, show_vis, save_path, dataset=None):
+        """
+        Visualize the prediction, ground truth with point cloud together.
+
+        Parameters
+        ----------
+        pred_box_tensor : torch.Tensor
+            (N, 8, 3) prediction.
+
+        gt_tensor : torch.Tensor
+            (N, 8, 3) groundtruth bbx
+
+        pcd : torch.Tensor
+            PointCloud, (N, 4).
+
+        show_vis : bool
+            Whether to show visualization.
+
+        save_path : str
+            Save the visualization results to given path.
+
+        dataset : BaseDataset
+            opencood dataset object.
+
+        """
+        vis_utils.visualize_single_sample_output_gt(pred_box_tensor,
+                                                    gt_tensor,
+                                                    pcd,
+                                                    show_vis,
+                                                    save_path)
diff --git a/opencood/opencood/data_utils/pre_processor/__init__.py b/opencood/opencood/data_utils/pre_processor/__init__.py
new file mode 100644
index 00000000..4096f05d
--- /dev/null
+++ b/opencood/opencood/data_utils/pre_processor/__init__.py
@@ -0,0 +1,32 @@
+# -*- coding: utf-8 -*-
+# Author: Runsheng Xu <rxx3386@ucla.edu>
+# License: TDG-Attribution-NonCommercial-NoDistrib
+
+from opencood.data_utils.pre_processor.base_preprocessor import BasePreprocessor
+from opencood.data_utils.pre_processor.voxel_preprocessor import VoxelPreprocessor
+from opencood.data_utils.pre_processor.bev_preprocessor import BevPreprocessor
+from opencood.data_utils.pre_processor.sp_voxel_preprocessor import SpVoxelPreprocessor
+
+__all__ = {
+    'BasePreprocessor': BasePreprocessor,
+    'VoxelPreprocessor': VoxelPreprocessor,
+    'BevPreprocessor': BevPreprocessor,
+    'SpVoxelPreprocessor': SpVoxelPreprocessor
+}
+
+
+def build_preprocessor(preprocess_cfg, train):
+    process_method_name = preprocess_cfg['core_method']
+    error_message = f"{process_method_name} is not found. " \
+                     f"Please add your processor file's name in opencood/" \
+                     f"data_utils/processor/init.py"
+    assert process_method_name in ['BasePreprocessor', 'VoxelPreprocessor',
+                                   'BevPreprocessor', 'SpVoxelPreprocessor'], \
+        error_message
+
+    processor = __all__[process_method_name](
+        preprocess_params=preprocess_cfg,
+        train=train
+    )
+
+    return processor
diff --git a/opencood/opencood/data_utils/pre_processor/base_preprocessor.py b/opencood/opencood/data_utils/pre_processor/base_preprocessor.py
new file mode 100644
index 00000000..34215183
--- /dev/null
+++ b/opencood/opencood/data_utils/pre_processor/base_preprocessor.py
@@ -0,0 +1,79 @@
+# -*- coding: utf-8 -*-
+# Author: Runsheng Xu <rxx3386@ucla.edu>
+# License: TDG-Attribution-NonCommercial-NoDistrib
+
+import numpy as np
+
+from opencood.utils import pcd_utils
+
+
+class BasePreprocessor(object):
+    """
+    Basic Lidar pre-processor.
+
+    Parameters
+    ----------
+    preprocess_params : dict
+        The dictionary containing all parameters of the preprocessing.
+
+    train : bool
+        Train or test mode.
+    """
+
+    def __init__(self, preprocess_params, train):
+        self.params = preprocess_params
+        self.train = train
+
+    def preprocess(self, pcd_np):
+        """
+        Preprocess the lidar points by simple sampling.
+
+        Parameters
+        ----------
+        pcd_np : np.ndarray
+            The raw lidar.
+
+        Returns
+        -------
+        data_dict : the output dictionary.
+        """
+        data_dict = {}
+        sample_num = self.params['args']['sample_num']
+
+        pcd_np = pcd_utils.downsample_lidar(pcd_np, sample_num)
+        data_dict['downsample_lidar'] = pcd_np
+
+        return data_dict
+
+    def project_points_to_bev_map(self, points, ratio=0.1):
+        """
+        Project points to BEV occupancy map with default ratio=0.1.
+
+        Parameters
+        ----------
+        points : np.ndarray
+            (N, 3) / (N, 4)
+
+        ratio : float
+            Discretization parameters. Default is 0.1.
+
+        Returns
+        -------
+        bev_map : np.ndarray
+            BEV occupancy map including projected points with shape
+            (img_row, img_col).
+
+        """
+        L1, W1, H1, L2, W2, H2 = self.params["cav_lidar_range"]
+        img_row = int((L2 - L1) / ratio)
+        img_col = int((W2 - W1) / ratio)
+        bev_map = np.zeros((img_row, img_col))
+        bev_origin = np.array([L1, W1, H1]).reshape(1, -1)
+        # (N, 3)
+        indices = ((points[:, :3] - bev_origin) / ratio).astype(int)
+        mask = np.logical_and(indices[:, 0] > 0, indices[:, 0] < img_row)
+        mask = np.logical_and(mask, np.logical_and(indices[:, 1] > 0,
+                                                   indices[:, 1] < img_col))
+        indices = indices[mask, :]
+        bev_map[indices[:, 0], indices[:, 1]] = 1
+        return bev_map
diff --git a/opencood/opencood/data_utils/pre_processor/bev_preprocessor.py b/opencood/opencood/data_utils/pre_processor/bev_preprocessor.py
new file mode 100644
index 00000000..79ba0dad
--- /dev/null
+++ b/opencood/opencood/data_utils/pre_processor/bev_preprocessor.py
@@ -0,0 +1,124 @@
+# -*- coding: utf-8 -*-
+# Author: Hao Xiang <haxiang@g.ucla.edu>
+# License: TDG-Attribution-NonCommercial-NoDistrib
+
+
+"""
+Convert lidar to bev
+"""
+
+import numpy as np
+import torch
+from opencood.data_utils.pre_processor.base_preprocessor import \
+    BasePreprocessor
+
+
+class BevPreprocessor(BasePreprocessor):
+    def __init__(self, preprocess_params, train):
+        super(BevPreprocessor, self).__init__(preprocess_params, train)
+        self.lidar_range = self.params['cav_lidar_range']
+        self.geometry_param = preprocess_params["geometry_param"]
+
+    def preprocess(self, pcd_raw):
+        """
+        Preprocess the lidar points to BEV representations.
+
+        Parameters
+        ----------
+        pcd_raw : np.ndarray
+            The raw lidar.
+
+        Returns
+        -------
+        data_dict : the structured output dictionary.
+        """
+        bev = np.zeros(self.geometry_param['input_shape'], dtype=np.float32)
+        intensity_map_count = np.zeros((bev.shape[0], bev.shape[1]),
+                                       dtype=np.int)
+        bev_origin = np.array(
+            [self.geometry_param["L1"], self.geometry_param["W1"],
+             self.geometry_param["H1"]]).reshape(1, -1)
+
+        indices = ((pcd_raw[:, :3] - bev_origin) / self.geometry_param[
+            "res"]).astype(int)
+
+        for i in range(indices.shape[0]):
+            bev[indices[i, 0], indices[i, 1], indices[i, 2]] = 1
+            bev[indices[i, 0], indices[i, 1], -1] += pcd_raw[i, 3]
+            intensity_map_count[indices[i, 0], indices[i, 1]] += 1
+        divide_mask = intensity_map_count != 0
+        bev[divide_mask, -1] = np.divide(bev[divide_mask, -1],
+                                         intensity_map_count[divide_mask])
+
+        data_dict = {
+            "bev_input": np.transpose(bev, (2, 0, 1))
+        }
+        return data_dict
+
+    @staticmethod
+    def collate_batch_list(batch):
+        """
+        Customized pytorch data loader collate function.
+
+        Parameters
+        ----------
+        batch : list
+            List of dictionary. Each dictionary represent a single frame.
+
+        Returns
+        -------
+        processed_batch : dict
+            Updated lidar batch.
+        """
+        bev_input_list = [
+            x["bev_input"][np.newaxis, ...] for x in batch
+        ]
+        processed_batch = {
+            "bev_input": torch.from_numpy(
+                np.concatenate(bev_input_list, axis=0))
+        }
+        return processed_batch
+
+    @staticmethod
+    def collate_batch_dict(batch):
+        """
+        Customized pytorch data loader collate function.
+
+        Parameters
+        ----------
+        batch : dict
+            Dict of list. Each element represents a CAV.
+
+        Returns
+        -------
+        processed_batch : dict
+            Updated lidar batch.
+        """
+        bev_input_list = [
+            x[np.newaxis, ...] for x in batch["bev_input"]
+        ]
+        processed_batch = {
+            "bev_input": torch.from_numpy(
+                np.concatenate(bev_input_list, axis=0))
+        }
+        return processed_batch
+
+    def collate_batch(self, batch):
+        """
+        Customized pytorch data loader collate function.
+
+        Parameters
+        ----------
+        batch : list / dict
+            Batched data.
+        Returns
+        -------
+        processed_batch : dict
+            Updated lidar batch.
+        """
+        if isinstance(batch, list):
+            return self.collate_batch_list(batch)
+        elif isinstance(batch, dict):
+            return self.collate_batch_dict(batch)
+        else:
+            raise NotImplemented
diff --git a/opencood/opencood/data_utils/pre_processor/sp_voxel_preprocessor.py b/opencood/opencood/data_utils/pre_processor/sp_voxel_preprocessor.py
new file mode 100644
index 00000000..4066241b
--- /dev/null
+++ b/opencood/opencood/data_utils/pre_processor/sp_voxel_preprocessor.py
@@ -0,0 +1,171 @@
+# -*- coding: utf-8 -*-
+# Author: Runsheng Xu <rxx3386@ucla.edu>, OpenPCDet
+# License: TDG-Attribution-NonCommercial-NoDistrib
+
+"""
+Transform points to voxels using sparse conv library
+"""
+import sys
+
+import numpy as np
+import torch
+from cumm import tensorview as tv
+from opencood.data_utils.pre_processor.base_preprocessor import \
+    BasePreprocessor
+
+
+class SpVoxelPreprocessor(BasePreprocessor):
+    def __init__(self, preprocess_params, train):
+        super(SpVoxelPreprocessor, self).__init__(preprocess_params,
+                                                  train)
+        self.spconv = 1
+        try:
+            # spconv v1.x
+            from spconv.utils import VoxelGeneratorV2 as VoxelGenerator
+        except:
+            # spconv v2.x
+            from spconv.utils import Point2VoxelCPU3d as VoxelGenerator
+            self.spconv = 2
+        self.lidar_range = self.params['cav_lidar_range']
+        self.voxel_size = self.params['args']['voxel_size']
+        self.max_points_per_voxel = self.params['args']['max_points_per_voxel']
+
+        if train:
+            self.max_voxels = self.params['args']['max_voxel_train']
+        else:
+            self.max_voxels = self.params['args']['max_voxel_test']
+
+        grid_size = (np.array(self.lidar_range[3:6]) -
+                     np.array(self.lidar_range[0:3])) / np.array(self.voxel_size)
+        self.grid_size = np.round(grid_size).astype(np.int64)
+
+        # use sparse conv library to generate voxel
+        if self.spconv == 1:
+            self.voxel_generator = VoxelGenerator(
+                voxel_size=self.voxel_size,
+                point_cloud_range=self.lidar_range,
+                max_num_points=self.max_points_per_voxel,
+                max_voxels=self.max_voxels
+            )
+        else:
+            self.voxel_generator = VoxelGenerator(
+                vsize_xyz=self.voxel_size,
+                coors_range_xyz=self.lidar_range,
+                max_num_points_per_voxel=self.max_points_per_voxel,
+                num_point_features=4,
+                max_num_voxels=self.max_voxels
+            )
+
+    def preprocess(self, pcd_np):
+        data_dict = {}
+        if self.spconv == 1:
+            voxel_output = self.voxel_generator.generate(pcd_np)
+        else:
+            pcd_tv = tv.from_numpy(pcd_np)
+            voxel_output = self.voxel_generator.point_to_voxel(pcd_tv)
+        if isinstance(voxel_output, dict):
+            voxels, coordinates, num_points = \
+                voxel_output['voxels'], voxel_output['coordinates'], \
+                voxel_output['num_points_per_voxel']
+        else:
+            voxels, coordinates, num_points = voxel_output
+
+        if self.spconv == 2:
+            voxels = voxels.numpy()
+            coordinates = coordinates.numpy()
+            num_points = num_points.numpy()
+
+        data_dict['voxel_features'] = voxels
+        data_dict['voxel_coords'] = coordinates
+        data_dict['voxel_num_points'] = num_points
+
+        return data_dict
+
+    def collate_batch(self, batch):
+        """
+        Customized pytorch data loader collate function.
+
+        Parameters
+        ----------
+        batch : list or dict
+            List or dictionary.
+
+        Returns
+        -------
+        processed_batch : dict
+            Updated lidar batch.
+        """
+
+        if isinstance(batch, list):
+            return self.collate_batch_list(batch)
+        elif isinstance(batch, dict):
+            return self.collate_batch_dict(batch)
+        else:
+            sys.exit('Batch has too be a list or a dictionarn')
+
+    @staticmethod
+    def collate_batch_list(batch):
+        """
+        Customized pytorch data loader collate function.
+
+        Parameters
+        ----------
+        batch : list
+            List of dictionary. Each dictionary represent a single frame.
+
+        Returns
+        -------
+        processed_batch : dict
+            Updated lidar batch.
+        """
+        voxel_features = []
+        voxel_num_points = []
+        voxel_coords = []
+
+        for i in range(len(batch)):
+            voxel_features.append(batch[i]['voxel_features'])
+            voxel_num_points.append(batch[i]['voxel_num_points'])
+            coords = batch[i]['voxel_coords']
+            voxel_coords.append(
+                np.pad(coords, ((0, 0), (1, 0)),
+                       mode='constant', constant_values=i))
+
+        voxel_num_points = torch.from_numpy(np.concatenate(voxel_num_points))
+        voxel_features = torch.from_numpy(np.concatenate(voxel_features))
+        voxel_coords = torch.from_numpy(np.concatenate(voxel_coords))
+
+        return {'voxel_features': voxel_features,
+                'voxel_coords': voxel_coords,
+                'voxel_num_points': voxel_num_points}
+
+    @staticmethod
+    def collate_batch_dict(batch: dict):
+        """
+        Collate batch if the batch is a dictionary,
+        eg: {'voxel_features': [feature1, feature2...., feature n]}
+
+        Parameters
+        ----------
+        batch : dict
+
+        Returns
+        -------
+        processed_batch : dict
+            Updated lidar batch.
+        """
+        voxel_features = \
+            torch.from_numpy(np.concatenate(batch['voxel_features']))
+        voxel_num_points = \
+            torch.from_numpy(np.concatenate(batch['voxel_num_points']))
+        coords = batch['voxel_coords']
+        voxel_coords = []
+
+        for i in range(len(coords)):
+            voxel_coords.append(
+                np.pad(coords[i], ((0, 0), (1, 0)),
+                       mode='constant', constant_values=i))
+        voxel_coords = torch.from_numpy(np.concatenate(voxel_coords))
+
+        return {'voxel_features': voxel_features,
+                'voxel_coords': voxel_coords,
+                'voxel_num_points': voxel_num_points}
diff --git a/opencood/opencood/data_utils/pre_processor/voxel_preprocessor.py b/opencood/opencood/data_utils/pre_processor/voxel_preprocessor.py
new file mode 100644
index 00000000..2149ce39
--- /dev/null
+++ b/opencood/opencood/data_utils/pre_processor/voxel_preprocessor.py
@@ -0,0 +1,158 @@
+# -*- coding: utf-8 -*-
+# Author: Runsheng Xu <rxx3386@ucla.edu>
+# License: TDG-Attribution-NonCommercial-NoDistrib
+
+
+"""
+Convert lidar to voxel. This class was manually designed, and we recommend
+to use sp_voxel_preprocessor.
+"""
+import sys
+
+import numpy as np
+import torch
+
+from opencood.data_utils.pre_processor.base_preprocessor import \
+    BasePreprocessor
+
+
+class VoxelPreprocessor(BasePreprocessor):
+    def __init__(self, preprocess_params, train):
+        super(VoxelPreprocessor, self).__init__(preprocess_params, train)
+        # TODO: add intermediate lidar range later
+        self.lidar_range = self.params['cav_lidar_range']
+
+        self.vw = self.params['args']['vw']
+        self.vh = self.params['args']['vh']
+        self.vd = self.params['args']['vd']
+        self.T = self.params['args']['T']
+
+    def preprocess(self, pcd_np):
+        """
+        Preprocess the lidar points by  voxelization.
+
+        Parameters
+        ----------
+        pcd_np : np.ndarray
+            The raw lidar.
+
+        Returns
+        -------
+        data_dict : the structured output dictionary.
+        """
+        data_dict = {}
+
+        # calculate the voxel coordinates
+        voxel_coords = ((pcd_np[:, :3] -
+                         np.floor(np.array([self.lidar_range[0],
+                                            self.lidar_range[1],
+                                            self.lidar_range[2]])) / (
+                             self.vw, self.vh, self.vd))).astype(np.int32)
+
+        # convert to  (D, H, W) as the paper
+        voxel_coords = voxel_coords[:, [2, 1, 0]]
+        voxel_coords, inv_ind, voxel_counts = np.unique(voxel_coords, axis=0,
+                                                        return_inverse=True,
+                                                        return_counts=True)
+
+        voxel_features = []
+
+        for i in range(len(voxel_coords)):
+            voxel = np.zeros((self.T, 7), dtype=np.float32)
+            pts = pcd_np[inv_ind == i]
+            if voxel_counts[i] > self.T:
+                pts = pts[:self.T, :]
+                voxel_counts[i] = self.T
+
+            # augment the points
+            voxel[:pts.shape[0], :] = np.concatenate((pts, pts[:, :3] -
+                                                      np.mean(pts[:, :3], 0)),
+                                                     axis=1)
+            voxel_features.append(voxel)
+
+        data_dict['voxel_features'] = np.array(voxel_features)
+        data_dict['voxel_coords'] = voxel_coords
+
+        return data_dict
+
+    def collate_batch(self, batch):
+        """
+        Customized pytorch data loader collate function.
+
+        Parameters
+        ----------
+        batch : list or dict
+            List or dictionary.
+
+        Returns
+        -------
+        processed_batch : dict
+            Updated lidar batch.
+        """
+
+        if isinstance(batch, list):
+            return self.collate_batch_list(batch)
+        elif isinstance(batch, dict):
+            return self.collate_batch_dict(batch)
+        else:
+            sys.exit('Batch has too be a list or a dictionarn')
+
+    @staticmethod
+    def collate_batch_list(batch):
+        """
+        Customized pytorch data loader collate function.
+
+        Parameters
+        ----------
+        batch : list
+            List of dictionary. Each dictionary represent a single frame.
+
+        Returns
+        -------
+        processed_batch : dict
+            Updated lidar batch.
+        """
+        voxel_features = []
+        voxel_coords = []
+
+        for i in range(len(batch)):
+            voxel_features.append(batch[i]['voxel_features'])
+            coords = batch[i]['voxel_coords']
+            voxel_coords.append(
+                np.pad(coords, ((0, 0), (1, 0)),
+                       mode='constant', constant_values=i))
+
+        voxel_features = torch.from_numpy(np.concatenate(voxel_features))
+        voxel_coords = torch.from_numpy(np.concatenate(voxel_coords))
+
+        return {'voxel_features': voxel_features,
+                'voxel_coords': voxel_coords}
+
+    @staticmethod
+    def collate_batch_dict(batch: dict):
+        """
+        Collate batch if the batch is a dictionary,
+        eg: {'voxel_features': [feature1, feature2...., feature n]}
+
+        Parameters
+        ----------
+        batch : dict
+
+        Returns
+        -------
+        processed_batch : dict
+            Updated lidar batch.
+        """
+        voxel_features = \
+            torch.from_numpy(np.concatenate(batch['voxel_features']))
+        coords = batch['voxel_coords']
+        voxel_coords = []
+
+        for i in range(len(coords)):
+            voxel_coords.append(
+                np.pad(coords[i], ((0, 0), (1, 0)),
+                       mode='constant', constant_values=i))
+        voxel_coords = torch.from_numpy(np.concatenate(voxel_coords))
+
+        return {'voxel_features': voxel_features,
+                'voxel_coords': voxel_coords}
diff --git a/opencood/opencood/hypes_yaml/__init__.py b/opencood/opencood/hypes_yaml/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/opencood/opencood/hypes_yaml/ciassd_early_fusion.yaml b/opencood/opencood/hypes_yaml/ciassd_early_fusion.yaml
new file mode 100644
index 00000000..d8c5380e
--- /dev/null
+++ b/opencood/opencood/hypes_yaml/ciassd_early_fusion.yaml
@@ -0,0 +1,120 @@
+name: ciassd_early_fusion
+
+yaml_parser: "load_voxel_params"
+root_dir: "/media/hdd/yuan/koko/data/OPV2V/train" # "/media/hdd/yuan/data/OPV2V/train"
+validate_dir: "/media/hdd/yuan/koko/data/OPV2V/validate"
+
+train_params:
+  batch_size: &batch_size 6
+  epoches: 100
+  eval_freq: 2
+  save_freq: 2
+
+fusion:
+  core_method: 'EarlyFusionDataset' # LateFusionDataset, EarlyFusionDataset, IntermediateFusionDataset supported
+  args: []
+
+# preprocess-related
+preprocess:
+  # options: BasePreprocessor, VoxelPreprocessor, BevPreprocessor
+  core_method: 'SpVoxelPreprocessor'
+  args:
+    voxel_size: &voxel_size [0.1, 0.1, 0.1]
+    max_points_per_voxel: &T 5
+    max_voxel_train: 36000
+    max_voxel_test: 70000
+  # lidar range for each individual cav.
+  cav_lidar_range: &cav_lidar [-140.8, -40, -3, 140.8, 40, 1]
+
+data_augment:
+  - NAME: random_world_flip
+    ALONG_AXIS_LIST: [ 'x' ]
+
+  - NAME: random_world_rotation
+    WORLD_ROT_ANGLE: [ -0.78539816, 0.78539816 ]
+
+  - NAME: random_world_scaling
+    WORLD_SCALE_RANGE: [ 0.95, 1.05 ]
+
+# anchor box related
+postprocess:
+  core_method: 'CiassdPostprocessor' # VoxelPostprocessor, BevPostprocessor supported
+  predict_iou: true
+  anchor_args:
+    cav_lidar_range: *cav_lidar
+    l: 3.9
+    w: 1.6
+    h: 1.56
+    r: [0, 90]
+    num: &achor_num 2
+    feature_stride: 8
+  target_args:
+    pos_threshold: 0.6
+    neg_threshold: 0.45
+    score_threshold: 0.3
+  order: 'hwl' # hwl or lwh
+  max_num: 100 # maximum number of objects in a single frame. use this number to make sure different frames has the same dimension in the same batch
+  nms_thresh: 0.2
+
+# model related
+model:
+  core_method: ciassd # corresponding to VoxelNet
+  args:
+    N: *batch_size
+    T: *T
+    lidar_range: *cav_lidar
+    voxel_size: *voxel_size
+    anchor_num: *achor_num
+    mean_vfe:
+      num_point_features: 4
+    spconv:
+      num_features_in: 64
+      num_features_out: 64
+    map2bev:
+      feature_num: 128
+    ssfa:
+      feature_num: 128
+    head:
+      num_input: 128
+      num_pred: 14
+      num_cls: 2
+      num_iou: 2
+      use_dir: True
+      num_dir: 4
+
+loss:
+  core_method: ciassd_loss
+  args:
+    pos_cls_weight: 50.0
+    encode_rad_error_by_sin: true
+    cls:
+      type: 'SigmoidFocalLoss'
+      alpha: 0.25
+      gamma: 2.0
+      weight: 2.0
+    reg:
+      type: 'WeightedSmoothL1Loss'
+      sigma: 3.0
+      codewise: true
+      weight: 2.0
+    iou:
+      type: 'WeightedSmoothL1Loss'
+      sigma: 3.0
+      codewise: true
+      weight: 1.0
+    dir:
+      type: 'WeightedSoftmaxClassificationLoss'
+      weight: 0.2
+
+
+optimizer:
+  core_method: Adam
+  lr: 0.002
+  args:
+    eps: 1e-10
+    weight_decay: 1e-4
+
+lr_scheduler:
+  core_method: multistep #step, multistep and Exponential support
+  gamma: 0.1
+  step_size: [15, 30]
\ No newline at end of file
diff --git a/opencood/opencood/hypes_yaml/ciassd_intermediate_fusion.yaml b/opencood/opencood/hypes_yaml/ciassd_intermediate_fusion.yaml
new file mode 100644
index 00000000..5c60302e
--- /dev/null
+++ b/opencood/opencood/hypes_yaml/ciassd_intermediate_fusion.yaml
@@ -0,0 +1,89 @@
+name: ciassd
+
+yaml_parser: "load_voxel_params"
+root_dir: "/media/hdd/yuan/data/OPV2V/train"
+validate_dir: "/media/hdd/yuan/data/OPV2V/validate"
+
+train_params:
+  batch_size: &batch_size 1
+  epoches: 30
+  eval_freq: 1
+  save_freq: 1
+
+fusion:
+  core_method: 'IntermediateFusionDataset' # LateFusionDataset, EarlyFusionDataset, IntermediateFusionDataset supported
+  args: []
+
+# preprocess-related
+preprocess:
+  # options: BasePreprocessor, VoxelPreprocessor, BevPreprocessor
+  core_method: 'SpVoxelPreprocessor'
+  args:
+    voxel_size: &voxel_size [0.4, 0.4, 0.4]
+    max_points_per_voxel: &T 32
+    max_voxel_train: 36000
+    max_voxel_test: 70000
+  # lidar range for each individual cav.
+  cav_lidar_range: &cav_lidar [-140.8, -41.6, -3, 140.8, 41.6, 1]
+
+data_augment:
+  - NAME: random_world_flip
+    ALONG_AXIS_LIST: [ 'x' ]
+
+  - NAME: random_world_rotation
+    WORLD_ROT_ANGLE: [ -0.78539816, 0.78539816 ]
+
+  - NAME: random_world_scaling
+    WORLD_SCALE_RANGE: [ 0.95, 1.05 ]
+
+# anchor box related
+postprocess:
+  core_method: 'VoxelPostprocessor' # VoxelPostprocessor, BevPostprocessor supported
+  anchor_args:
+    cav_lidar_range: *cav_lidar
+    l: 3.9
+    w: 1.6
+    h: 1.56
+    r: [0, 90]
+    num: &achor_num 2
+  target_args:
+    pos_threshold: 0.6
+    neg_threshold: 0.45
+    score_threshold: 0.25
+  order: 'hwl' # hwl or lwh
+  max_num: 100 # maximum number of objects in a single frame. use this number to make sure different frames has the same dimension in the same batch
+  nms_thresh: 0.15
+
+# model related
+model:
+  core_method: voxel_net_intermediate # corresponding to VoxelNet
+  args:
+    N: *batch_size
+    T: *T
+    lidar_range: *cav_lidar
+    voxel_size: *voxel_size
+    anchor_num: *achor_num
+    compression: 4
+    pillar_vfe:
+      use_norm: true
+      with_distance: false
+      use_absolute_xyz: true
+      num_filters: [ 64 ]
+
+loss:
+  core_method: point_pillar_loss
+  args:
+    cls_weight: 1.0
+    reg: 2.0
+
+optimizer:
+  core_method: Adam
+  lr: 0.002
+  args:
+    eps: 1e-10
+    weight_decay: 1e-4
+
+lr_scheduler:
+  core_method: multistep #step, multistep and Exponential support
+  gamma: 0.1
+  step_size: [15, 30]
\ No newline at end of file
diff --git a/opencood/opencood/hypes_yaml/fpvrcnn_intermediate_fusion.yaml b/opencood/opencood/hypes_yaml/fpvrcnn_intermediate_fusion.yaml
new file mode 100644
index 00000000..a5159a17
--- /dev/null
+++ b/opencood/opencood/hypes_yaml/fpvrcnn_intermediate_fusion.yaml
@@ -0,0 +1,176 @@
+name: fpvrcnn_intermediate_fusion
+
+yaml_parser: "load_voxel_params"
+root_dir: "opv2v_data_dumping/train"
+validate_dir: "opv2v_data_dumping/validate"
+
+train_params:
+  batch_size: &batch_size 4
+  epoches: 20
+  eval_freq: 1
+  save_freq: 1
+
+fusion:
+  core_method: 'IntermediateFusionDatasetV2' # LateFusionDataset, EarlyFusionDataset, IntermediateFusionDataset IntermediateFusionDatasetV2 supported
+  args: []
+
+# preprocess-related
+preprocess:
+  # options: BasePreprocessor, VoxelPreprocessor, BevPreprocessor
+  core_method: 'SpVoxelPreprocessor'
+  args:
+    voxel_size: &voxel_size [0.1, 0.1, 0.1]
+    max_points_per_voxel: &T 5
+    max_voxel_train: 36000
+    max_voxel_test: 70000
+  # lidar range for each individual cav.
+  cav_lidar_range: &cav_lidar [-140.8, -41.6, -3, 140.8, 41.6, 1]
+  keep_original_lidar: true
+
+data_augment:
+  - NAME: random_world_flip
+    ALONG_AXIS_LIST: [ 'x' ]
+
+  - NAME: random_world_rotation
+    WORLD_ROT_ANGLE: [ -0.78539816, 0.78539816 ]
+
+  - NAME: random_world_scaling
+    WORLD_SCALE_RANGE: [ 0.95, 1.05 ]
+
+# anchor box related
+postprocess: &postprocess_params
+  core_method: 'FpvrcnnPostprocessor' # VoxelPostprocessor, BevPostprocessor, FpvrcnnPostprocessor supported
+  anchor_args:
+    cav_lidar_range: *cav_lidar
+    l: 3.9
+    w: 1.6
+    h: 1.56
+    r: [0, 90]
+    feature_stride: 8
+    num: &achor_num 2
+  target_args:
+    pos_threshold: 0.6
+    neg_threshold: 0.45
+    score_threshold: 0.5
+  order: 'hwl' # hwl or lwh
+  max_num: 100 # maximum number of objects in a single frame. use this number to make sure different frames has the same dimension in the same batch
+  nms_thresh: 0.15
+
+# model related
+model:
+  core_method: fpvrcnn # corresponding to FPVRCNN
+  args:
+    activate_stage2: False
+    N: *batch_size
+    T: *T
+    lidar_range: *cav_lidar
+    voxel_size: *voxel_size
+    anchor_num: *achor_num
+    mean_vfe:
+      num_point_features: 4
+    spconv:
+      num_features_in: 64
+      num_features_out: 64
+    map2bev:
+      feature_num: 128
+    ssfa:
+      feature_num: 128
+    head:
+      num_input: 128
+      num_pred: 14
+      num_cls: 2
+      num_iou: 2
+      use_dir: True
+      num_dir: 4
+    post_processer: *postprocess_params
+    vsa:
+      enlarge_selection_boxes: true
+      point_source: 'raw_points'
+      num_keypoints: 4096
+      num_out_features: &cpm_features 32
+      features_source: ['bev', 'x_conv1', 'x_conv2', 'x_conv3', 'x_conv4', 'raw_points']
+      sa_layer:
+        raw_points:
+          mlps: [[16, 16], [16, 16]]
+          pool_radius: [0.4, 0.8]
+          n_sample: [16, 16]
+        x_conv1:
+          downsample_factor: 1
+          mlps: [[16, 16], [16, 16]]
+          pool_radius: [0.4, 0.8]
+          n_sample: [16, 16]
+        x_conv2:
+          downsample_factor: 2
+          mlps: [[32, 32], [32, 32]]
+          pool_radius: [0.8, 1.2]
+          n_sample: [16, 32]
+        x_conv3:
+          downsample_factor: 4
+          mlps: [[64, 64], [64, 64]]
+          pool_radius: [1.2, 2.4]
+          n_sample: [16, 32]
+        x_conv4:
+          downsample_factor: 8
+          mlps: [[64, 64], [64, 64]]
+          pool_radius: [2.4, 4.8]
+          n_sample: [16, 32]
+    matcher: 'none' # may need to configure parameters for loc correction
+    roi_head:
+      num_cls: 1
+      in_channels: *cpm_features
+      n_fc_neurons: 256
+      dp_ratio: 0.3 # dropout ratio
+      roi_grid_pool:
+        grid_size: 6
+        mlps: [[64, 64], [64, 64]]
+        pool_radius: [0.8, 1.6]
+        n_sample: [16, 16]
+        pool_method: 'max_pool'
+
+loss:
+  core_method: fpvrcnn_loss
+  args:
+    stage1:
+      pos_cls_weight: 2.0
+      encode_rad_error_by_sin: true
+      cls:
+        type: 'SigmoidFocalLoss'
+        alpha: 0.25
+        gamma: 2.0
+        weight: 2.0
+      reg:
+        type: 'WeightedSmoothL1Loss'
+        sigma: 3.0
+        codewise: true
+        weight: 2.0
+      iou: &iou
+        type: 'WeightedSmoothL1Loss'
+        sigma: 3.0
+        codewise: true
+        weight: 1.0
+      dir:
+        type: 'WeightedSoftmaxClassificationLoss'
+        weight: 0.2
+    stage2:
+      cls:
+        type: 'WeightedSigmoidBinaryCELoss'
+        weight: 1.0
+      iou: *iou
+      reg:
+        type: 'WeightedSmoothL1Loss'
+        sigma: 3.0
+        reduction: 'mean'
+        code_weights: [20.0]
+        weight: 1.0
+
+optimizer:
+  core_method: Adam
+  lr: 0.002
+  args:
+    eps: 1e-10
+    weight_decay: 1e-4
+
+lr_scheduler:
+  core_method: multistep #step, multistep and Exponential support
+  gamma: 0.1
+  step_size: [10, 20]
\ No newline at end of file
diff --git a/opencood/opencood/hypes_yaml/pixor_early_fusion.yaml b/opencood/opencood/hypes_yaml/pixor_early_fusion.yaml
new file mode 100644
index 00000000..0edc495a
--- /dev/null
+++ b/opencood/opencood/hypes_yaml/pixor_early_fusion.yaml
@@ -0,0 +1,64 @@
+name: pixor_early_fusion
+
+yaml_parser: "load_bev_params"
+root_dir: "opv2v_data_dumping/train"
+validate_dir: "opv2v_data_dumping/validate"
+train_params:
+  batch_size: &batch_size 2
+  epoches: 15
+  eval_freq: 1
+  save_freq: 1
+fusion:
+  core_method: 'EarlyFusionDataset' # LateFusionDataset, EarlyFusionDataset, IntermediateFusionDataset supported
+  args: []
+
+# preprocess-related
+preprocess:
+  # options: BasePreprocessor, VoxelPreprocessor, BevPreprocessor
+  core_method: 'BevPreprocessor'
+  args:
+    res: &res 0.2 # discretization resolusion
+    downsample_rate: &downsample_rate 4 # pixor downsample ratio
+  # lidar range for each individual cav.
+  cav_lidar_range: &cav_lidar [-160, -40, -3, 160, 40, 1] # must be divisible by 16
+
+data_augment: []
+
+# anchor box related
+postprocess:
+  core_method: 'BevPostprocessor' # VoxelPostprocessor, BevPostprocessor supported
+  nms_thresh: 0.15
+  anchor_args:
+    cav_lidar_range: *cav_lidar
+    res: *res
+    downsample_rate: *downsample_rate # pixor downsample ratio
+  target_args:
+    score_threshold: 0.5
+
+
+  order: 'lwh' # hwl or lwh
+  max_num: 100 # maximum number of objects in a single frame. use this number to make sure different frames has the same dimension in the same batch
+
+# model related
+model:
+  core_method: pixor # corresponding to VoxelNet
+  args:
+    use_bn: True
+    decode: False
+
+loss:
+  core_method: pixor_loss
+  args:
+    alpha: 1.0
+    beta: 1.0
+
+optimizer:
+  core_method: Adam
+  lr: 0.001
+  args:
+    eps: 1e-10
+    weight_decay: 1e-4
+
+lr_scheduler:
+  core_method: Exponential #step, multistep and Exponential support
+  gamma: 0.99
diff --git a/opencood/opencood/hypes_yaml/pixor_intermediate_fusion.yaml b/opencood/opencood/hypes_yaml/pixor_intermediate_fusion.yaml
new file mode 100644
index 00000000..ea62c259
--- /dev/null
+++ b/opencood/opencood/hypes_yaml/pixor_intermediate_fusion.yaml
@@ -0,0 +1,64 @@
+name: pixor_intermediate_fusion
+
+yaml_parser: "load_bev_params"
+root_dir: "opv2v_data_dumping/train"
+validate_dir: "opv2v_data_dumping/validate"
+train_params:
+  batch_size: &batch_size 2
+  epoches: 15
+  eval_freq: 1
+  save_freq: 1
+fusion:
+  core_method: 'IntermediateFusionDataset' # LateFusionDataset, EarlyFusionDataset, IntermediateFusionDataset supported
+  args: []
+
+# preprocess-related
+preprocess:
+  # options: BasePreprocessor, VoxelPreprocessor, BevPreprocessor
+  core_method: 'BevPreprocessor'
+  args:
+    res: &res 0.2 # discretization resolusion
+    downsample_rate: &downsample_rate 4 # pixor downsample ratio
+  # lidar range for each individual cav.
+  cav_lidar_range: &cav_lidar [-160, -40, -3, 160, 40, 1] # must be divisible by 16
+
+data_augment: []
+
+# anchor box related
+postprocess:
+  core_method: 'BevPostprocessor' # VoxelPostprocessor, BevPostprocessor supported
+  nms_thresh: 0.15
+  anchor_args:
+    cav_lidar_range: *cav_lidar
+    res: *res
+    downsample_rate: *downsample_rate # pixor downsample ratio
+  target_args:
+    score_threshold: 0.7
+
+
+  order: 'lwh' # hwl or lwh
+  max_num: 100 # maximum number of objects in a single frame. use this number to make sure different frames has the same dimension in the same batch
+
+# model related
+model:
+  core_method: pixor_intermediate
+  args:
+    use_bn: True
+    decode: False
+
+loss:
+  core_method: pixor_loss
+  args:
+    alpha: 1.0
+    beta: 1.0
+
+optimizer:
+  core_method: Adam
+  lr: 0.001
+  args:
+    eps: 1e-10
+    weight_decay: 1e-4
+
+lr_scheduler:
+  core_method: Exponential #step, multistep and Exponential support
+  gamma: 0.99
diff --git a/opencood/opencood/hypes_yaml/pixor_late_fusion.yaml b/opencood/opencood/hypes_yaml/pixor_late_fusion.yaml
new file mode 100644
index 00000000..d0f3025b
--- /dev/null
+++ b/opencood/opencood/hypes_yaml/pixor_late_fusion.yaml
@@ -0,0 +1,58 @@
+name: pixor_post_fusion
+
+yaml_parser: "load_bev_params"
+root_dir: "opv2v_data_dumping/train"
+validate_dir: "opv2v_data_dumping/validate"
+train_params:
+  batch_size: &batch_size 2
+  epoches: 15
+  eval_freq: 1
+  save_freq: 1
+fusion:
+  core_method: 'LateFusionDataset' # LateFusionDataset, EarlyFusionDataset, IntermediateFusionDataset supported
+  args: []
+
+# preprocess-related
+preprocess:
+  # options: BasePreprocessor, VoxelPreprocessor, BevPreprocessor
+  core_method: 'BevPreprocessor'
+  args:
+    res: &res 0.2 # discretization resolusion
+    downsample_rate: &downsample_rate 4 # pixor downsample ratio
+  # lidar range for each individual cav.
+  cav_lidar_range: &cav_lidar [-80, -40, -3, 80, 40, 1] # must be divisible by 16
+
+# anchor box related
+postprocess:
+  core_method: 'BevPostprocessor' # VoxelPostprocessor, BevPostprocessor supported
+  nms_thresh: 0.15
+  anchor_args:
+    cav_lidar_range: *cav_lidar
+    res: *res
+    downsample_rate: *downsample_rate # pixor downsample ratio
+  target_args:
+    score_threshold: 0.3
+
+
+  order: 'lwh' # hwl or lwh
+  max_num: 100 # maximum number of objects in a single frame. use this number to make sure different frames has the same dimension in the same batch
+
+# model related
+model:
+  core_method: pixor # corresponding to VoxelNet
+  args:
+    use_bn: True
+    decode: False
+
+loss:
+  core_method: pixor_loss
+  args:
+    alpha: 1.0
+    beta: 1.0
+
+optimizer:
+  core_method: Adam
+  lr: 0.001
+  args:
+    eps: 1e-10
+    weight_decay: 1e-4
\ No newline at end of file
diff --git a/opencood/opencood/hypes_yaml/point_pillar_cobevt.yaml b/opencood/opencood/hypes_yaml/point_pillar_cobevt.yaml
new file mode 100644
index 00000000..5e7fdc9c
--- /dev/null
+++ b/opencood/opencood/hypes_yaml/point_pillar_cobevt.yaml
@@ -0,0 +1,132 @@
+name: corpbevtlidar
+root_dir: 'v2xset/train'
+validate_dir: 'v2xset/validate'
+
+wild_setting:
+  async: false
+  async_overhead: 100
+  seed: 20
+  loc_err: false
+  xyz_std: 0.2
+  ryp_std: 0.2
+  data_size: 1.06 # Mb!!
+  transmission_speed: 27 # Mbps!!
+  backbone_delay: 10 # ms
+
+yaml_parser: "load_point_pillar_params"
+train_params:
+  batch_size: &batch_size 4
+  epoches: &epoches 90
+  eval_freq: 2
+  save_freq: 1
+  max_cav: &max_cav 5
+
+fusion:
+  core_method: 'IntermediateFusionDataset' # LateFusionDataset, EarlyFusionDataset, IntermediateFusionDataset supported
+  args: []
+
+# preprocess-related
+preprocess:
+  # options: BasePreprocessor, VoxelPreprocessor, BevPreprocessor
+  core_method: 'SpVoxelPreprocessor'
+  args:
+    voxel_size: &voxel_size [0.4, 0.4, 4]
+    max_points_per_voxel: 32
+    max_voxel_train: 32000
+    max_voxel_test: 70000
+  # lidar range for each individual cav.
+  cav_lidar_range: &cav_lidar [-140.8, -38.4, -3, 140.8, 38.4, 1]
+
+data_augment:
+  - NAME: random_world_flip
+    ALONG_AXIS_LIST: [ 'x' ]
+
+  - NAME: random_world_rotation
+    WORLD_ROT_ANGLE: [ -0.78539816, 0.78539816 ]
+
+  - NAME: random_world_scaling
+    WORLD_SCALE_RANGE: [ 0.95, 1.05 ]
+
+# anchor box related
+postprocess:
+  core_method: 'VoxelPostprocessor' # VoxelPostprocessor, BevPostprocessor supported
+  anchor_args:
+    cav_lidar_range: *cav_lidar
+    l: 3.9
+    w: 1.6
+    h: 1.56
+    r: [0, 90]
+    feature_stride: 4
+    num: &achor_num 2
+  target_args:
+    pos_threshold: 0.6
+    neg_threshold: 0.45
+    score_threshold: 0.20
+  order: 'hwl' # hwl or lwh
+  max_num: 100 # maximum number of objects in a single frame. use this number to make sure different frames has the same dimension in the same batch
+  nms_thresh: 0.15
+
+# model related
+model:
+  core_method: point_pillar_cobevt
+  args:
+    voxel_size: *voxel_size
+    lidar_range: *cav_lidar
+    anchor_number: *achor_num
+    max_cav: *max_cav
+    compression: 0 # compression rate
+    backbone_fix: false
+
+    pillar_vfe:
+      use_norm: true
+      with_distance: false
+      use_absolute_xyz: true
+      num_filters: [64]
+    point_pillar_scatter:
+      num_features: 64
+
+    base_bev_backbone:
+      layer_nums: [3, 5, 8]
+      layer_strides: [2, 2, 2]
+      num_filters: [64, 128, 256]
+      upsample_strides: [1, 2, 4]
+      num_upsample_filter: [128, 128, 128]
+    shrink_header:
+      kernal_size: [3]
+      stride: [2]
+      padding: [1]
+      dim: [256]
+      input_dim: 384 # 128 * 3
+
+    fax_fusion:
+      input_dim: 256
+      mlp_dim: 256
+      agent_size: *max_cav
+      window_size: 4
+      dim_head: 32
+      drop_out: 0.1
+      depth: 3
+      mask: true
+
+
+      # add decoder later
+
+loss:
+  core_method: point_pillar_loss
+  args:
+    cls_weight: 1.0
+    reg: 2.0
+
+optimizer:
+  core_method: Adam
+  lr: 0.001
+  args:
+    eps: 1e-10
+    weight_decay: 1e-4
+
+lr_scheduler:
+    core_method: cosineannealwarm #step, multistep, Exponential and cosineannealwarm support
+    epoches: *epoches
+    warmup_lr: 2e-4
+    warmup_epoches: 10
+    lr_min: 2e-5
diff --git a/opencood/opencood/hypes_yaml/point_pillar_early_fusion.yaml b/opencood/opencood/hypes_yaml/point_pillar_early_fusion.yaml
new file mode 100644
index 00000000..9e55f5d6
--- /dev/null
+++ b/opencood/opencood/hypes_yaml/point_pillar_early_fusion.yaml
@@ -0,0 +1,97 @@
+name: point_pillar_early_fusion
+root_dir: "opv2v_data_dumping/train"
+validate_dir: "opv2v_data_dumping/validate"
+
+yaml_parser: "load_point_pillar_params"
+train_params:
+  batch_size: &batch_size 2
+  epoches: 15
+  eval_freq: 1
+  save_freq: 1
+
+fusion:
+  core_method: 'EarlyFusionDataset' # LateFusionDataset, EarlyFusionDataset, IntermediateFusionDataset supported
+  args: []
+
+# preprocess-related
+preprocess:
+  # options: BasePreprocessor, VoxelPreprocessor, BevPreprocessor
+  core_method: 'SpVoxelPreprocessor'
+  args:
+    voxel_size: &voxel_size [0.4, 0.4, 4]
+    max_points_per_voxel: 32
+    max_voxel_train: 32000
+    max_voxel_test: 70000
+  # lidar range for each individual cav.
+  cav_lidar_range: &cav_lidar [-140.8, -40, -3, 140.8, 40, 1]
+
+data_augment:
+  - NAME: random_world_flip
+    ALONG_AXIS_LIST: [ 'x' ]
+
+  - NAME: random_world_rotation
+    WORLD_ROT_ANGLE: [ -0.78539816, 0.78539816 ]
+
+  - NAME: random_world_scaling
+    WORLD_SCALE_RANGE: [ 0.95, 1.05 ]
+
+# anchor box related
+postprocess:
+  core_method: 'VoxelPostprocessor' # VoxelPostprocessor, BevPostprocessor supported
+  anchor_args:
+    cav_lidar_range: *cav_lidar
+    l: 3.9
+    w: 1.6
+    h: 1.56
+    r: [0, 90]
+    num: &achor_num 2
+  target_args:
+    pos_threshold: 0.6
+    neg_threshold: 0.45
+    score_threshold: 0.20
+  order: 'hwl' # hwl or lwh
+  max_num: 100 # maximum number of objects in a single frame. use this number to make sure different frames has the same dimension in the same batch
+  nms_thresh: 0.15
+
+# model related
+model:
+  core_method: point_pillar
+  args:
+    voxel_size: *voxel_size
+    lidar_range: *cav_lidar
+    anchor_number: *achor_num
+    pillar_vfe:
+      use_norm: true
+      with_distance: false
+      use_absolute_xyz: true
+      num_filters: [64]
+    point_pillar_scatter:
+      num_features: 64
+
+    base_bev_backbone:
+      layer_nums: [3, 5, 8]
+      layer_strides: [2, 2, 2]
+      num_filters: [64, 128, 256]
+      upsample_strides: [1, 2, 4]
+      num_upsample_filter: [128, 128, 128]
+
+    anchor_num: *achor_num
+
+loss:
+  core_method: point_pillar_loss
+  args:
+    cls_weight: 1.0
+    reg: 2.0
+
+optimizer:
+  core_method: Adam
+  lr: 0.002
+  args:
+    eps: 1e-10
+    weight_decay: 1e-4
+
+lr_scheduler:
+  core_method: multistep #step, multistep and Exponential support
+  gamma: 0.1
+  step_size: [10, 15]
+
diff --git a/opencood/opencood/hypes_yaml/point_pillar_fcooper.yaml b/opencood/opencood/hypes_yaml/point_pillar_fcooper.yaml
new file mode 100644
index 00000000..eaec3bcc
--- /dev/null
+++ b/opencood/opencood/hypes_yaml/point_pillar_fcooper.yaml
@@ -0,0 +1,107 @@
+name: point_pillar_fcooper
+root_dir: 'opv2v_data_dumping/train'
+validate_dir: 'opv2v_data_dumping/validate'
+
+yaml_parser: "load_point_pillar_params"
+train_params:
+  batch_size: &batch_size 2
+  epoches: 15
+  eval_freq: 1
+  save_freq: 1
+  max_cav: &max_cav 5
+
+fusion:
+  core_method: 'IntermediateFusionDataset' # LateFusionDataset, EarlyFusionDataset, IntermediateFusionDataset supported
+  args: []
+
+# preprocess-related
+preprocess:
+  # options: BasePreprocessor, VoxelPreprocessor, BevPreprocessor
+  core_method: 'SpVoxelPreprocessor'
+  args:
+    voxel_size: &voxel_size [0.4, 0.4, 4]
+    max_points_per_voxel: 32
+    max_voxel_train: 32000
+    max_voxel_test: 70000
+  # lidar range for each individual cav.
+  cav_lidar_range: &cav_lidar [-140.8, -40, -3, 140.8, 40, 1]
+
+data_augment:
+  - NAME: random_world_flip
+    ALONG_AXIS_LIST: [ 'x' ]
+
+  - NAME: random_world_rotation
+    WORLD_ROT_ANGLE: [ -0.78539816, 0.78539816 ]
+
+  - NAME: random_world_scaling
+    WORLD_SCALE_RANGE: [ 0.95, 1.05 ]
+
+# anchor box related
+postprocess:
+  core_method: 'VoxelPostprocessor' # VoxelPostprocessor, BevPostprocessor supported
+  anchor_args:
+    cav_lidar_range: *cav_lidar
+    l: 3.9
+    w: 1.6
+    h: 1.56
+    r: [0, 90]
+    feature_stride: 2
+    num: &achor_num 2
+  target_args:
+    pos_threshold: 0.6
+    neg_threshold: 0.45
+    score_threshold: 0.20
+  order: 'hwl' # hwl or lwh
+  max_num: 100 # maximum number of objects in a single frame. use this number to make sure different frames has the same dimension in the same batch
+  nms_thresh: 0.15
+
+# model related
+model:
+  core_method: point_pillar_fcooper
+  args:
+    voxel_size: *voxel_size
+    lidar_range: *cav_lidar
+    anchor_number: *achor_num
+    max_cav: *max_cav
+    compression: 0 # compression rate
+    backbone_fix: false
+
+    pillar_vfe:
+      use_norm: true
+      with_distance: false
+      use_absolute_xyz: true
+      num_filters: [64]
+    point_pillar_scatter:
+      num_features: 64
+
+    base_bev_backbone:
+      layer_nums: [3, 5, 8]
+      layer_strides: [2, 2, 2]
+      num_filters: [64, 128, 256]
+      upsample_strides: [1, 2, 4]
+      num_upsample_filter: [128, 128, 128]
+    shrink_header:
+      kernal_size: [ 1 ]
+      stride: [ 1 ]
+      padding: [ 0 ]
+      dim: [ 256 ]
+      input_dim: 384 # 128 * 3
+
+loss:
+  core_method: point_pillar_loss
+  args:
+    cls_weight: 1.0
+    reg: 2.0
+
+optimizer:
+  core_method: Adam
+  lr: 0.001
+  args:
+    eps: 1e-10
+    weight_decay: 1e-4
+
+lr_scheduler:
+  core_method: multistep #step, multistep and Exponential support
+  gamma: 0.1
+  step_size: [10, 15]
+
diff --git a/opencood/opencood/hypes_yaml/point_pillar_intermediate_fusion.yaml b/opencood/opencood/hypes_yaml/point_pillar_intermediate_fusion.yaml
new file mode 100644
index 00000000..c109cb25
--- /dev/null
+++ b/opencood/opencood/hypes_yaml/point_pillar_intermediate_fusion.yaml
@@ -0,0 +1,100 @@
+name: point_pillar_intermediate_fusion
+root_dir: "opv2v_data_dumping/train"
+validate_dir: "opv2v_data_dumping/validate"
+
+yaml_parser: "load_point_pillar_params"
+train_params:
+  batch_size: &batch_size 2
+  epoches: 15
+  eval_freq: 1
+  save_freq: 1
+
+fusion:
+  core_method: 'IntermediateFusionDataset' # LateFusionDataset, EarlyFusionDataset, IntermediateFusionDataset supported
+  args: []
+
+# preprocess-related
+preprocess:
+  # options: BasePreprocessor, VoxelPreprocessor, BevPreprocessor
+  core_method: 'SpVoxelPreprocessor'
+  args:
+    voxel_size: &voxel_size [0.4, 0.4, 4]
+    max_points_per_voxel: 32
+    max_voxel_train: 32000
+    max_voxel_test: 70000
+  # lidar range for each individual cav.
+  cav_lidar_range: &cav_lidar [-140.8, -40, -3, 140.8, 40, 1]
+
+data_augment:
+  - NAME: random_world_flip
+    ALONG_AXIS_LIST: [ 'x' ]
+
+  - NAME: random_world_rotation
+    WORLD_ROT_ANGLE: [ -0.78539816, 0.78539816 ]
+
+  - NAME: random_world_scaling
+    WORLD_SCALE_RANGE: [ 0.95, 1.05 ]
+
+# anchor box related
+postprocess:
+  core_method: 'VoxelPostprocessor' # VoxelPostprocessor, BevPostprocessor supported
+  anchor_args:
+    cav_lidar_range: *cav_lidar
+    l: 3.9
+    w: 1.6
+    h: 1.56
+    r: [0, 90]
+    feature_stride: 2
+    num: &achor_num 2
+  target_args:
+    pos_threshold: 0.6
+    neg_threshold: 0.45
+    score_threshold: 0.20
+  order: 'hwl' # hwl or lwh
+  max_num: 100 # maximum number of objects in a single frame. use this number to make sure different frames has the same dimension in the same batch
+  nms_thresh: 0.15
+
+# model related
+model:
+  core_method: point_pillar_intermediate
+  args:
+    voxel_size: *voxel_size
+    lidar_range: *cav_lidar
+    anchor_number: *achor_num
+
+    pillar_vfe:
+      use_norm: true
+      with_distance: false
+      use_absolute_xyz: true
+      num_filters: [64]
+    point_pillar_scatter:
+      num_features: 64
+
+    base_bev_backbone:
+      layer_nums: [3, 5, 8]
+      layer_strides: [2, 2, 2]
+      num_filters: [64, 128, 256]
+      upsample_strides: [1, 2, 4]
+      num_upsample_filter: [128, 128, 128]
+      compression: 0
+
+    anchor_num: *achor_num
+
+loss:
+  core_method: point_pillar_loss
+  args:
+    cls_weight: 1.0
+    reg: 2.0
+
+optimizer:
+  core_method: Adam
+  lr: 0.002
+  args:
+    eps: 1e-10
+    weight_decay: 1e-4
+
+lr_scheduler:
+  core_method: multistep #step, multistep and Exponential support
+  gamma: 0.1
+  step_size: [10, 15]
+
diff --git a/opencood/opencood/hypes_yaml/point_pillar_late_fusion.yaml b/opencood/opencood/hypes_yaml/point_pillar_late_fusion.yaml
new file mode 100644
index 00000000..3347b296
--- /dev/null
+++ b/opencood/opencood/hypes_yaml/point_pillar_late_fusion.yaml
@@ -0,0 +1,97 @@
+name: point_pillar_late_fusion
+root_dir: "opv2v_data_dumping/train"
+validate_dir: "opv2v_data_dumping/validate"
+
+yaml_parser: "load_point_pillar_params"
+train_params:
+  batch_size: &batch_size 2
+  epoches: 15
+  eval_freq: 1
+  save_freq: 1
+
+fusion:
+  core_method: 'LateFusionDataset' # LateFusionDataset, EarlyFusionDataset, IntermediateFusionDataset supported
+  args: []
+
+# preprocess-related
+preprocess:
+  # options: BasePreprocessor, VoxelPreprocessor, BevPreprocessor
+  core_method: 'SpVoxelPreprocessor'
+  args:
+    voxel_size: &voxel_size [0.4, 0.4, 4]
+    max_points_per_voxel: 32
+    max_voxel_train: 16000
+    max_voxel_test: 40000
+  # lidar range for each individual cav.
+  cav_lidar_range: &cav_lidar [-70.4, -40, -3, 70.4, 40, 1]
+
+data_augment:
+  - NAME: random_world_flip
+    ALONG_AXIS_LIST: [ 'x' ]
+
+  - NAME: random_world_rotation
+    WORLD_ROT_ANGLE: [ -0.78539816, 0.78539816 ]
+
+  - NAME: random_world_scaling
+    WORLD_SCALE_RANGE: [ 0.95, 1.05 ]
+
+# anchor box related
+postprocess:
+  core_method: 'VoxelPostprocessor' # VoxelPostprocessor, BevPostprocessor supported
+  anchor_args:
+    cav_lidar_range: *cav_lidar
+    l: 3.9
+    w: 1.6
+    h: 1.56
+    r: [0, 90]
+    num: &achor_num 2
+  target_args:
+    pos_threshold: 0.6
+    neg_threshold: 0.45
+    score_threshold: 0.20
+  order: 'hwl' # hwl or lwh
+  max_num: 100 # maximum number of objects in a single frame. use this number to make sure different frames has the same dimension in the same batch
+  nms_thresh: 0.15
+
+# model related
+model:
+  core_method: point_pillar
+  args:
+    voxel_size: *voxel_size
+    lidar_range: *cav_lidar
+    anchor_number: *achor_num
+    pillar_vfe:
+      use_norm: true
+      with_distance: false
+      use_absolute_xyz: true
+      num_filters: [64]
+    point_pillar_scatter:
+      num_features: 64
+
+    base_bev_backbone:
+      layer_nums: [3, 5, 8]
+      layer_strides: [2, 2, 2]
+      num_filters: [64, 128, 256]
+      upsample_strides: [1, 2, 4]
+      num_upsample_filter: [128, 128, 128]
+
+    anchor_num: *achor_num
+
+loss:
+  core_method: point_pillar_loss
+  args:
+    cls_weight: 1.0
+    reg: 2.0
+
+optimizer:
+  core_method: Adam
+  lr: 0.002
+  args:
+    eps: 1e-10
+    weight_decay: 1e-4
+
+lr_scheduler:
+  core_method: multistep #step, multistep and Exponential support
+  gamma: 0.1
+  step_size: [10, 15]
+
diff --git a/opencood/opencood/hypes_yaml/point_pillar_v2vnet.yaml b/opencood/opencood/hypes_yaml/point_pillar_v2vnet.yaml
new file mode 100644
index 00000000..4f849a37
--- /dev/null
+++ b/opencood/opencood/hypes_yaml/point_pillar_v2vnet.yaml
@@ -0,0 +1,124 @@
+name: point_pillar_v2vnet
+root_dir: 'opv2v_data_dumping/train'
+validate_dir: 'opv2v_data_dumping/validate'
+
+yaml_parser: "load_point_pillar_params"
+train_params:
+  batch_size: &batch_size 4
+  epoches: 60
+  eval_freq: 1
+  save_freq: 1
+  max_cav: &max_cav 5
+
+fusion:
+  core_method: 'IntermediateFusionDataset' # LateFusionDataset, EarlyFusionDataset, IntermediateFusionDataset supported
+  args:
+    proj_first: true
+
+# preprocess-related
+preprocess:
+  # options: BasePreprocessor, VoxelPreprocessor, BevPreprocessor
+  core_method: 'SpVoxelPreprocessor'
+  args:
+    voxel_size: &voxel_size [0.4, 0.4, 4]
+    max_points_per_voxel: 32
+    max_voxel_train: 32000
+    max_voxel_test: 70000
+  # lidar range for each individual cav.
+  cav_lidar_range: &cav_lidar [-140.8, -40, -3, 140.8, 40, 1]
+
+data_augment:
+  - NAME: random_world_flip
+    ALONG_AXIS_LIST: [ 'x' ]
+
+  - NAME: random_world_rotation
+    WORLD_ROT_ANGLE: [ -0.78539816, 0.78539816 ]
+
+  - NAME: random_world_scaling
+    WORLD_SCALE_RANGE: [ 0.95, 1.05 ]
+
+# anchor box related
+postprocess:
+  core_method: 'VoxelPostprocessor' # VoxelPostprocessor, BevPostprocessor supported
+  anchor_args:
+    cav_lidar_range: *cav_lidar
+    l: 3.9
+    w: 1.6
+    h: 1.56
+    r: [0, 90]
+    feature_stride: 4
+    num: &achor_num 2
+  target_args:
+    pos_threshold: 0.6
+    neg_threshold: 0.45
+    score_threshold: 0.20
+  order: 'hwl' # hwl or lwh
+  max_num: 100 # maximum number of objects in a single frame. use this number to make sure different frames has the same dimension in the same batch
+  nms_thresh: 0.15
+
+# model related
+model:
+  core_method: point_pillar_v2vnet
+  args:
+    voxel_size: *voxel_size
+    lidar_range: *cav_lidar
+    anchor_number: *achor_num
+    max_cav: *max_cav
+    compression: 0 # compression rate
+    backbone_fix: false
+
+    pillar_vfe:
+      use_norm: true
+      with_distance: false
+      use_absolute_xyz: true
+      num_filters: [64]
+    point_pillar_scatter:
+      num_features: 64
+
+    base_bev_backbone:
+      layer_nums: [3, 5, 8]
+      layer_strides: [2, 2, 2]
+      num_filters: [64, 128, 256]
+      upsample_strides: [1, 2, 4]
+      num_upsample_filter: [128, 128, 128]
+    shrink_header:
+      kernal_size: [ 3 ]
+      stride: [ 2 ]
+      padding: [ 1 ]
+      dim: [ 256 ]
+      input_dim: 384 # 128 * 3
+
+    v2vfusion:
+      voxel_size: *voxel_size
+      downsample_rate: 4
+      num_iteration: 2
+      in_channels: 256
+      gru_flag: true
+      agg_operator: "avg" # max or avg
+      conv_gru:
+        H: 50
+        W: 176
+        num_layers: 1
+        kernel_size: [[3,3]]
+
+
+      # add decoder later
+
+loss:
+  core_method: point_pillar_loss
+  args:
+    cls_weight: 1.0
+    reg: 2.0
+
+optimizer:
+  core_method: Adam
+  lr: 0.001
+  args:
+    eps: 1e-10
+    weight_decay: 1e-4
+
+lr_scheduler:
+  core_method: multistep #step, multistep and Exponential support
+  gamma: 0.1
+  step_size: [10, 50, 80]
+
diff --git a/opencood/opencood/hypes_yaml/point_pillar_v2xvit.yaml b/opencood/opencood/hypes_yaml/point_pillar_v2xvit.yaml
new file mode 100644
index 00000000..a496e703
--- /dev/null
+++ b/opencood/opencood/hypes_yaml/point_pillar_v2xvit.yaml
@@ -0,0 +1,162 @@
+name: point_pillar_v2xvit
+root_dir: 'v2xset/train'
+validate_dir: 'v2xset/validate'
+
+wild_setting:
+  async: false
+  async_mode: 'sim'
+  async_overhead: 100
+  seed: 25
+  loc_err: false
+  xyz_std: 0.2
+  ryp_std: 0.2
+  data_size: 1.06 # Mb!!
+  transmission_speed: 27 # Mbps!!
+  backbone_delay: 10 # ms
+
+yaml_parser: "load_point_pillar_params"
+train_params:
+  batch_size: &batch_size 2
+  epoches: 60
+  eval_freq: 1
+  save_freq: 1
+  max_cav: &max_cav 5
+
+fusion:
+  core_method: 'IntermediateFusionDataset' # LateFusionDataset, EarlyFusionDataset, IntermediateFusionDataset supported
+  args:
+    cur_ego_pose_flag: False
+      # when the cur_ego_pose_flag is set to True, there is no time gap
+      # between  the time when the LiDAR data is captured by connected
+      # agents and when the extracted features are received by
+      # the ego vehicle, which is equal to implement STCM. When set to False,
+      # STCM has to be used.
+
+# preprocess-related
+preprocess:
+  # options: BasePreprocessor, VoxelPreprocessor, BevPreprocessor
+  core_method: 'SpVoxelPreprocessor'
+  args:
+    voxel_size: &voxel_size [0.4, 0.4, 4]
+    max_points_per_voxel: 32
+    max_voxel_train: 32000
+    max_voxel_test: 70000
+  # lidar range for each individual cav.
+  cav_lidar_range: &cav_lidar [-140.8, -38.4, -3, 140.8, 38.4, 1]
+
+data_augment:
+  - NAME: random_world_flip
+    ALONG_AXIS_LIST: [ 'x' ]
+
+  - NAME: random_world_rotation
+    WORLD_ROT_ANGLE: [ -0.78539816, 0.78539816 ]
+
+  - NAME: random_world_scaling
+    WORLD_SCALE_RANGE: [ 0.95, 1.05 ]
+
+# anchor box related
+postprocess:
+  core_method: 'VoxelPostprocessor' # VoxelPostprocessor, BevPostprocessor supported
+  anchor_args:
+    cav_lidar_range: *cav_lidar
+    l: 3.9
+    w: 1.6
+    h: 1.56
+    r: [0, 90]
+    feature_stride: 4
+    num: &achor_num 2
+  target_args:
+    pos_threshold: 0.6
+    neg_threshold: 0.45
+    score_threshold: 0.27
+  order: 'hwl' # hwl or lwh
+  max_num: 100 # maximum number of objects in a single frame. use this number to make sure different frames has the same dimension in the same batch
+  nms_thresh: 0.15
+
+# model related
+model:
+  core_method: point_pillar_transformer
+  args:
+    voxel_size: *voxel_size
+    lidar_range: *cav_lidar
+    anchor_number: *achor_num
+    max_cav: *max_cav
+    compression: 0 # compression rate
+    backbone_fix: false
+
+    pillar_vfe:
+      use_norm: true
+      with_distance: false
+      use_absolute_xyz: true
+      num_filters: [64]
+    point_pillar_scatter:
+      num_features: 64
+
+    base_bev_backbone:
+      layer_nums: [3, 5, 8]
+      layer_strides: [2, 2, 2]
+      num_filters: [64, 128, 256]
+      upsample_strides: [1, 2, 4]
+      num_upsample_filter: [128, 128, 128]
+    shrink_header:
+      kernal_size: [3]
+      stride: [2]
+      padding: [1]
+      dim: [256]
+      input_dim: 384 # 128 * 3
+
+    transformer:
+      encoder: &encoder
+        # number of fusion blocks per encoder layer
+        num_blocks: 1
+        # number of encoder layers
+        depth: 3
+        use_roi_mask: true
+        use_RTE: &use_RTE true
+        RTE_ratio: &RTE_ratio 2 # 2 means the dt has 100ms interval while 1 means 50 ms interval
+        # agent-wise attention
+        cav_att_config: &cav_att_config
+          dim: 256
+          use_hetero: true
+          use_RTE: *use_RTE
+          RTE_ratio: *RTE_ratio
+          heads: 8
+          dim_head: 32
+          dropout: 0.3
+        # spatial-wise attention
+        pwindow_att_config: &pwindow_att_config
+          dim: 256
+          heads: [16, 8, 4]
+          dim_head: [16, 32, 64]
+          dropout: 0.3
+          window_size: [4, 8, 16]
+          relative_pos_embedding: true
+          fusion_method: 'split_attn'
+        # feedforward condition
+        feed_forward: &feed_forward
+          mlp_dim: 256
+          dropout: 0.3
+        sttf: &sttf
+          voxel_size: *voxel_size
+          downsample_rate: 4
+
+      # add decoder later
+
+loss:
+  core_method: point_pillar_loss
+  args:
+    cls_weight: 1.0
+    reg: 2.0
+
+optimizer:
+  core_method: Adam
+  lr: 0.001
+  args:
+    eps: 1e-10
+    weight_decay: 1e-4
+
+lr_scheduler:
+  core_method: multistep #step, multistep and Exponential support
+  gamma: 0.1
+  step_size: [15, 50]
+
diff --git a/opencood/opencood/hypes_yaml/point_pillar_where2comm.yaml b/opencood/opencood/hypes_yaml/point_pillar_where2comm.yaml
new file mode 100644
index 00000000..7126d197
--- /dev/null
+++ b/opencood/opencood/hypes_yaml/point_pillar_where2comm.yaml
@@ -0,0 +1,132 @@
+name: point_pillar_where2comm
+root_dir: '/data/opv2x/train'
+validate_dir: '/data/opv2x/validate'
+
+
+wild_setting:
+  seed: 20
+  async: False
+  async_mode: 'sim'
+  async_overhead: 100
+  loc_err: False
+  xyz_std: 0.2
+  ryp_std: 0.2
+  data_size: 1.06  # Mb
+  transmission_speed: 27  # Mbps
+  backbone_delay: 10  # ms
+
+yaml_parser: 'load_point_pillar_params'
+train_params:
+  batch_size: &batch_size 1
+  epoches: &epoches 50
+  eval_freq: 1
+  save_freq: 1
+  max_cav: &max_cav 5
+
+fusion:
+  core_method: 'IntermediateFusionDataset'  # LateFusionDataset, EarlyFusionDataset, IntermediateFusionDataset supported
+  args: [ ]
+
+# Preprocess-related
+preprocess:
+  # Options: BasePreprocessor, VoxelPreprocessor, BevPreprocessor
+  core_method: 'SpVoxelPreprocessor'
+  args:
+    voxel_size: &voxel_size [ 0.4, 0.4, 4 ]
+    max_points_per_voxel: 32
+    max_voxel_train: 32000
+    max_voxel_test: 70000
+  # LiDAR range for each individual CAV
+  cav_lidar_range: &cav_lidar [ -140.8, -38.4, -3, 140.8, 38.4, 1 ]
+
+data_augment:
+  - NAME: random_world_flip
+    ALONG_AXIS_LIST: [ 'x' ]
+  - NAME: random_world_rotation
+    WORLD_ROT_ANGLE: [ -0.78539816, 0.78539816 ]
+  - NAME: random_world_scaling
+    WORLD_SCALE_RANGE: [ 0.95, 1.05 ]
+
+# Anchor box related
+postprocess:
+  core_method: 'VoxelPostprocessor'  # VoxelPostprocessor, BevPostprocessor supported
+  anchor_args:
+    cav_lidar_range: *cav_lidar
+    l: 3.9
+    w: 1.6
+    h: 1.56
+    r: [ 0, 90 ]
+    num: &achor_num 2
+    feature_stride: 4
+  target_args:
+    pos_threshold: 0.6
+    neg_threshold: 0.45
+    score_threshold: 0.2
+  order: 'hwl'  # hwl or lwh
+  max_num: 100  # Maximum number of objects in a single frame. Use this number to make sure different frames has the same dimension in the same batch
+  nms_thresh: 0.15
+
+# Model related
+model:
+  core_method: point_pillar_where2comm
+  args:
+    voxel_size: *voxel_size
+    lidar_range: *cav_lidar
+    anchor_number: *achor_num
+    head_dim: 256
+    max_cav: *max_cav
+    compression: 0  # Compression rate
+    backbone_fix: False
+    pillar_vfe:
+      use_norm: True
+      with_distance: False
+      use_absolute_xyz: True
+      num_filters: [ 64 ]
+    point_pillar_scatter:
+      num_features: 64
+    base_bev_backbone:
+      layer_nums: &layer_nums [ 3, 5, 8 ]
+      layer_strides: [ 2, 2, 2 ]
+      num_filters: &num_filters [ 64, 128, 256 ]
+      upsample_strides: [ 1, 2, 4 ]
+      num_upsample_filter: [ 128, 128, 128 ]
+    shrink_header:
+      kernal_size: [ 3 ]
+      stride: [ 2 ]
+      padding: [ 1 ]
+      dim: [ 256 ]
+      input_dim: 384  # 128 * 3
+    where2comm_fusion:
+      fully: False
+      voxel_size: *voxel_size
+      downsample_rate: 4
+      in_channels: 256
+      multi_scale: True
+      layer_nums: *layer_nums
+      num_filters: *num_filters
+      communication:
+        round: 1
+        threshold: 0.01
+        gaussian_smooth:
+          k_size: 5
+          c_sigma: 1.0
+
+loss:
+  core_method: point_pillar_loss
+  args:
+    cls_weight: 1.0
+    reg: 2.0
+
+optimizer:
+  core_method: Adam
+  lr: 2e-4
+  args:
+    eps: 1e-10
+    weight_decay: 1e-2
+
+lr_scheduler:
+  core_method: cosineannealwarm # step, multistep, exponential and cosineannealwarm support
+  epoches: *epoches
+  warmup_lr: 2e-5
+  warmup_epoches: 10
+  lr_min: 5e-6
\ No newline at end of file
diff --git a/opencood/opencood/hypes_yaml/second_early_fusion.yaml b/opencood/opencood/hypes_yaml/second_early_fusion.yaml
new file mode 100644
index 00000000..2daaa5da
--- /dev/null
+++ b/opencood/opencood/hypes_yaml/second_early_fusion.yaml
@@ -0,0 +1,99 @@
+name: second_early_fusion
+root_dir: "opv2v_data_dumping/train"
+validate_dir: "opv2v_data_dumping/validate"
+
+yaml_parser: "load_second_params"
+train_params:
+  batch_size: &batch_size 2
+  epoches: 30
+  eval_freq: 1
+  save_freq: 1
+
+fusion:
+  core_method: 'EarlyFusionDataset' # LateFusionDataset, EarlyFusionDataset, IntermediateFusionDataset supported
+  args: []
+
+# preprocess-related
+preprocess:
+  # options: BasePreprocessor, VoxelPreprocessor, BevPreprocessor
+  core_method: 'SpVoxelPreprocessor'
+  args:
+    voxel_size: &voxel_size [0.1, 0.1, 0.1]
+    max_points_per_voxel: 5
+    max_voxel_train: 32000
+    max_voxel_test: 70000
+  # lidar range for each individual cav.
+  cav_lidar_range: &cav_lidar [-140.8, -40, -3, 140.8, 40, 1]
+
+data_augment:
+  - NAME: random_world_flip
+    ALONG_AXIS_LIST: [ 'x' ]
+
+  - NAME: random_world_rotation
+    WORLD_ROT_ANGLE: [ -0.78539816, 0.78539816 ]
+
+  - NAME: random_world_scaling
+    WORLD_SCALE_RANGE: [ 0.95, 1.05 ]
+
+# anchor box related
+postprocess:
+  core_method: 'VoxelPostprocessor' # VoxelPostprocessor, BevPostprocessor supported
+  anchor_args:
+    cav_lidar_range: *cav_lidar
+    l: 3.9
+    w: 1.6
+    h: 1.56
+    r: [0, 90]
+    feature_stride: 8
+    num: &achor_num 2
+  target_args:
+    pos_threshold: 0.6
+    neg_threshold: 0.45
+    score_threshold: 0.20
+  order: 'hwl' # hwl or lwh
+  max_num: 100 # maximum number of objects in a single frame. use this number to make sure different frames has the same dimension in the same batch
+  nms_thresh: 0.15
+
+# model related
+model:
+  core_method: second
+  args:
+    batch_size: *batch_size
+    voxel_size: *voxel_size
+    lidar_range: *cav_lidar
+    anchor_number: *achor_num
+    mean_vfe:
+      feature_points: 4
+
+    backbone_3d: []
+
+    height_compression:
+      feature_num: 256
+
+    base_bev_backbone:
+      layer_nums: [5, 5]
+      layer_strides: [1, 2]
+      num_filters: [128, 256]
+      upsample_strides: [1, 2]
+      num_upsample_filter: [256, 256]
+
+    anchor_num: *achor_num
+
+loss:
+  core_method: point_pillar_loss
+  args:
+    cls_weight: 1.0
+    reg: 2.0
+
+optimizer:
+  core_method: Adam
+  lr: 0.002
+  args:
+    eps: 1e-10
+    weight_decay: 1e-4
+
+lr_scheduler:
+  core_method: multistep #step, multistep and Exponential support
+  gamma: 0.1
+  step_size: [15, 30]
+
diff --git a/opencood/opencood/hypes_yaml/second_intermediate_fusion.yaml b/opencood/opencood/hypes_yaml/second_intermediate_fusion.yaml
new file mode 100644
index 00000000..7f6fc1f1
--- /dev/null
+++ b/opencood/opencood/hypes_yaml/second_intermediate_fusion.yaml
@@ -0,0 +1,99 @@
+name: second_intermediate_fusion
+root_dir: "opv2v_data_dumping/train"
+validate_dir: "opv2v_data_dumping/validate"
+
+yaml_parser: "load_second_params"
+train_params:
+  batch_size: &batch_size 2
+  epoches: 60
+  eval_freq: 1
+  save_freq: 1
+
+fusion:
+  core_method: 'IntermediateFusionDataset' # LateFusionDataset, EarlyFusionDataset, IntermediateFusionDataset supported
+  args: []
+
+# preprocess-related
+preprocess:
+  # options: BasePreprocessor, VoxelPreprocessor, BevPreprocessor
+  core_method: 'SpVoxelPreprocessor'
+  args:
+    voxel_size: &voxel_size [0.1, 0.1, 0.1]
+    max_points_per_voxel: 5
+    max_voxel_train: 36000
+    max_voxel_test: 70000
+  # lidar range for each individual cav.
+  cav_lidar_range: &cav_lidar [-140.8, -40, -3, 140.8, 40, 1]
+
+data_augment:
+  - NAME: random_world_flip
+    ALONG_AXIS_LIST: [ 'x' ]
+
+  - NAME: random_world_rotation
+    WORLD_ROT_ANGLE: [ -0.78539816, 0.78539816 ]
+
+  - NAME: random_world_scaling
+    WORLD_SCALE_RANGE: [ 0.95, 1.05 ]
+
+# anchor box related
+postprocess:
+  core_method: 'VoxelPostprocessor' # VoxelPostprocessor, BevPostprocessor supported
+  anchor_args:
+    cav_lidar_range: *cav_lidar
+    l: 3.9
+    w: 1.6
+    h: 1.56
+    r: [0, 90]
+    feature_stride: 8
+    num: &achor_num 2
+  target_args:
+    pos_threshold: 0.6
+    neg_threshold: 0.45
+    score_threshold: 0.20
+  order: 'hwl' # hwl or lwh
+  max_num: 100 # maximum number of objects in a single frame. use this number to make sure different frames has the same dimension in the same batch
+  nms_thresh: 0.15
+
+# model related
+model:
+  core_method: second_intermediate
+  args:
+    batch_size: *batch_size
+    voxel_size: *voxel_size
+    lidar_range: *cav_lidar
+    anchor_number: *achor_num
+    mean_vfe:
+      feature_points: 4
+
+    backbone_3d: []
+
+    height_compression:
+      feature_num: 256
+
+    base_bev_backbone:
+      layer_nums: [5, 5]
+      layer_strides: [1, 2]
+      num_filters: [128, 256]
+      upsample_strides: [1, 2]
+      num_upsample_filter: [256, 256]
+
+    anchor_num: *achor_num
+
+loss:
+  core_method: point_pillar_loss
+  args:
+    cls_weight: 1.0
+    reg: 2.0
+
+optimizer:
+  core_method: Adam
+  lr: 0.002
+  args:
+    eps: 1e-10
+    weight_decay: 1e-4
+
+lr_scheduler:
+  core_method: multistep #step, multistep and Exponential support
+  gamma: 0.1
+  step_size: [15, 30]
+
diff --git a/opencood/opencood/hypes_yaml/second_late_fusion.yaml b/opencood/opencood/hypes_yaml/second_late_fusion.yaml
new file mode 100644
index 00000000..4fb42f23
--- /dev/null
+++ b/opencood/opencood/hypes_yaml/second_late_fusion.yaml
@@ -0,0 +1,99 @@
+name: second_late_fusion_low_res
+root_dir: "opv2v_data_dumping/train"
+validate_dir: "opv2v_data_dumping/validate"
+
+yaml_parser: "load_second_params"
+train_params:
+  batch_size: &batch_size 2
+  epoches: 40
+  eval_freq: 1
+  save_freq: 1
+
+fusion:
+  core_method: 'LateFusionDataset' # LateFusionDataset, EarlyFusionDataset, IntermediateFusionDataset supported
+  args: []
+
+# preprocess-related
+preprocess:
+  # options: BasePreprocessor, VoxelPreprocessor, BevPreprocessor
+  core_method: 'SpVoxelPreprocessor'
+  args:
+    voxel_size: &voxel_size [0.1, 0.1, 0.1]
+    max_points_per_voxel: 5
+    max_voxel_train: 16000
+    max_voxel_test: 40000
+  # lidar range for each individual cav.
+  cav_lidar_range: &cav_lidar [-70.4, -40, -3, 70.4, 40, 1]
+
+data_augment:
+  - NAME: random_world_flip
+    ALONG_AXIS_LIST: [ 'x' ]
+
+  - NAME: random_world_rotation
+    WORLD_ROT_ANGLE: [ -0.78539816, 0.78539816 ]
+
+  - NAME: random_world_scaling
+    WORLD_SCALE_RANGE: [ 0.95, 1.05 ]
+
+# anchor box related
+postprocess:
+  core_method: 'VoxelPostprocessor' # VoxelPostprocessor, BevPostprocessor supported
+  anchor_args:
+    cav_lidar_range: *cav_lidar
+    l: 3.9
+    w: 1.6
+    h: 1.56
+    r: [0, 90]
+    feature_stride: 8
+    num: &achor_num 2
+  target_args:
+    pos_threshold: 0.6
+    neg_threshold: 0.45
+    score_threshold: 0.20
+  order: 'hwl' # hwl or lwh
+  max_num: 100 # maximum number of objects in a single frame. use this number to make sure different frames has the same dimension in the same batch
+  nms_thresh: 0.15
+
+# model related
+model:
+  core_method: second
+  args:
+    batch_size: *batch_size
+    voxel_size: *voxel_size
+    lidar_range: *cav_lidar
+    anchor_number: *achor_num
+    mean_vfe:
+      feature_points: 4
+
+    backbone_3d: []
+
+    height_compression:
+      feature_num: 256
+
+    base_bev_backbone:
+      layer_nums: [5, 5]
+      layer_strides: [1, 2]
+      num_filters: [128, 256]
+      upsample_strides: [1, 2]
+      num_upsample_filter: [256, 256]
+
+    anchor_num: *achor_num
+
+loss:
+  core_method: point_pillar_loss
+  args:
+    cls_weight: 1.0
+    reg: 2.0
+
+optimizer:
+  core_method: Adam
+  lr: 0.002
+  args:
+    eps: 1e-10
+    weight_decay: 1e-4
+
+lr_scheduler:
+  core_method: multistep #step, multistep and Exponential support
+  gamma: 0.1
+  step_size: [15, 30]
+
diff --git a/opencood/opencood/hypes_yaml/visualization.yaml b/opencood/opencood/hypes_yaml/visualization.yaml
new file mode 100644
index 00000000..394e33dd
--- /dev/null
+++ b/opencood/opencood/hypes_yaml/visualization.yaml
@@ -0,0 +1,56 @@
+# this yaml is only for visualization
+name: visualization
+
+yaml_parser: "load_voxel_params"
+root_dir: "opv2v_data_dumping/train"
+validate_dir: "opv2v_data_dumping/test"
+
+train_params:
+  batch_size: &batch_size 4
+  epoches: 100
+  eval_freq: 1
+  save_freq: 1
+
+fusion:
+  core_method: 'EarlyFusionDataset' # LateFusionDataset, EarlyFusionDataset, IntermediateFusionDataset supported
+  args: []
+
+# preprocess-related
+preprocess:
+  # options: BasePreprocessor, VoxelPreprocessor, BevPreprocessor
+  core_method: 'SpVoxelPreprocessor'
+  args:
+    voxel_size: &voxel_size [0.4, 0.4, 0.4]
+    max_points_per_voxel: &T 32
+    max_voxel_train: 36000
+    max_voxel_test: 70000
+  # lidar range for each individual cav.
+  cav_lidar_range: &cav_lidar [-140.8, -40, -3, 140.8, 40, 1]
+
+data_augment:
+  - NAME: random_world_flip
+    ALONG_AXIS_LIST: [ 'x' ]
+
+  - NAME: random_world_rotation
+    WORLD_ROT_ANGLE: [ -0.78539816, 0.78539816 ]
+
+  - NAME: random_world_scaling
+    WORLD_SCALE_RANGE: [ 0.95, 1.05 ]
+
+# anchor box related
+postprocess:
+  core_method: 'VoxelPostprocessor' # VoxelPostprocessor, BevPostprocessor supported
+  anchor_args:
+    cav_lidar_range: *cav_lidar
+    l: 3.9
+    w: 1.6
+    h: 1.56
+    r: [0, 90]
+    num: &achor_num 2
+  target_args:
+    pos_threshold: 0.6
+    neg_threshold: 0.45
+    score_threshold: 0.96
+  order: 'hwl' # hwl or lwh
+  max_num: 100 # maximum number of objects in a single frame. use this number to make sure different frames has the same dimension in the same batch
+  nms_thresh: 0.15
\ No newline at end of file
diff --git a/opencood/opencood/hypes_yaml/voxelnet_early_fusion.yaml b/opencood/opencood/hypes_yaml/voxelnet_early_fusion.yaml
new file mode 100644
index 00000000..43801c80
--- /dev/null
+++ b/opencood/opencood/hypes_yaml/voxelnet_early_fusion.yaml
@@ -0,0 +1,88 @@
+name: voxelnet_early_fusion
+
+yaml_parser: "load_voxel_params"
+root_dir: "opv2v_data_dumping/train"
+validate_dir: "opv2v_data_dumping/validate"
+
+train_params:
+  batch_size: &batch_size 4
+  epoches: 100
+  eval_freq: 1
+  save_freq: 1
+
+fusion:
+  core_method: 'EarlyFusionDataset' # LateFusionDataset, EarlyFusionDataset, IntermediateFusionDataset supported
+  args: []
+
+# preprocess-related
+preprocess:
+  # options: BasePreprocessor, VoxelPreprocessor, BevPreprocessor
+  core_method: 'SpVoxelPreprocessor'
+  args:
+    voxel_size: &voxel_size [0.4, 0.4, 0.4]
+    max_points_per_voxel: &T 32
+    max_voxel_train: 36000
+    max_voxel_test: 70000
+  # lidar range for each individual cav.
+  cav_lidar_range: &cav_lidar [-140.8, -40, -3, 140.8, 40, 1]
+
+data_augment:
+  - NAME: random_world_flip
+    ALONG_AXIS_LIST: [ 'x' ]
+
+  - NAME: random_world_rotation
+    WORLD_ROT_ANGLE: [ -0.78539816, 0.78539816 ]
+
+  - NAME: random_world_scaling
+    WORLD_SCALE_RANGE: [ 0.95, 1.05 ]
+
+# anchor box related
+postprocess:
+  core_method: 'VoxelPostprocessor' # VoxelPostprocessor, BevPostprocessor supported
+  anchor_args:
+    cav_lidar_range: *cav_lidar
+    l: 3.9
+    w: 1.6
+    h: 1.56
+    r: [0, 90]
+    num: &achor_num 2
+  target_args:
+    pos_threshold: 0.6
+    neg_threshold: 0.45
+    score_threshold: 0.96
+  order: 'hwl' # hwl or lwh
+  max_num: 100 # maximum number of objects in a single frame. use this number to make sure different frames has the same dimension in the same batch
+  nms_thresh: 0.20
+
+# model related
+model:
+  core_method: voxel_net # corresponding to VoxelNet
+  args:
+    N: *batch_size
+    T: *T
+    lidar_range: *cav_lidar
+    voxel_size: *voxel_size
+    anchor_num: *achor_num
+    pillar_vfe:
+      use_norm: true
+      with_distance: false
+      use_absolute_xyz: true
+      num_filters: [ 64 ]
+
+loss:
+  core_method: point_pillar_loss
+  args:
+    cls_weight: 1.0
+    reg: 2.0
+
+optimizer:
+  core_method: Adam
+  lr: 0.002
+  args:
+    eps: 1e-10
+    weight_decay: 1e-4
+
+lr_scheduler:
+  core_method: multistep #step, multistep and Exponential support
+  gamma: 0.1
+  step_size: [15, 30]
\ No newline at end of file
diff --git a/opencood/opencood/hypes_yaml/voxelnet_intermediate_fusion.yaml b/opencood/opencood/hypes_yaml/voxelnet_intermediate_fusion.yaml
new file mode 100644
index 00000000..22908289
--- /dev/null
+++ b/opencood/opencood/hypes_yaml/voxelnet_intermediate_fusion.yaml
@@ -0,0 +1,89 @@
+name: voxelnet_intermediate_fusion
+
+yaml_parser: "load_voxel_params"
+root_dir: "opv2v_data_dumping/train"
+validate_dir: "opv2v_data_dumping/validate"
+
+train_params:
+  batch_size: &batch_size 2
+  epoches: 30
+  eval_freq: 1
+  save_freq: 1
+
+fusion:
+  core_method: 'IntermediateFusionDataset' # LateFusionDataset, EarlyFusionDataset, IntermediateFusionDataset supported
+  args: []
+
+# preprocess-related
+preprocess:
+  # options: BasePreprocessor, VoxelPreprocessor, BevPreprocessor
+  core_method: 'SpVoxelPreprocessor'
+  args:
+    voxel_size: &voxel_size [0.4, 0.4, 0.4]
+    max_points_per_voxel: &T 32
+    max_voxel_train: 36000
+    max_voxel_test: 70000
+  # lidar range for each individual cav.
+  cav_lidar_range: &cav_lidar [-140.8, -41.6, -3, 140.8, 41.6, 1]
+
+data_augment:
+  - NAME: random_world_flip
+    ALONG_AXIS_LIST: [ 'x' ]
+
+  - NAME: random_world_rotation
+    WORLD_ROT_ANGLE: [ -0.78539816, 0.78539816 ]
+
+  - NAME: random_world_scaling
+    WORLD_SCALE_RANGE: [ 0.95, 1.05 ]
+
+# anchor box related
+postprocess:
+  core_method: 'VoxelPostprocessor' # VoxelPostprocessor, BevPostprocessor supported
+  anchor_args:
+    cav_lidar_range: *cav_lidar
+    l: 3.9
+    w: 1.6
+    h: 1.56
+    r: [0, 90]
+    num: &achor_num 2
+  target_args:
+    pos_threshold: 0.6
+    neg_threshold: 0.45
+    score_threshold: 0.25
+  order: 'hwl' # hwl or lwh
+  max_num: 100 # maximum number of objects in a single frame. use this number to make sure different frames has the same dimension in the same batch
+  nms_thresh: 0.15
+
+# model related
+model:
+  core_method: voxel_net_intermediate # corresponding to VoxelNet
+  args:
+    N: *batch_size
+    T: *T
+    lidar_range: *cav_lidar
+    voxel_size: *voxel_size
+    anchor_num: *achor_num
+    compression: 0
+    pillar_vfe:
+      use_norm: true
+      with_distance: false
+      use_absolute_xyz: true
+      num_filters: [ 64 ]
+
+loss:
+  core_method: point_pillar_loss
+  args:
+    cls_weight: 1.0
+    reg: 2.0
+
+optimizer:
+  core_method: Adam
+  lr: 0.002
+  args:
+    eps: 1e-10
+    weight_decay: 1e-4
+
+lr_scheduler:
+  core_method: multistep #step, multistep and Exponential support
+  gamma: 0.1
+  step_size: [15, 30]
\ No newline at end of file
diff --git a/opencood/opencood/hypes_yaml/voxelnet_late_fusion.yaml b/opencood/opencood/hypes_yaml/voxelnet_late_fusion.yaml
new file mode 100644
index 00000000..80b747f3
--- /dev/null
+++ b/opencood/opencood/hypes_yaml/voxelnet_late_fusion.yaml
@@ -0,0 +1,88 @@
+name: voxelnet_late_fusion
+
+yaml_parser: "load_voxel_params"
+root_dir: "opv2v_data_dumping/train"
+validate_dir: "opv2v_data_dumping/validate"
+
+train_params:
+  batch_size: &batch_size 4
+  epoches: 30
+  eval_freq: 1
+  save_freq: 1
+
+fusion:
+  core_method: 'LateFusionDataset' # LateFusionDataset, EarlyFusionDataset, IntermediateFusionDataset supported
+  args: []
+
+# preprocess-related
+preprocess:
+  # options: BasePreprocessor, VoxelPreprocessor, BevPreprocessor
+  core_method: 'SpVoxelPreprocessor'
+  args:
+    voxel_size: &voxel_size [0.4, 0.4, 0.4]
+    max_points_per_voxel: &T 32
+    max_voxel_train: 16000
+    max_voxel_test: 40000
+  # lidar range for each individual cav.
+  cav_lidar_range: &cav_lidar [-70.4, -40, -3, 70.4, 40, 1]
+
+data_augment:
+  - NAME: random_world_flip
+    ALONG_AXIS_LIST: [ 'x' ]
+
+  - NAME: random_world_rotation
+    WORLD_ROT_ANGLE: [ -0.78539816, 0.78539816 ]
+
+  - NAME: random_world_scaling
+    WORLD_SCALE_RANGE: [ 0.95, 1.05 ]
+
+# anchor box related
+postprocess:
+  core_method: 'VoxelPostprocessor' # VoxelPostprocessor, BevPostprocessor supported
+  anchor_args:
+    cav_lidar_range: *cav_lidar
+    l: 3.9
+    w: 1.6
+    h: 1.56
+    r: [0, 90]
+    num: &achor_num 2
+  target_args:
+    pos_threshold: 0.6
+    neg_threshold: 0.45
+    score_threshold: 0.25
+  order: 'hwl' # hwl or lwh
+  max_num: 100 # maximum number of objects in a single frame. use this number to make sure different frames has the same dimension in the same batch
+  nms_thresh: 0.05
+
+# model related
+model:
+  core_method: voxel_net # corresponding to VoxelNet
+  args:
+    N: *batch_size
+    T: *T
+    lidar_range: *cav_lidar
+    voxel_size: *voxel_size
+    anchor_num: *achor_num
+    pillar_vfe:
+      use_norm: true
+      with_distance: false
+      use_absolute_xyz: true
+      num_filters: [ 64 ]
+
+loss:
+  core_method: point_pillar_loss
+  args:
+    cls_weight: 1.0
+    reg: 2.0
+
+optimizer:
+  core_method: Adam
+  lr: 0.002
+  args:
+    eps: 1e-10
+    weight_decay: 1e-4
+
+lr_scheduler:
+  core_method: multistep #step, multistep and Exponential support
+  gamma: 0.1
+  step_size: [15, 30]
\ No newline at end of file
diff --git a/opencood/opencood/hypes_yaml/yaml_utils.py b/opencood/opencood/hypes_yaml/yaml_utils.py
new file mode 100644
index 00000000..682c5a4e
--- /dev/null
+++ b/opencood/opencood/hypes_yaml/yaml_utils.py
@@ -0,0 +1,264 @@
+# -*- coding: utf-8 -*-
+# Author: Runsheng Xu <rxx3386@ucla.edu>, Hao Xiang <haxiang@g.ucla.edu>
+# License: TDG-Attribution-NonCommercial-NoDistrib
+
+
+import re
+import yaml
+import os
+import math
+
+import numpy as np
+
+
+def load_yaml(file, opt=None):
+    """
+    Load yaml file and return a dictionary.
+
+    Parameters
+    ----------
+    file : string
+        yaml file path.
+
+    opt : argparser
+         Argparser.
+    Returns
+    -------
+    param : dict
+        A dictionary that contains defined parameters.
+    """
+    if opt and opt.model_dir:
+        file = os.path.join(opt.model_dir, 'config.yaml')
+
+    stream = open(file, 'r')
+    loader = yaml.Loader
+    loader.add_implicit_resolver(
+        u'tag:yaml.org,2002:float',
+        re.compile(u'''^(?:
+         [-+]?(?:[0-9][0-9_]*)\\.[0-9_]*(?:[eE][-+]?[0-9]+)?
+        |[-+]?(?:[0-9][0-9_]*)(?:[eE][-+]?[0-9]+)
+        |\\.[0-9_]+(?:[eE][-+][0-9]+)?
+        |[-+]?[0-9][0-9_]*(?::[0-5]?[0-9])+\\.[0-9_]*
+        |[-+]?\\.(?:inf|Inf|INF)
+        |\\.(?:nan|NaN|NAN))$''', re.X),
+        list(u'-+0123456789.'))
+    param = yaml.load(stream, Loader=loader)
+    if "yaml_parser" in param:
+        param = eval(param["yaml_parser"])(param)
+
+    return param
+
+
+def load_voxel_params(param):
+    """
+    Based on the lidar range and resolution of voxel, calcuate the anchor box
+    and target resolution.
+
+    Parameters
+    ----------
+    param : dict
+        Original loaded parameter dictionary.
+
+    Returns
+    -------
+    param : dict
+        Modified parameter dictionary with new attribute `anchor_args[W][H][L]`
+    """
+    anchor_args = param['postprocess']['anchor_args']
+    cav_lidar_range = anchor_args['cav_lidar_range']
+    voxel_size = param['preprocess']['args']['voxel_size']
+
+    vw = voxel_size[0]
+    vh = voxel_size[1]
+    vd = voxel_size[2]
+
+    anchor_args['vw'] = vw
+    anchor_args['vh'] = vh
+    anchor_args['vd'] = vd
+
+    anchor_args['W'] = int((cav_lidar_range[3] - cav_lidar_range[0]) / vw)
+    anchor_args['H'] = int((cav_lidar_range[4] - cav_lidar_range[1]) / vh)
+    anchor_args['D'] = int((cav_lidar_range[5] - cav_lidar_range[2]) / vd)
+
+    param['postprocess'].update({'anchor_args': anchor_args})
+
+    # sometimes we just want to visualize the data without implementing model
+    if 'model' in param:
+        param['model']['args']['W'] = anchor_args['W']
+        param['model']['args']['H'] = anchor_args['H']
+        param['model']['args']['D'] = anchor_args['D']
+
+    return param
+
+
+def load_point_pillar_params(param):
+    """
+    Based on the lidar range and resolution of voxel, calcuate the anchor box
+    and target resolution.
+
+    Parameters
+    ----------
+    param : dict
+        Original loaded parameter dictionary.
+
+    Returns
+    -------
+    param : dict
+        Modified parameter dictionary with new attribute.
+    """
+    cav_lidar_range = param['preprocess']['cav_lidar_range']
+    voxel_size = param['preprocess']['args']['voxel_size']
+
+    grid_size = (np.array(cav_lidar_range[3:6]) - np.array(
+        cav_lidar_range[0:3])) / \
+                np.array(voxel_size)
+    grid_size = np.round(grid_size).astype(np.int64)
+    param['model']['args']['point_pillar_scatter']['grid_size'] = grid_size
+
+    anchor_args = param['postprocess']['anchor_args']
+
+    vw = voxel_size[0]
+    vh = voxel_size[1]
+    vd = voxel_size[2]
+
+    anchor_args['vw'] = vw
+    anchor_args['vh'] = vh
+    anchor_args['vd'] = vd
+
+    anchor_args['W'] = math.ceil((cav_lidar_range[3] - cav_lidar_range[0]) / vw)
+    anchor_args['H'] = math.ceil((cav_lidar_range[4] - cav_lidar_range[1]) / vh)
+    anchor_args['D'] = math.ceil((cav_lidar_range[5] - cav_lidar_range[2]) / vd)
+
+    param['postprocess'].update({'anchor_args': anchor_args})
+
+    return param
+
+
+def load_second_params(param):
+    """
+    Based on the lidar range and resolution of voxel, calcuate the anchor box
+    and target resolution.
+
+    Parameters
+    ----------
+    param : dict
+        Original loaded parameter dictionary.
+
+    Returns
+    -------
+    param : dict
+        Modified parameter dictionary with new attribute.
+    """
+    cav_lidar_range = param['preprocess']['cav_lidar_range']
+    voxel_size = param['preprocess']['args']['voxel_size']
+
+    grid_size = (np.array(cav_lidar_range[3:6]) - np.array(
+        cav_lidar_range[0:3])) / \
+                np.array(voxel_size)
+    grid_size = np.round(grid_size).astype(np.int64)
+    param['model']['args']['grid_size'] = grid_size
+
+    anchor_args = param['postprocess']['anchor_args']
+
+    vw = voxel_size[0]
+    vh = voxel_size[1]
+    vd = voxel_size[2]
+
+    anchor_args['vw'] = vw
+    anchor_args['vh'] = vh
+    anchor_args['vd'] = vd
+
+    anchor_args['W'] = int((cav_lidar_range[3] - cav_lidar_range[0]) / vw)
+    anchor_args['H'] = int((cav_lidar_range[4] - cav_lidar_range[1]) / vh)
+    anchor_args['D'] = int((cav_lidar_range[5] - cav_lidar_range[2]) / vd)
+
+    param['postprocess'].update({'anchor_args': anchor_args})
+
+    return param
+
+
+def load_bev_params(param):
+    """
+    Load bev related geometry parameters s.t. boundary, resolutions, input
+    shape, target shape etc.
+
+    Parameters
+    ----------
+    param : dict
+        Original loaded parameter dictionary.
+
+    Returns
+    -------
+    param : dict
+        Modified parameter dictionary with new attribute `geometry_param`.
+
+    """
+    res = param["preprocess"]["args"]["res"]
+    L1, W1, H1, L2, W2, H2 = param["preprocess"]["cav_lidar_range"]
+    downsample_rate = param["preprocess"]["args"]["downsample_rate"]
+
+    def f(low, high, r):
+        return int((high - low) / r)
+
+    input_shape = (
+        int((f(L1, L2, res))),
+        int((f(W1, W2, res))),
+        int((f(H1, H2, res)) + 1)
+    )
+    label_shape = (
+        int(input_shape[0] / downsample_rate),
+        int(input_shape[1] / downsample_rate),
+        7
+    )
+    geometry_param = {
+        'L1': L1,
+        'L2': L2,
+        'W1': W1,
+        'W2': W2,
+        'H1': H1,
+        'H2': H2,
+        "downsample_rate": downsample_rate,
+        "input_shape": input_shape,
+        "label_shape": label_shape,
+        "res": res
+    }
+    param["preprocess"]["geometry_param"] = geometry_param
+    param["postprocess"]["geometry_param"] = geometry_param
+    param["model"]["args"]["geometry_param"] = geometry_param
+    return param
+
+
+def save_yaml(data, save_name):
+    """
+    Save the dictionary into a yaml file.
+
+    Parameters
+    ----------
+    data : dict
+        The dictionary contains all data.
+
+    save_name : string
+        Full path of the output yaml file.
+    """
+
+    with open(save_name, 'w') as outfile:
+        yaml.dump(data, outfile, default_flow_style=False)
+
+
+def save_yaml_wo_overwriting(data, save_name):
+    """
+    Save the yaml file without overwriting the existing one.
+
+    Parameters
+    ----------
+    data : dict
+        The dictionary contains all data.
+
+    save_name : string
+        Full path of the output yaml file.
+    """
+    if os.path.exists(save_name):
+        prev_data = load_yaml(save_name)
+        data = {**data, **prev_data}
+
+    save_yaml(data, save_name)
\ No newline at end of file
diff --git a/opencood/opencood/loss/__init__.py b/opencood/opencood/loss/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/opencood/opencood/loss/ciassd_loss.py b/opencood/opencood/loss/ciassd_loss.py
new file mode 100644
index 00000000..6d0eb130
--- /dev/null
+++ b/opencood/opencood/loss/ciassd_loss.py
@@ -0,0 +1,227 @@
+import torch
+import torch.nn as nn
+
+from opencood.data_utils.post_processor.voxel_postprocessor import VoxelPostprocessor
+from opencood.pcdet_utils.iou3d_nms.iou3d_nms_utils import aligned_boxes_iou3d_gpu
+
+
+class CiassdLoss(nn.Module):
+    def __init__(self, args):
+        super(CiassdLoss, self).__init__()
+        self.pos_cls_weight = args['pos_cls_weight']
+        self.encode_rad_error_by_sin = args['encode_rad_error_by_sin']
+        self.cls = args['cls']
+        self.reg = args['reg']
+        self.iou = args['iou']
+        self.dir = args['dir']
+        self.loss_dict = {}
+        ##
+        self.num_cls = 2
+        self.box_codesize = 7
+
+    def forward(self, output_dict, label_dict):
+        """
+        Parameters
+        ----------
+        output_dict : dict
+        target_dict : dict
+        """
+        preds_dict = output_dict['preds_dict_stage1']
+        target_dict = label_dict['stage1']
+        if 'record_len' in output_dict:
+            batch_size = int(output_dict['record_len'].sum())
+        else:
+            batch_size = output_dict['batch_size']
+
+        # ########
+        # pred = torch.sigmoid(preds_dict['cls_preds'][0]).sum(dim=0).cpu().detach().numpy()
+        # tagt_pos = target_dict['pos_equal_one'][0].sum(dim=-1).cpu().detach().numpy()
+        # tagt_neg = target_dict['neg_equal_one'][0].sum(dim=-1).cpu().detach().numpy()
+        # import matplotlib.pyplot as plt
+        #
+        # fig = plt.figure(figsize=(18, 8))
+        # ax1 = fig.add_subplot(3, 1, 1)
+        # ax1.imshow(pred, cmap='cool')
+        # ax2 = fig.add_subplot(3, 1, 2)
+        # ax2.imshow(tagt_pos, cmap='cool')
+        # ax3 = fig.add_subplot(3, 1, 3)
+        # ax3.imshow(tagt_neg, cmap='cool')
+        # plt.show()
+        # plt.close()
+        # #########
+
+        cls_labls = target_dict['pos_equal_one'].view(batch_size, -1,  self.num_cls - 1)
+        positives = cls_labls > 0
+        negatives = target_dict['neg_equal_one'].view(batch_size, -1,  self.num_cls - 1) > 0
+        cared = torch.logical_or(positives, negatives)
+        cls_labls = cls_labls * cared.type_as(cls_labls)
+        # num_normalizer = cared.sum(1, keepdim=True)
+        pos_normalizer = positives.sum(1, keepdim=True).float()
+
+        # cls loss
+        cls_preds = preds_dict["cls_preds"].permute(0, 2, 3, 1).contiguous() \
+                    .view(batch_size, -1,  self.num_cls - 1)
+        cls_weights = positives * self.pos_cls_weight + negatives * 1.0
+        cls_weights /= torch.clamp(pos_normalizer, min=1.0)
+        cls_loss = sigmoid_focal_loss(cls_preds, cls_labls, weights=cls_weights, **self.cls)
+        cls_loss_reduced = cls_loss.sum() * self.cls['weight'] / batch_size
+
+        # reg loss
+        reg_weights = positives / torch.clamp(pos_normalizer, min=1.0)
+        reg_preds = preds_dict['box_preds'].permute(0, 2, 3, 1).contiguous().view(batch_size, -1, self.box_codesize)
+        reg_targets = target_dict['targets'].view(batch_size, -1, self.box_codesize)
+        if self.encode_rad_error_by_sin:
+            reg_preds, reg_targets = add_sin_difference(reg_preds, reg_targets)
+        reg_loss = weighted_smooth_l1_loss(reg_preds, reg_targets, weights=reg_weights, sigma=self.reg['sigma'])
+        reg_loss_reduced = reg_loss.sum() * self.reg['weight'] / batch_size
+
+        # dir loss
+        dir_targets = get_direction_target(reg_targets, output_dict['anchor_box'])
+        dir_logits = preds_dict["dir_cls_preds"].permute(0, 2, 3, 1).contiguous().view(batch_size, -1, 2)
+        dir_weights = (cls_labls > 0).type_as(dir_logits).view(batch_size, -1)
+        dir_weights /= torch.clamp(dir_weights.sum(-1, keepdim=True), min=1.0)  # [8, 70400], averaged in sample.
+        dir_loss = softmax_cross_entropy_with_logits(dir_logits.view(-1, self.num_cls), dir_targets.view(-1, self.num_cls))
+        dir_loss = dir_loss.view(dir_weights.shape) * dir_weights
+        dir_loss_reduced = dir_loss.sum() * self.dir['weight'] / batch_size
+
+        # iou loss
+        iou_preds = preds_dict["iou_preds"].permute(0, 2, 3, 1).contiguous()
+        pos_pred_mask = reg_weights.squeeze(dim=-1) > 0 # (4, 70400)
+        iou_pos_preds = iou_preds.view(batch_size, -1)[pos_pred_mask]
+        boxes3d_pred = VoxelPostprocessor.delta_to_boxes3d(preds_dict['box_preds'].permute(0, 2, 3, 1).contiguous().detach(),
+                                                           output_dict['anchor_box'],
+                                                           False)[pos_pred_mask]
+        boxes3d_tgt = VoxelPostprocessor.delta_to_boxes3d(target_dict['targets'],
+                                                          output_dict['anchor_box'],
+                                                          False)[pos_pred_mask]
+        iou_weights = reg_weights[pos_pred_mask].view(-1)
+        iou_pos_targets = aligned_boxes_iou3d_gpu(boxes3d_pred.float()[:, [0, 1, 2, 5, 4, 3, 6]],
+                                                  boxes3d_tgt.float()[:, [0, 1, 2, 5, 4, 3, 6]]).detach().squeeze()
+        iou_pos_targets = 2 * iou_pos_targets.view(-1) - 1
+        iou_loss = weighted_smooth_l1_loss(iou_pos_preds, iou_pos_targets, weights=iou_weights, sigma=self.iou['sigma'])
+
+        iou_loss_reduced = iou_loss.sum() * self.iou['weight'] / batch_size
+
+        loss = cls_loss_reduced + reg_loss_reduced + dir_loss_reduced + iou_loss_reduced
+
+        self.loss_dict.update({
+            'total_loss': loss,
+            'cls_loss': cls_loss_reduced,
+            'reg_loss': reg_loss_reduced,
+            'dir_loss': dir_loss_reduced,
+            'iou_loss': iou_loss_reduced
+        })
+
+        return loss
+
+    def logging(self, epoch, batch_id, batch_len, writer, pbar=None):
+        """
+        Print out  the loss function for current iteration.
+
+        Parameters
+        ----------
+        epoch : int
+            Current epoch for training.
+        batch_id : int
+            The current batch.
+        batch_len : int
+            Total batch length in one iteration of training,
+        writer : SummaryWriter
+            Used to visualize on tensorboard
+        """
+        total_loss = self.loss_dict['total_loss']
+        reg_loss = self.loss_dict['reg_loss']
+        cls_loss = self.loss_dict['cls_loss']
+        dir_loss = self.loss_dict['dir_loss']
+        iou_loss = self.loss_dict['iou_loss']
+        if (batch_id + 1) % 10 == 0:
+            print("[epoch %d][%d/%d], || Loss: %.4f || Cls: %.4f"
+                  " || Loc: %.4f || Dir: %.4f || Iou: %.4f" % (
+                      epoch, batch_id + 1, batch_len,
+                      total_loss.item(), cls_loss.item(), reg_loss.item(), dir_loss.item(), iou_loss.item()))
+
+        writer.add_scalar('Regression_loss', reg_loss.item(),
+                          epoch*batch_len + batch_id)
+        writer.add_scalar('Confidence_loss', cls_loss.item(),
+                          epoch*batch_len + batch_id)
+        writer.add_scalar('Direction_loss', dir_loss.item(),
+                          epoch*batch_len + batch_id)
+        writer.add_scalar('Iou_loss', iou_loss.item(),
+                          epoch*batch_len + batch_id)
+
+
+def add_sin_difference(boxes1, boxes2):
+    rad_pred_encoding = torch.sin(boxes1[..., -1:]) * torch.cos(boxes2[..., -1:])   # ry -> sin(pred_ry)*cos(gt_ry)
+    rad_gt_encoding = torch.cos(boxes1[..., -1:]) * torch.sin(boxes2[..., -1:])     # ry -> cos(pred_ry)*sin(gt_ry)
+    res_boxes1 = torch.cat([boxes1[..., :-1], rad_pred_encoding], dim=-1)
+    res_boxes2 = torch.cat([boxes2[..., :-1], rad_gt_encoding], dim=-1)
+    return res_boxes1, res_boxes2
+
+
+def get_direction_target(reg_targets, anchors, one_hot=True, dir_offset=0.0):
+    """
+    Generate targets for bounding box direction classification.
+
+    Parameters
+    ----------
+    anchors: torch.Tensor
+        shape as (H*W*2, 7) or (H, W, 2, 7)
+    reg_targets: torch.Tensor
+        shape as (B, H*W*2, 7)
+
+    Returns
+    -------
+    dir_cls_targets : torch.Tensor
+        [batch_size, w*h*num_anchor_per_pos, 2]
+    """
+    batch_size = reg_targets.shape[0]
+    anchors = anchors.view(1,  -1, anchors.shape[-1]).repeat(batch_size, 1, 1)
+    rot_gt = reg_targets[..., -1] + anchors[..., -1]  # [4, 70400]
+    dir_cls_targets = ((rot_gt - dir_offset) > 0).long()  # [4, 70400]
+    if one_hot:
+        dir_cls_targets = one_hot_f(dir_cls_targets, 2, dtype=anchors.dtype)
+    return dir_cls_targets
+
+
+def one_hot_f(tensor, depth, dim=-1, on_value=1.0, dtype=torch.float32):
+    tensor_onehot = torch.zeros(*list(tensor.shape), depth, dtype=dtype, device=tensor.device) # [4, 70400, 2]
+    tensor_onehot.scatter_(dim, tensor.unsqueeze(dim).long(), on_value)                        # [4, 70400, 2]
+    return tensor_onehot
+
+
+def sigmoid_focal_loss(preds, targets, weights=None, **kwargs):
+    assert 'gamma' in kwargs and 'alpha' in kwargs
+    # sigmoid cross entropy with logits
+    # more details: https://www.tensorflow.org/api_docs/python/tf/nn/sigmoid_cross_entropy_with_logits
+    per_entry_cross_ent = torch.clamp(preds, min=0) - preds * targets.type_as(preds)
+    per_entry_cross_ent += torch.log1p(torch.exp(-torch.abs(preds)))
+    # focal loss
+    prediction_probabilities = torch.sigmoid(preds)
+    p_t = (targets * prediction_probabilities) + ((1 - targets) * (1 - prediction_probabilities))
+    modulating_factor = torch.pow(1.0 - p_t, kwargs['gamma'])
+    alpha_weight_factor = targets * kwargs['alpha'] + (1 - targets) * (1 - kwargs['alpha'])
+
+    loss = modulating_factor * alpha_weight_factor * per_entry_cross_ent
+    if weights is not None:
+        loss *= weights
+    return loss
+
+
+def softmax_cross_entropy_with_logits(logits, labels):
+    param = list(range(len(logits.shape)))
+    transpose_param = [0] + [param[-1]] + param[1:-1]
+    logits = logits.permute(*transpose_param)
+    loss_ftor = torch.nn.CrossEntropyLoss(reduction="none")
+    loss = loss_ftor(logits, labels.max(dim=-1)[1])
+    return loss
+
+
+def weighted_smooth_l1_loss(preds, targets, sigma=3.0, weights=None):
+    diff = preds - targets
+    abs_diff = torch.abs(diff)
+    abs_diff_lt_1 = torch.le(abs_diff, 1 / (sigma ** 2)).type_as(abs_diff)
+    loss = abs_diff_lt_1 * 0.5 * torch.pow(abs_diff * sigma, 2) + \
+               (abs_diff - 0.5 / (sigma ** 2)) * (1.0 - abs_diff_lt_1)
+    if weights is not None:
+        loss *= weights
+    return loss
\ No newline at end of file
diff --git a/opencood/opencood/loss/fpvrcnn_loss.py b/opencood/opencood/loss/fpvrcnn_loss.py
new file mode 100644
index 00000000..037c76c2
--- /dev/null
+++ b/opencood/opencood/loss/fpvrcnn_loss.py
@@ -0,0 +1,193 @@
+import torch
+from torch import nn
+import numpy as np
+from opencood.loss.ciassd_loss import CiassdLoss, weighted_smooth_l1_loss
+
+
+class FpvrcnnLoss(nn.Module):
+    def __init__(self, args):
+        super(FpvrcnnLoss, self).__init__()
+        self.ciassd_loss = CiassdLoss(args['stage1'])
+        self.cls = args['stage2']['cls']
+        self.reg = args['stage2']['reg']
+        self.iou = args['stage2']['iou']
+        self.loss_dict = {}
+
+    def forward(self, output_dict, label_dict):
+        """
+        Parameters
+        ----------
+        output_dict : dict
+        target_dict : dict
+        """
+        ciassd_loss = self.ciassd_loss(output_dict, label_dict)
+
+        # only update ciassd if no bbox is detected in the first stage
+        if 'fpvrcnn_out' not in output_dict:
+            self.loss_dict = {
+                'loss': ciassd_loss,
+            }
+            return ciassd_loss
+
+        # rcnn out
+        rcnn_cls = output_dict['fpvrcnn_out']['rcnn_cls'].view(1, -1, 1)
+        rcnn_iou = output_dict['fpvrcnn_out']['rcnn_iou'].view(1, -1, 1)
+        rcnn_reg = output_dict['fpvrcnn_out']['rcnn_reg'].view(1, -1, 7)
+
+        tgt_cls = output_dict['rcnn_label_dict']['cls_tgt'].view(1, -1, 1)
+        tgt_iou = output_dict['rcnn_label_dict']['iou_tgt'].view(1, -1, 1)
+        tgt_reg = output_dict['rcnn_label_dict']['reg_tgt'].view(1, -1, 7)
+
+        pos_norm = tgt_cls.sum()
+        # cls loss
+        loss_cls = weighted_sigmoid_binary_cross_entropy(rcnn_cls, tgt_cls)
+
+        # iou loss
+        # TODO: also count the negative samples
+        loss_iou = weighted_smooth_l1_loss(rcnn_iou, tgt_iou,
+                                           weights=tgt_cls).mean()
+
+        # regression loss
+        # Target resampling : Generate a weights mask to force the regressor concentrate on low iou predictions
+        # sample 50% with iou>0.7 and 50% < 0.7
+        weights = torch.ones(tgt_iou.shape, device=tgt_iou.device)
+        weights[tgt_cls == 0] = 0
+        neg = torch.logical_and(tgt_iou < 0.7, tgt_cls != 0)
+        pos = torch.logical_and(tgt_iou >= 0.7, tgt_cls != 0)
+        num_neg = int(neg.sum(dim=1))
+        num_pos = int(pos.sum(dim=1))
+        num_pos_smps = max(num_neg, 2)
+        pos_indices = torch.where(pos)[1]
+        not_selsected = torch.randperm(num_pos)[:num_pos - num_pos_smps]
+        # not_selsected_indices = pos_indices[not_selsected]
+        weights[:, pos_indices[not_selsected]] = 0
+        loss_reg = weighted_smooth_l1_loss(rcnn_reg, tgt_reg,
+                                           weights=weights / max(weights.sum(),
+                                                                 1)).sum()
+
+        loss_cls_reduced = loss_cls * self.cls['weight']
+        loss_iou_reduced = loss_iou * self.iou['weight']
+        loss_reg_reduced = loss_reg * self.reg['weight']
+
+        # if torch.isnan(loss_reg_reduced):
+        #     print('debug')
+
+        rcnn_loss = loss_cls_reduced + loss_iou_reduced + loss_reg_reduced
+        loss = rcnn_loss + ciassd_loss
+
+        self.loss_dict.update({
+            'loss': loss,
+            'rcnn_loss': rcnn_loss,
+            'cls_loss': loss_cls_reduced,
+            'iou_loss': loss_iou_reduced,
+            'reg_loss': loss_reg_reduced,
+        })
+
+        return loss
+
+    def logging(self, epoch, batch_id, batch_len, writer, pbar=None):
+        """
+        Print out  the loss function for current iteration.
+
+        Parameters
+        ----------
+        epoch : int
+            Current epoch for training.
+        batch_id : int
+            The current batch.
+        batch_len : int
+            Total batch length in one iteration of training,
+        writer : SummaryWriter
+            Used to visualize on tensorboard
+        """
+        ciassd_loss_dict = self.ciassd_loss.loss_dict
+        ciassd_total_loss = ciassd_loss_dict['total_loss']
+        reg_loss = ciassd_loss_dict['reg_loss']
+        cls_loss = ciassd_loss_dict['cls_loss']
+        dir_loss = ciassd_loss_dict['dir_loss']
+        iou_loss = ciassd_loss_dict['iou_loss']
+
+        if (batch_id + 1) % 10 == 0:
+            str_to_print = "[epoch %d][%d/%d], || Loss: %.4f || Ciassd: %.4f " \
+                           "|| Cls1: %.4f || Loc1: %.4f || Dir1: %.4f || Iou1: %.4f" % (
+                               epoch, batch_id + 1, batch_len, self.loss_dict['loss'],
+                               ciassd_total_loss.item(), cls_loss.item(), reg_loss.item(),
+                               dir_loss.item(), iou_loss.item(),
+                               )
+            if 'rcnn_loss' in self.loss_dict:
+                str_to_print += " || Rcnn: %.4f || Cls2: %.4f || Loc2: %.4f || Iou2: %.4f" % (
+                        self.loss_dict['rcnn_loss'],
+                        self.loss_dict['cls_loss'].item(),
+                        self.loss_dict['reg_loss'].item(),
+                        self.loss_dict['iou_loss'].item(),
+                    )
+            print(str_to_print)
+
+        writer.add_scalar('Ciassd_regression_loss', reg_loss.item(),
+                          epoch * batch_len + batch_id)
+        writer.add_scalar('Ciassd_Confidence_loss', cls_loss.item(),
+                          epoch * batch_len + batch_id)
+        writer.add_scalar('Ciassd_Direction_loss', dir_loss.item(),
+                          epoch * batch_len + batch_id)
+        writer.add_scalar('Ciassd_Iou_loss', iou_loss.item(),
+                          epoch * batch_len + batch_id)
+        writer.add_scalar('Ciassd_loss', ciassd_total_loss.item(),
+                          epoch * batch_len + batch_id)
+        if 'rcnn_loss' in self.loss_dict:
+            writer.add_scalar('Rcnn_regression_loss',
+                              self.loss_dict['reg_loss'].item(),
+                              epoch * batch_len + batch_id)
+            writer.add_scalar('Rcnn_Confidence_loss',
+                              self.loss_dict['cls_loss'].item(),
+                              epoch * batch_len + batch_id)
+            writer.add_scalar('Rcnn_Iou_loss',
+                              self.loss_dict['iou_loss'].item(),
+                              epoch * batch_len + batch_id)
+            writer.add_scalar('Rcnn_loss', self.loss_dict['rcnn_loss'].item(),
+                              epoch * batch_len + batch_id)
+            writer.add_scalar('Total_loss', self.loss_dict['loss'].item(),
+                              epoch * batch_len + batch_id)
+
+
+def weighted_sigmoid_binary_cross_entropy(preds, tgts, weights=None,
+                                          class_indices=None):
+    if weights is not None:
+        weights = weights.unsqueeze(-1)
+    if class_indices is not None:
+        weights *= (
+            indices_to_dense_vector(class_indices, preds.shape[2])
+                .view(1, 1, -1)
+                .type_as(preds)
+        )
+    per_entry_cross_ent = nn.functional.binary_cross_entropy_with_logits(preds,
+                                                                         tgts,
+                                                                         weights)
+    return per_entry_cross_ent
+
+
+def indices_to_dense_vector(
+        indices, size, indices_value=1.0, default_value=0, dtype=np.float32
+):
+    """Creates dense vector with indices set to specific value and rest to zeros.
+
+    This function exists because it is unclear if it is safe to use
+        tf.sparse_to_dense(indices, [size], 1, validate_indices=False)
+    with indices which are not ordered.
+    This function accepts a dynamic size (e.g. tf.shape(tensor)[0])
+
+    Args:
+        indices: 1d Tensor with integer indices which are to be set to
+            indices_values.
+        size: scalar with size (integer) of output Tensor.
+        indices_value: values of elements specified by indices in the output vector
+        default_value: values of other elements in the output vector.
+        dtype: data type.
+
+    Returns:
+        dense 1D Tensor of shape [size] with indices set to indices_values and the
+            rest set to default_value.
+    """
+    dense = torch.zeros(size).fill_(default_value)
+    dense[indices] = indices_value
+
+    return dense
diff --git a/opencood/opencood/loss/pixor_loss.py b/opencood/opencood/loss/pixor_loss.py
new file mode 100644
index 00000000..3bcbfd41
--- /dev/null
+++ b/opencood/opencood/loss/pixor_loss.py
@@ -0,0 +1,115 @@
+# -*- coding: utf-8 -*-
+# Author: Hao Xiang <haxiang@g.ucla.edu>
+# License: TDG-Attribution-NonCommercial-NoDistrib
+
+
+from functools import reduce
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class PixorLoss(nn.Module):
+    def __init__(self, args):
+        super(PixorLoss, self).__init__()
+        self.alpha = args["alpha"]
+        self.beta = args["beta"]
+        self.loss_dict = {}
+
+    def forward(self, output_dict, target_dict):
+        """
+        Compute loss for pixor network
+        Parameters
+        ----------
+        output_dict : dict
+           The dictionary that contains the output.
+
+        target_dict : dict
+           The dictionary that contains the target.
+
+        Returns
+        -------
+        total_loss : torch.Tensor
+            Total loss.
+
+        """
+        targets = target_dict["label_map"]
+        cls_preds, loc_preds = output_dict["cls"], output_dict["reg"]
+
+        cls_targets, loc_targets = targets.split([1, 6], dim=1)
+        pos_count = cls_targets.sum()
+        neg_count = (cls_targets == 0).sum()
+        w1, w2 = neg_count / (pos_count + neg_count), pos_count / (
+                    pos_count + neg_count)
+        weights = torch.ones_like(cls_preds.reshape(-1))
+        weights[cls_targets.reshape(-1) == 1] = w1
+        weights[cls_targets.reshape(-1) == 0] = w2
+        # cls_targets = cls_targets.float()
+        # cls_loss = F.binary_cross_entropy_with_logits(input=cls_preds.reshape(-1), target=cls_targets.reshape(-1), weight=weights,
+        #                                               reduction='mean')
+        cls_loss = F.binary_cross_entropy_with_logits(
+            input=cls_preds, target=cls_targets,
+            reduction='mean')
+        pos_pixels = cls_targets.sum()
+
+        loc_loss = F.smooth_l1_loss(cls_targets * loc_preds,
+                                    cls_targets * loc_targets,
+                                    reduction='sum')
+        loc_loss = loc_loss / pos_pixels if pos_pixels > 0 else loc_loss
+
+        total_loss = self.alpha * cls_loss + self.beta * loc_loss
+
+        self.loss_dict.update({'total_loss': total_loss,
+                               'reg_loss': loc_loss,
+                               'cls_loss': cls_loss})
+
+        return total_loss
+
+    def logging(self, epoch, batch_id, batch_len, writer, pbar=None):
+        """
+        Print out  the loss function for current iteration.
+
+        Parameters
+        ----------
+        epoch : int
+            Current epoch for training.
+        batch_id : int
+            The current batch.
+        batch_len : int
+            Total batch length in one iteration of training,
+        writer : SummaryWriter
+            Used to visualize on tensorboard
+        """
+        total_loss = self.loss_dict['total_loss']
+        reg_loss = self.loss_dict['reg_loss']
+        cls_loss = self.loss_dict['cls_loss']
+
+        if pbar is None:
+            print("[epoch %d][%d/%d], || Loss: %.4f || Conf Loss: %.4f"
+                " || Loc Loss: %.4f" % (
+                    epoch, batch_id + 1, batch_len,
+                    total_loss.item(), cls_loss.item(), reg_loss.item()))
+        else:
+            pbar.set_description("[epoch %d][%d/%d], || Loss: %.4f || Conf Loss: %.4f"
+                  " || Loc Loss: %.4f" % (
+                      epoch, batch_id + 1, batch_len,
+                      total_loss.item(), cls_loss.item(), reg_loss.item()))
+
+        writer.add_scalar('Regression_loss', reg_loss.item(),
+                          epoch * batch_len + batch_id)
+        writer.add_scalar('Confidence_loss', cls_loss.item(),
+                          epoch * batch_len + batch_id)
+
+
+def test():
+    torch.manual_seed(0)
+    loss = PixorLoss(None)
+    pred = torch.sigmoid(torch.randn(1, 7, 2, 3))
+    label = torch.zeros(1, 7, 2, 3)
+    loss = loss(pred, label)
+    print(loss)
+
+
+if __name__ == "__main__":
+    test()
diff --git a/opencood/opencood/loss/point_pillar_loss.py b/opencood/opencood/loss/point_pillar_loss.py
new file mode 100644
index 00000000..8b061806
--- /dev/null
+++ b/opencood/opencood/loss/point_pillar_loss.py
@@ -0,0 +1,248 @@
+# -*- coding: utf-8 -*-
+# Author: OpenPCDet, Runsheng Xu <rxx3386@ucla.edu>
+# License: TDG-Attribution-NonCommercial-NoDistrib
+
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import numpy as np
+
+
+class WeightedSmoothL1Loss(nn.Module):
+    """
+    Code-wise Weighted Smooth L1 Loss modified based on fvcore.nn.smooth_l1_loss
+    https://github.com/facebookresearch/fvcore/blob/master/fvcore/nn/smooth_l1_loss.py
+                  | 0.5 * x ** 2 / beta   if abs(x) < beta
+    smoothl1(x) = |
+                  | abs(x) - 0.5 * beta   otherwise,
+    where x = input - target.
+    """
+    def __init__(self, beta: float = 1.0 / 9.0, code_weights: list = None):
+        """
+        Args:
+            beta: Scalar float.
+                L1 to L2 change point.
+                For beta values < 1e-5, L1 loss is computed.
+            code_weights: (#codes) float list if not None.
+                Code-wise weights.
+        """
+        super(WeightedSmoothL1Loss, self).__init__()
+        self.beta = beta
+        if code_weights is not None:
+            self.code_weights = np.array(code_weights, dtype=np.float32)
+            self.code_weights = torch.from_numpy(self.code_weights).cuda()
+
+    @staticmethod
+    def smooth_l1_loss(diff, beta):
+        if beta < 1e-5:
+            loss = torch.abs(diff)
+        else:
+            n = torch.abs(diff)
+            loss = torch.where(n < beta, 0.5 * n ** 2 / beta, n - 0.5 * beta)
+
+        return loss
+
+    def forward(self, input: torch.Tensor,
+                target: torch.Tensor, weights: torch.Tensor = None):
+        """
+        Args:
+            input: (B, #anchors, #codes) float tensor.
+                Ecoded predicted locations of objects.
+            target: (B, #anchors, #codes) float tensor.
+                Regression targets.
+            weights: (B, #anchors) float tensor if not None.
+
+        Returns:
+            loss: (B, #anchors) float tensor.
+                Weighted smooth l1 loss without reduction.
+        """
+        target = torch.where(torch.isnan(target), input, target)  # ignore nan targets
+
+        diff = input - target
+        loss = self.smooth_l1_loss(diff, self.beta)
+
+        # anchor-wise weighting
+        if weights is not None:
+            assert weights.shape[0] == loss.shape[0] and weights.shape[1] == loss.shape[1]
+            loss = loss * weights.unsqueeze(-1)
+
+        return loss
+
+
+class PointPillarLoss(nn.Module):
+    def __init__(self, args):
+        super(PointPillarLoss, self).__init__()
+        self.reg_loss_func = WeightedSmoothL1Loss()
+        self.alpha = 0.25
+        self.gamma = 2.0
+
+        self.cls_weight = args['cls_weight']
+        self.reg_coe = args['reg']
+        self.loss_dict = {}
+
+    def forward(self, output_dict, target_dict):
+        """
+        Parameters
+        ----------
+        output_dict : dict
+        target_dict : dict
+        """
+        rm = output_dict['rm']
+        psm = output_dict['psm']
+        targets = target_dict['targets']
+
+        cls_preds = psm.permute(0, 2, 3, 1).contiguous()
+
+        box_cls_labels = target_dict['pos_equal_one']
+        box_cls_labels = box_cls_labels.view(psm.shape[0], -1).contiguous()
+
+        positives = box_cls_labels > 0
+        negatives = box_cls_labels == 0
+        negative_cls_weights = negatives * 1.0
+        cls_weights = (negative_cls_weights + 1.0 * positives).float()
+        reg_weights = positives.float()
+
+        pos_normalizer = positives.sum(1, keepdim=True).float()
+        reg_weights /= torch.clamp(pos_normalizer, min=1.0)
+        cls_weights /= torch.clamp(pos_normalizer, min=1.0)
+        cls_targets = box_cls_labels
+        cls_targets = cls_targets.unsqueeze(dim=-1)
+
+        cls_targets = cls_targets.squeeze(dim=-1)
+        one_hot_targets = torch.zeros(
+            *list(cls_targets.shape), 2,
+            dtype=cls_preds.dtype, device=cls_targets.device
+        )
+        one_hot_targets.scatter_(-1, cls_targets.unsqueeze(dim=-1).long(), 1.0)
+        cls_preds = cls_preds.view(psm.shape[0], -1, 1)
+        one_hot_targets = one_hot_targets[..., 1:]
+
+        cls_loss_src = self.cls_loss_func(cls_preds,
+                                          one_hot_targets,
+                                          weights=cls_weights)  # [N, M]
+        cls_loss = cls_loss_src.sum() / psm.shape[0]
+        conf_loss = cls_loss * self.cls_weight
+
+        # regression
+        rm = rm.permute(0, 2, 3, 1).contiguous()
+        rm = rm.view(rm.size(0), -1, 7)
+        targets = targets.view(targets.size(0), -1, 7)
+        box_preds_sin, reg_targets_sin = self.add_sin_difference(rm,
+                                                                 targets)
+        loc_loss_src =\
+            self.reg_loss_func(box_preds_sin,
+                               reg_targets_sin,
+                               weights=reg_weights)
+        reg_loss = loc_loss_src.sum() / rm.shape[0]
+        reg_loss *= self.reg_coe
+
+        total_loss = reg_loss + conf_loss
+
+        self.loss_dict.update({'total_loss': total_loss,
+                               'reg_loss': reg_loss,
+                               'conf_loss': conf_loss})
+
+        return total_loss
+
+    def cls_loss_func(self, input: torch.Tensor,
+                      target: torch.Tensor,
+                      weights: torch.Tensor):
+        """
+        Args:
+            input: (B, #anchors, #classes) float tensor.
+                Predicted logits for each class
+            target: (B, #anchors, #classes) float tensor.
+                One-hot encoded classification targets
+            weights: (B, #anchors) float tensor.
+                Anchor-wise weights.
+
+        Returns:
+            weighted_loss: (B, #anchors, #classes) float tensor after weighting.
+        """
+        pred_sigmoid = torch.sigmoid(input)
+        alpha_weight = target * self.alpha + (1 - target) * (1 - self.alpha)
+        pt = target * (1.0 - pred_sigmoid) + (1.0 - target) * pred_sigmoid
+        focal_weight = alpha_weight * torch.pow(pt, self.gamma)
+
+        bce_loss = self.sigmoid_cross_entropy_with_logits(input, target)
+
+        loss = focal_weight * bce_loss
+
+        if weights.shape.__len__() == 2 or \
+                (weights.shape.__len__() == 1 and target.shape.__len__() == 2):
+            weights = weights.unsqueeze(-1)
+
+        assert weights.shape.__len__() == loss.shape.__len__()
+
+        return loss * weights
+
+    @staticmethod
+    def sigmoid_cross_entropy_with_logits(input: torch.Tensor, target: torch.Tensor):
+        """ PyTorch Implementation for tf.nn.sigmoid_cross_entropy_with_logits:
+            max(x, 0) - x * z + log(1 + exp(-abs(x))) in
+            https://www.tensorflow.org/api_docs/python/tf/nn/sigmoid_cross_entropy_with_logits
+
+        Args:
+            input: (B, #anchors, #classes) float tensor.
+                Predicted logits for each class
+            target: (B, #anchors, #classes) float tensor.
+                One-hot encoded classification targets
+
+        Returns:
+            loss: (B, #anchors, #classes) float tensor.
+                Sigmoid cross entropy loss without reduction
+        """
+        loss = torch.clamp(input, min=0) - input * target + \
+               torch.log1p(torch.exp(-torch.abs(input)))
+        return loss
+
+    @staticmethod
+    def add_sin_difference(boxes1, boxes2, dim=6):
+        assert dim != -1
+        rad_pred_encoding = torch.sin(boxes1[..., dim:dim + 1]) * \
+                            torch.cos(boxes2[..., dim:dim + 1])
+        rad_tg_encoding = torch.cos(boxes1[..., dim:dim + 1]) * \
+                          torch.sin(boxes2[..., dim:dim + 1])
+
+        boxes1 = torch.cat([boxes1[..., :dim], rad_pred_encoding,
+                            boxes1[..., dim + 1:]], dim=-1)
+        boxes2 = torch.cat([boxes2[..., :dim], rad_tg_encoding,
+                            boxes2[..., dim + 1:]], dim=-1)
+        return boxes1, boxes2
+
+
+    def logging(self, epoch, batch_id, batch_len, writer, pbar=None):
+        """
+        Print out  the loss function for current iteration.
+
+        Parameters
+        ----------
+        epoch : int
+            Current epoch for training.
+        batch_id : int
+            The current batch.
+        batch_len : int
+            Total batch length in one iteration of training,
+        writer : SummaryWriter
+            Used to visualize on tensorboard
+        """
+        total_loss = self.loss_dict['total_loss']
+        reg_loss = self.loss_dict['reg_loss']
+        conf_loss = self.loss_dict['conf_loss']
+        if pbar is None:
+            print("[epoch %d][%d/%d], || Loss: %.4f || Conf Loss: %.4f"
+                " || Loc Loss: %.4f" % (
+                    epoch, batch_id + 1, batch_len,
+                    total_loss.item(), conf_loss.item(), reg_loss.item()))
+        else:
+            pbar.set_description("[epoch %d][%d/%d], || Loss: %.4f || Conf Loss: %.4f"
+                  " || Loc Loss: %.4f" % (
+                      epoch, batch_id + 1, batch_len,
+                      total_loss.item(), conf_loss.item(), reg_loss.item()))
+
+
+        writer.add_scalar('Regression_loss', reg_loss.item(),
+                          epoch*batch_len + batch_id)
+        writer.add_scalar('Confidence_loss', conf_loss.item(),
+                          epoch*batch_len + batch_id)
\ No newline at end of file
diff --git a/opencood/opencood/models/__init__.py b/opencood/opencood/models/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/opencood/opencood/models/ciassd.py b/opencood/opencood/models/ciassd.py
new file mode 100644
index 00000000..db64c468
--- /dev/null
+++ b/opencood/opencood/models/ciassd.py
@@ -0,0 +1,39 @@
+import torch
+from torch import nn
+import numpy as np
+
+from opencood.models.sub_modules.mean_vfe import MeanVFE
+from opencood.models.sub_modules.sparse_backbone_3d import VoxelBackBone8x
+from opencood.models.sub_modules.height_compression import HeightCompression
+from opencood.models.sub_modules.cia_ssd_utils import SSFA, Head
+
+
+class CIASSD(nn.Module):
+    def __init__(self, args):
+        super(CIASSD, self).__init__()
+        lidar_range = np.array(args['lidar_range'])
+        grid_size = np.round((lidar_range[3:6] - lidar_range[:3]) /
+                             np.array(args['voxel_size'])).astype(np.int64)
+        self.vfe = MeanVFE(args['mean_vfe'], args['mean_vfe']['num_point_features'])
+        self.spconv_block = VoxelBackBone8x(args['spconv'],
+                                            input_channels=args['spconv']['num_features_in'],
+                                            grid_size=grid_size)
+        self.map_to_bev = HeightCompression(args['map2bev'])
+        self.ssfa = SSFA(args['ssfa'])
+        self.head = Head(**args['head'])
+
+    def forward(self, batch_dict):
+        batch_dict['batch_size'] = batch_dict['object_bbx_center'].shape[0]
+        batch_dict = self.vfe(batch_dict)
+        batch_dict = self.spconv_block(batch_dict)
+        batch_dict = self.map_to_bev(batch_dict)
+        out = self.ssfa(batch_dict['processed_lidar']['spatial_features'])
+        out = self.head(out)
+        batch_dict['preds_dict_stage1'] = out
+
+        return batch_dict
+
+
+if __name__=="__main__":
+    model = SSFA(None)
+    print(model)
\ No newline at end of file
diff --git a/opencood/opencood/models/fpvrcnn.py b/opencood/opencood/models/fpvrcnn.py
new file mode 100644
index 00000000..fec8f0d2
--- /dev/null
+++ b/opencood/opencood/models/fpvrcnn.py
@@ -0,0 +1,81 @@
+import random, os
+
+import torch
+from torch import nn
+import numpy as np
+
+from opencood.models.sub_modules.mean_vfe import MeanVFE
+from opencood.models.sub_modules.sparse_backbone_3d import VoxelBackBone8x
+from opencood.models.sub_modules.height_compression import HeightCompression
+from opencood.models.sub_modules.cia_ssd_utils import SSFA, Head
+from opencood.models.sub_modules.vsa import VoxelSetAbstraction
+from opencood.models.sub_modules.roi_head import RoIHead
+from opencood.models.sub_modules.matcher import Matcher
+from opencood.data_utils.post_processor.fpvrcnn_postprocessor import \
+    FpvrcnnPostprocessor
+
+
+class FPVRCNN(nn.Module):
+    def __init__(self, args):
+        super(FPVRCNN, self).__init__()
+        lidar_range = np.array(args['lidar_range'])
+        grid_size = np.round((lidar_range[3:6] - lidar_range[:3]) /
+                             np.array(args['voxel_size'])).astype(np.int64)
+        self.vfe = MeanVFE(args['mean_vfe'],
+                           args['mean_vfe']['num_point_features'])
+        self.spconv_block = VoxelBackBone8x(args['spconv'],
+                                            input_channels=args['spconv'][
+                                                'num_features_in'],
+                                            grid_size=grid_size)
+        self.map_to_bev = HeightCompression(args['map2bev'])
+        self.ssfa = SSFA(args['ssfa'])
+        self.head = Head(**args['head'])
+        self.post_processor = FpvrcnnPostprocessor(args['post_processer'],
+                                                   train=True)
+        self.vsa = VoxelSetAbstraction(args['vsa'], args['voxel_size'],
+                                       args['lidar_range'],
+                                       num_bev_features=128,
+                                       num_rawpoint_features=3)
+        self.matcher = Matcher(args['matcher'], args['lidar_range'])
+        self.roi_head = RoIHead(args['roi_head'])
+        self.train_stage2 = args['activate_stage2']
+
+    def forward(self, batch_dict):
+        voxel_features = batch_dict['processed_lidar']['voxel_features']
+        voxel_coords = batch_dict['processed_lidar']['voxel_coords']
+        voxel_num_points = batch_dict['processed_lidar']['voxel_num_points']
+
+        # save memory
+        batch_dict.pop('processed_lidar')
+        batch_dict.update({'voxel_features': voxel_features,
+                           'voxel_coords': voxel_coords,
+                           'voxel_num_points': voxel_num_points,
+                           'batch_size': int(batch_dict['record_len'].sum())})
+
+        batch_dict = self.vfe(batch_dict)
+        batch_dict = self.spconv_block(batch_dict)
+        batch_dict = self.map_to_bev(batch_dict)
+
+        out = self.ssfa(batch_dict['spatial_features'])
+        batch_dict['preds_dict_stage1'] = self.head(out)
+
+        data_dict, output_dict = {}, {}
+        data_dict['ego'], output_dict['ego'] = batch_dict, batch_dict
+
+        pred_box3d_list, scores_list = \
+            self.post_processor.post_process(data_dict, output_dict,
+                                             stage1=True)
+        batch_dict['det_boxes'] = pred_box3d_list
+        batch_dict['det_scores'] = scores_list
+
+        if pred_box3d_list is not None and self.train_stage2:
+            batch_dict = self.vsa(batch_dict)
+            batch_dict = self.matcher(batch_dict)
+            batch_dict = self.roi_head(batch_dict)
+
+        return batch_dict
+
+
+if __name__ == "__main__":
+    model = SSFA(None)
+    print(model)
diff --git a/opencood/opencood/models/fuse_modules/__init__.py b/opencood/opencood/models/fuse_modules/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/opencood/opencood/models/fuse_modules/f_cooper_fuse.py b/opencood/opencood/models/fuse_modules/f_cooper_fuse.py
new file mode 100644
index 00000000..0527e769
--- /dev/null
+++ b/opencood/opencood/models/fuse_modules/f_cooper_fuse.py
@@ -0,0 +1,30 @@
+# -*- coding: utf-8 -*-
+# Author: Runsheng Xu <rxx3386@ucla.edu>
+# License: TDG-Attribution-NonCommercial-NoDistrib
+
+
+"""
+Implementation of F-cooper maxout fusing.
+"""
+import torch
+import torch.nn as nn
+
+
+class SpatialFusion(nn.Module):
+    def __init__(self):
+        super(SpatialFusion, self).__init__()
+
+    def regroup(self, x, record_len):
+        cum_sum_len = torch.cumsum(record_len, dim=0)
+        split_x = torch.tensor_split(x, cum_sum_len[:-1].cpu())
+        return split_x
+
+    def forward(self, x, record_len):
+        # x: B, C, H, W, split x:[(B1, C, W, H), (B2, C, W, H)]
+        split_x = self.regroup(x, record_len)
+        out = []
+
+        for xx in split_x:
+            xx = torch.max(xx, dim=0, keepdim=True)[0]
+            out.append(xx)
+        return torch.cat(out, dim=0)
\ No newline at end of file
diff --git a/opencood/opencood/models/fuse_modules/fuse_utils.py b/opencood/opencood/models/fuse_modules/fuse_utils.py
new file mode 100644
index 00000000..83fce7dd
--- /dev/null
+++ b/opencood/opencood/models/fuse_modules/fuse_utils.py
@@ -0,0 +1,66 @@
+# -*- coding: utf-8 -*-
+# Author: Runsheng Xu <rxx3386@ucla.edu>
+# License: TDG-Attribution-NonCommercial-NoDistrib
+
+
+import torch
+import numpy as np
+
+from einops import rearrange
+from opencood.utils.common_utils import torch_tensor_to_numpy
+
+
+def regroup(dense_feature, record_len, max_len):
+    """
+    Regroup the data based on the record_len.
+
+    Parameters
+    ----------
+    dense_feature : torch.Tensor
+        N, C, H, W
+    record_len : list
+        [sample1_len, sample2_len, ...]
+    max_len : int
+        Maximum cav number
+
+    Returns
+    -------
+    regroup_feature : torch.Tensor
+        B, L, C, H, W
+    """
+    cum_sum_len = list(np.cumsum(torch_tensor_to_numpy(record_len)))
+    split_features = torch.tensor_split(dense_feature,
+                                        cum_sum_len[:-1])
+    regroup_features = []
+    mask = []
+
+    for split_feature in split_features:
+        # M, C, H, W
+        feature_shape = split_feature.shape
+
+        # the maximum M is 5 as most 5 cavs
+        padding_len = max_len - feature_shape[0]
+        mask.append([1] * feature_shape[0] + [0] * padding_len)
+
+        padding_tensor = torch.zeros(padding_len, feature_shape[1],
+                                     feature_shape[2], feature_shape[3])
+        padding_tensor = padding_tensor.to(split_feature.device)
+
+        split_feature = torch.cat([split_feature, padding_tensor],
+                                  dim=0)
+
+        # 1, 5C, H, W
+        split_feature = split_feature.view(-1,
+                                           feature_shape[2],
+                                           feature_shape[3]).unsqueeze(0)
+        regroup_features.append(split_feature)
+
+    # B, 5C, H, W
+    regroup_features = torch.cat(regroup_features, dim=0)
+    # B, L, C, H, W
+    regroup_features = rearrange(regroup_features,
+                                 'b (l c) h w -> b l c h w',
+                                 l=max_len)
+    mask = torch.from_numpy(np.array(mask)).to(regroup_features.device)
+
+    return regroup_features, mask
diff --git a/opencood/opencood/models/fuse_modules/hmsa.py b/opencood/opencood/models/fuse_modules/hmsa.py
new file mode 100644
index 00000000..814b298c
--- /dev/null
+++ b/opencood/opencood/models/fuse_modules/hmsa.py
@@ -0,0 +1,151 @@
+import torch
+from torch import nn
+
+from einops import rearrange
+
+
+class HGTCavAttention(nn.Module):
+    def __init__(self, dim, heads, num_types=2,
+                 num_relations=4, dim_head=64, dropout=0.1):
+        super().__init__()
+        inner_dim = heads * dim_head
+
+        self.heads = heads
+        self.scale = dim_head ** -0.5
+        self.num_types = num_types
+
+        self.attend = nn.Softmax(dim=-1)
+        self.drop_out = nn.Dropout(dropout)
+        self.k_linears = nn.ModuleList()
+        self.q_linears = nn.ModuleList()
+        self.v_linears = nn.ModuleList()
+        self.a_linears = nn.ModuleList()
+        self.norms = nn.ModuleList()
+        for t in range(num_types):
+            self.k_linears.append(nn.Linear(dim, inner_dim))
+            self.q_linears.append(nn.Linear(dim, inner_dim))
+            self.v_linears.append(nn.Linear(dim, inner_dim))
+            self.a_linears.append(nn.Linear(inner_dim, dim))
+
+        self.relation_att = nn.Parameter(
+            torch.Tensor(num_relations, heads, dim_head, dim_head))
+        self.relation_msg = nn.Parameter(
+            torch.Tensor(num_relations, heads, dim_head, dim_head))
+
+        torch.nn.init.xavier_uniform(self.relation_att)
+        torch.nn.init.xavier_uniform(self.relation_msg)
+
+    def to_qkv(self, x, types):
+        # x: (B,H,W,L,C)
+        # types: (B,L)
+        q_batch = []
+        k_batch = []
+        v_batch = []
+
+        for b in range(x.shape[0]):
+            q_list = []
+            k_list = []
+            v_list = []
+
+            for i in range(x.shape[-2]):
+                # (H,W,1,C)
+                q_list.append(
+                    self.q_linears[types[b, i]](x[b, :, :, i, :].unsqueeze(2)))
+                k_list.append(
+                    self.k_linears[types[b, i]](x[b, :, :, i, :].unsqueeze(2)))
+                v_list.append(
+                    self.v_linears[types[b, i]](x[b, :, :, i, :].unsqueeze(2)))
+            # (1,H,W,L,C)
+            q_batch.append(torch.cat(q_list, dim=2).unsqueeze(0))
+            k_batch.append(torch.cat(k_list, dim=2).unsqueeze(0))
+            v_batch.append(torch.cat(v_list, dim=2).unsqueeze(0))
+        # (B,H,W,L,C)
+        q = torch.cat(q_batch, dim=0)
+        k = torch.cat(k_batch, dim=0)
+        v = torch.cat(v_batch, dim=0)
+        return q, k, v
+
+    def get_relation_type_index(self, type1, type2):
+        return type1 * self.num_types + type2
+
+    def get_hetero_edge_weights(self, x, types):
+        w_att_batch = []
+        w_msg_batch = []
+
+        for b in range(x.shape[0]):
+            w_att_list = []
+            w_msg_list = []
+
+            for i in range(x.shape[-2]):
+                w_att_i_list = []
+                w_msg_i_list = []
+
+                for j in range(x.shape[-2]):
+                    e_type = self.get_relation_type_index(types[b, i],
+                                                          types[b, j])
+                    w_att_i_list.append(self.relation_att[e_type].unsqueeze(0))
+                    w_msg_i_list.append(self.relation_msg[e_type].unsqueeze(0))
+                w_att_list.append(torch.cat(w_att_i_list, dim=0).unsqueeze(0))
+                w_msg_list.append(torch.cat(w_msg_i_list, dim=0).unsqueeze(0))
+
+            w_att_batch.append(torch.cat(w_att_list, dim=0).unsqueeze(0))
+            w_msg_batch.append(torch.cat(w_msg_list, dim=0).unsqueeze(0))
+
+        # (B,M,L,L,C_head,C_head)
+        w_att = torch.cat(w_att_batch, dim=0).permute(0, 3, 1, 2, 4, 5)
+        w_msg = torch.cat(w_msg_batch, dim=0).permute(0, 3, 1, 2, 4, 5)
+        return w_att, w_msg
+
+    def to_out(self, x, types):
+        out_batch = []
+        for b in range(x.shape[0]):
+            out_list = []
+            for i in range(x.shape[-2]):
+                out_list.append(
+                    self.a_linears[types[b, i]](x[b, :, :, i, :].unsqueeze(2)))
+            out_batch.append(torch.cat(out_list, dim=2).unsqueeze(0))
+        out = torch.cat(out_batch, dim=0)
+        return out
+
+    def forward(self, x, mask, prior_encoding):
+        # x: (B, L, H, W, C) -> (B, H, W, L, C)
+        # mask: (B, H, W, L, 1)
+        # prior_encoding: (B,L,H,W,3)
+        x = x.permute(0, 2, 3, 1, 4)
+        # mask: (B, 1, H, W, L, 1)
+        mask = mask.unsqueeze(1)
+        # (B,L)
+        velocities, dts, types = [itm.squeeze(-1) for itm in
+                                  prior_encoding[:, :, 0, 0, :].split(
+                                      [1, 1, 1], dim=-1)]
+        types = types.to(torch.int)
+        dts = dts.to(torch.int)
+        qkv = self.to_qkv(x, types)
+        # (B,M,L,L,C_head,C_head)
+        w_att, w_msg = self.get_hetero_edge_weights(x, types)
+
+        # q: (B, M, H, W, L, C)
+        q, k, v = map(lambda t: rearrange(t, 'b h w l (m c) -> b m h w l c',
+                                          m=self.heads), (qkv))
+        # attention, (B, M, H, W, L, L)
+        att_map = torch.einsum(
+            'b m h w i p, b m i j p q, bm h w j q -> b m h w i j',
+            [q, w_att, k]) * self.scale
+        # add mask
+        att_map = att_map.masked_fill(mask == 0, -float('inf'))
+        # softmax
+        att_map = self.attend(att_map)
+
+        # out:(B, M, H, W, L, C_head)
+        v_msg = torch.einsum('b m i j p c, b m h w j p -> b m h w i j c',
+                             w_msg, v)
+        out = torch.einsum('b m h w i j, b m h w i j c -> b m h w i c',
+                           att_map, v_msg)
+
+        out = rearrange(out, 'b m h w l c -> b h w l (m c)',
+                        m=self.heads)
+        out = self.to_out(out, types)
+        out = self.drop_out(out)
+        # (B L H W C)
+        out = out.permute(0, 3, 1, 2, 4)
+        return out
\ No newline at end of file
diff --git a/opencood/opencood/models/fuse_modules/mswin.py b/opencood/opencood/models/fuse_modules/mswin.py
new file mode 100644
index 00000000..c6fc22e3
--- /dev/null
+++ b/opencood/opencood/models/fuse_modules/mswin.py
@@ -0,0 +1,119 @@
+"""
+Multi-scale window transformer
+"""
+import torch
+import torch.nn as nn
+import numpy as np
+
+from einops import rearrange
+from opencood.models.sub_modules.split_attn import SplitAttn
+
+
+def get_relative_distances(window_size):
+    indices = torch.tensor(np.array(
+        [[x, y] for x in range(window_size) for y in range(window_size)]))
+    distances = indices[None, :, :] - indices[:, None, :]
+    return distances
+
+
+class BaseWindowAttention(nn.Module):
+    def __init__(self, dim, heads, dim_head, drop_out, window_size,
+                 relative_pos_embedding):
+        super().__init__()
+        inner_dim = dim_head * heads
+
+        self.heads = heads
+        self.scale = dim_head ** -0.5
+        self.window_size = window_size
+        self.relative_pos_embedding = relative_pos_embedding
+
+        self.to_qkv = nn.Linear(dim, inner_dim * 3, bias=False)
+
+        if self.relative_pos_embedding:
+            self.relative_indices = get_relative_distances(window_size) + \
+                                    window_size - 1
+            self.pos_embedding = nn.Parameter(torch.randn(2 * window_size - 1,
+                                                          2 * window_size - 1))
+        else:
+            self.pos_embedding = nn.Parameter(torch.randn(window_size ** 2,
+                                                          window_size ** 2))
+
+        self.to_out = nn.Sequential(
+            nn.Linear(inner_dim, dim),
+            nn.Dropout(drop_out)
+        )
+
+    def forward(self, x):
+        b, l, h, w, c, m = *x.shape, self.heads
+
+        qkv = self.to_qkv(x).chunk(3, dim=-1)
+        new_h = h // self.window_size
+        new_w = w // self.window_size
+
+        # q : (b, l, m, new_h*new_w, window_size^2, c_head)
+        q, k, v = map(
+            lambda t: rearrange(t,
+                                'b l (new_h w_h) (new_w w_w) (m c) -> b l m (new_h new_w) (w_h w_w) c',
+                                m=m, w_h=self.window_size,
+                                w_w=self.window_size), qkv)
+        # b l m h window_size window_size
+        dots = torch.einsum('b l m h i c, b l m h j c -> b l m h i j',
+                            q, k, ) * self.scale
+        # consider prior knowledge of the local window
+        if self.relative_pos_embedding:
+            dots += self.pos_embedding[self.relative_indices[:, :, 0],
+                                       self.relative_indices[:, :, 1]]
+        else:
+            dots += self.pos_embedding
+
+        attn = dots.softmax(dim=-1)
+
+        out = torch.einsum('b l m h i j, b l m h j c -> b l m h i c', attn, v)
+        # b l h w c
+        out = rearrange(out,
+                        'b l m (new_h new_w) (w_h w_w) c -> b l (new_h w_h) (new_w w_w) (m c)',
+                        m=self.heads, w_h=self.window_size,
+                        w_w=self.window_size,
+                        new_w=new_w, new_h=new_h)
+        out = self.to_out(out)
+
+        return out
+
+
+class PyramidWindowAttention(nn.Module):
+    def __init__(self, dim, heads, dim_heads, drop_out, window_size,
+                 relative_pos_embedding, fuse_method='naive'):
+        super().__init__()
+
+        assert isinstance(window_size, list)
+        assert isinstance(heads, list)
+        assert isinstance(dim_heads, list)
+        assert len(dim_heads) == len(heads)
+
+        self.pwmsa = nn.ModuleList([])
+
+        for (head, dim_head, ws) in zip(heads, dim_heads, window_size):
+            self.pwmsa.append(BaseWindowAttention(dim,
+                                                  head,
+                                                  dim_head,
+                                                  drop_out,
+                                                  ws,
+                                                  relative_pos_embedding))
+        self.fuse_mehod = fuse_method
+        if fuse_method == 'split_attn':
+            self.split_attn = SplitAttn(256)
+
+    def forward(self, x):
+        output = None
+        # naive fusion will just sum up all window attention output and do a
+        # mean
+        if self.fuse_mehod == 'naive':
+            for wmsa in self.pwmsa:
+                output = wmsa(x) if output is None else output + wmsa(x)
+            return output / len(self.pwmsa)
+
+        elif self.fuse_mehod == 'split_attn':
+            window_list = []
+            for wmsa in self.pwmsa:
+                window_list.append(wmsa(x))
+            return self.split_attn(window_list)
\ No newline at end of file
diff --git a/opencood/opencood/models/fuse_modules/self_attn.py b/opencood/opencood/models/fuse_modules/self_attn.py
new file mode 100644
index 00000000..b2278d55
--- /dev/null
+++ b/opencood/opencood/models/fuse_modules/self_attn.py
@@ -0,0 +1,66 @@
+# -*- coding: utf-8 -*-
+# Author: Hao Xiang <haxiang@g.ucla.edu>, Runsheng Xu <rxx3386@ucla.edu>
+# License: TDG-Attribution-NonCommercial-NoDistrib
+
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class ScaledDotProductAttention(nn.Module):
+    """
+    Scaled Dot-Product Attention proposed in "Attention Is All You Need"
+    Compute the dot products of the query with all keys, divide each by sqrt(dim),
+    and apply a softmax function to obtain the weights on the values
+    Args: dim, mask
+        dim (int): dimention of attention
+        mask (torch.Tensor): tensor containing indices to be masked
+    Inputs: query, key, value, mask
+        - **query** (batch, q_len, d_model): tensor containing projection
+          vector for decoder.
+        - **key** (batch, k_len, d_model): tensor containing projection
+          vector for encoder.
+        - **value** (batch, v_len, d_model): tensor containing features of the
+          encoded input sequence.
+        - **mask** (-): tensor containing indices to be masked
+    Returns: context, attn
+        - **context**: tensor containing the context vector from
+          attention mechanism.
+        - **attn**: tensor containing the attention (alignment) from the
+          encoder outputs.
+    """
+
+    def __init__(self, dim):
+        super(ScaledDotProductAttention, self).__init__()
+        self.sqrt_dim = np.sqrt(dim)
+
+    def forward(self, query, key, value):
+        score = torch.bmm(query, key.transpose(1, 2)) / self.sqrt_dim
+        attn = F.softmax(score, -1)
+        context = torch.bmm(attn, value)
+        return context
+
+
+class AttFusion(nn.Module):
+    def __init__(self, feature_dim):
+        super(AttFusion, self).__init__()
+        self.att = ScaledDotProductAttention(feature_dim)
+
+    def forward(self, x, record_len):
+        split_x = self.regroup(x, record_len)
+        C, W, H = split_x[0].shape[1:]
+        out = []
+        for xx in split_x:
+            cav_num = xx.shape[0]
+            xx = xx.view(cav_num, C, -1).permute(2, 0, 1)
+            h = self.att(xx, xx, xx)
+            h = h.permute(1, 2, 0).view(cav_num, C, W, H)[0, ...]
+            out.append(h)
+        return torch.stack(out)
+
+    def regroup(self, x, record_len):
+        cum_sum_len = torch.cumsum(record_len, dim=0)
+        split_x = torch.tensor_split(x, cum_sum_len[:-1].cpu())
+        return split_x
diff --git a/opencood/opencood/models/fuse_modules/swap_fusion_modules.py b/opencood/opencood/models/fuse_modules/swap_fusion_modules.py
new file mode 100644
index 00000000..6a154c92
--- /dev/null
+++ b/opencood/opencood/models/fuse_modules/swap_fusion_modules.py
@@ -0,0 +1,311 @@
+"""
+This class is about swap fusion applications (also known as Fused Axial Attention)
+"""
+import torch
+from einops import rearrange
+from torch import nn, einsum
+from einops.layers.torch import Rearrange, Reduce
+
+from opencood.models.sub_modules.base_transformer import \
+    FeedForward, PreNormResidual
+
+
+# swap attention -> max_vit
+class Attention(nn.Module):
+    """
+    Unit Attention class. Todo: mask is not added yet.
+
+    Parameters
+    ----------
+    dim: int
+        Input feature dimension.
+    dim_head: int
+        The head dimension.
+    dropout: float
+        Dropout rate
+    agent_size: int
+        The agent can be different views, timestamps or vehicles.
+    """
+
+    def __init__(
+            self,
+            dim,
+            dim_head=32,
+            dropout=0.,
+            agent_size=6,
+            window_size=7
+    ):
+        super().__init__()
+        assert (dim % dim_head) == 0, \
+            'dimension should be divisible by dimension per head'
+
+        self.heads = dim // dim_head
+        self.scale = dim_head ** -0.5
+        self.window_size = [agent_size, window_size, window_size]
+
+        self.to_qkv = nn.Linear(dim, dim * 3, bias=False)
+        self.attend = nn.Sequential(
+            nn.Softmax(dim=-1)
+        )
+
+        self.to_out = nn.Sequential(
+            nn.Linear(dim, dim, bias=False),
+            nn.Dropout(dropout)
+        )
+
+        self.relative_position_bias_table = nn.Embedding(
+            (2 * self.window_size[0] - 1) *
+            (2 * self.window_size[1] - 1) *
+            (2 * self.window_size[2] - 1),
+            self.heads)  # 2*Wd-1 * 2*Wh-1 * 2*Ww-1, nH
+
+        # get pair-wise relative position index for
+        # each token inside the window
+        coords_d = torch.arange(self.window_size[0])
+        coords_h = torch.arange(self.window_size[1])
+        coords_w = torch.arange(self.window_size[2])
+        # 3, Wd, Wh, Ww
+        coords = torch.stack(torch.meshgrid(coords_d, coords_h, coords_w))
+        coords_flatten = torch.flatten(coords, 1)  # 3, Wd*Wh*Ww
+
+        # 3, Wd*Wh*Ww, Wd*Wh*Ww
+        relative_coords = \
+            coords_flatten[:, :, None] - coords_flatten[:, None, :]
+        # Wd*Wh*Ww, Wd*Wh*Ww, 3
+        relative_coords = relative_coords.permute(1, 2, 0).contiguous()
+        # shift to start from 0
+        relative_coords[:, :, 0] += self.window_size[0] - 1
+        relative_coords[:, :, 1] += self.window_size[1] - 1
+        relative_coords[:, :, 2] += self.window_size[2] - 1
+
+        relative_coords[:, :, 0] *= \
+            (2 * self.window_size[1] - 1) * (2 * self.window_size[2] - 1)
+        relative_coords[:, :, 1] *= (2 * self.window_size[2] - 1)
+        relative_position_index = relative_coords.sum(-1)  # Wd*Wh*Ww, Wd*Wh*Ww
+        self.register_buffer("relative_position_index",
+                             relative_position_index)
+
+    def forward(self, x, mask=None):
+        # x shape: b, l, h, w, w_h, w_w, c
+        batch, agent_size, height, width, window_height, window_width, _, device, h \
+            = *x.shape, x.device, self.heads
+
+        # flatten
+        x = rearrange(x, 'b l x y w1 w2 d -> (b x y) (l w1 w2) d')
+        # project for queries, keys, values
+        q, k, v = self.to_qkv(x).chunk(3, dim=-1)
+        # split heads
+        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h=h),
+                      (q, k, v))
+        # scale
+        q = q * self.scale
+
+        # sim
+        sim = einsum('b h i d, b h j d -> b h i j', q, k)
+
+        # add positional bias
+        bias = self.relative_position_bias_table(self.relative_position_index)
+        sim = sim + rearrange(bias, 'i j h -> h i j')
+
+        # mask shape if exist: b x y w1 w2 e l
+        if mask is not None:
+            # b x y w1 w2 e l -> (b x y) 1 (l w1 w2)
+            mask = rearrange(mask, 'b x y w1 w2 e l -> (b x y) e (l w1 w2)')
+            # (b x y) 1 1 (l w1 w2) = b h 1 n
+            mask = mask.unsqueeze(1)
+            sim = sim.masked_fill(mask == 0, -float('inf'))
+
+        # attention
+        attn = self.attend(sim)
+        # aggregate
+        out = einsum('b h i j, b h j d -> b h i d', attn, v)
+        # merge heads
+        out = rearrange(out, 'b h (l w1 w2) d -> b l w1 w2 (h d)',
+                        l=agent_size, w1=window_height, w2=window_width)
+
+        # combine heads out
+        out = self.to_out(out)
+        return rearrange(out, '(b x y) l w1 w2 d -> b l x y w1 w2 d',
+                         b=batch, x=height, y=width)
+
+
+class SwapFusionBlockMask(nn.Module):
+    """
+    Swap Fusion Block contains window attention and grid attention with
+    mask enabled for multi-vehicle cooperation.
+    """
+
+    def __init__(self,
+                 input_dim,
+                 mlp_dim,
+                 dim_head,
+                 window_size,
+                 agent_size,
+                 drop_out):
+        super(SwapFusionBlockMask, self).__init__()
+
+        self.window_size = window_size
+
+        self.window_attention = PreNormResidual(input_dim,
+                                                Attention(input_dim, dim_head,
+                                                          drop_out,
+                                                          agent_size,
+                                                          window_size))
+        self.window_ffd = PreNormResidual(input_dim,
+                                          FeedForward(input_dim, mlp_dim,
+                                                      drop_out))
+        self.grid_attention = PreNormResidual(input_dim,
+                                              Attention(input_dim, dim_head,
+                                                        drop_out,
+                                                        agent_size,
+                                                        window_size))
+        self.grid_ffd = PreNormResidual(input_dim,
+                                        FeedForward(input_dim, mlp_dim,
+                                                    drop_out))
+
+    def forward(self, x, mask):
+        # x: b l c h w
+        # mask: b h w 1 l
+        # window attention -> grid attention
+        mask_swap = mask
+
+        # mask b h w 1 l -> b x y w1 w2 1 L
+        mask_swap = rearrange(mask_swap,
+                              'b (x w1) (y w2) e l -> b x y w1 w2 e l',
+                              w1=self.window_size, w2=self.window_size)
+        x = rearrange(x, 'b m d (x w1) (y w2) -> b m x y w1 w2 d',
+                      w1=self.window_size, w2=self.window_size)
+        x = self.window_attention(x, mask=mask_swap)
+        x = self.window_ffd(x)
+        x = rearrange(x, 'b m x y w1 w2 d -> b m d (x w1) (y w2)')
+
+        # grid attention
+        mask_swap = mask
+        mask_swap = rearrange(mask_swap,
+                              'b (w1 x) (w2 y) e l -> b x y w1 w2 e l',
+                              w1=self.window_size, w2=self.window_size)
+        x = rearrange(x, 'b m d (w1 x) (w2 y) -> b m x y w1 w2 d',
+                      w1=self.window_size, w2=self.window_size)
+        x = self.grid_attention(x, mask=mask_swap)
+        x = self.grid_ffd(x)
+        x = rearrange(x, 'b m x y w1 w2 d -> b m d (w1 x) (w2 y)')
+
+        return x
+
+
+class SwapFusionBlock(nn.Module):
+    """
+    Swap Fusion Block contains window attention and grid attention.
+    """
+
+    def __init__(self,
+                 input_dim,
+                 mlp_dim,
+                 dim_head,
+                 window_size,
+                 agent_size,
+                 drop_out):
+        super(SwapFusionBlock, self).__init__()
+        # b = batch * max_cav
+        self.block = nn.Sequential(
+            Rearrange('b m d (x w1) (y w2) -> b m x y w1 w2 d',
+                      w1=window_size, w2=window_size),
+            PreNormResidual(input_dim, Attention(input_dim, dim_head, drop_out,
+                                                 agent_size, window_size)),
+            PreNormResidual(input_dim,
+                            FeedForward(input_dim, mlp_dim, drop_out)),
+            Rearrange('b m x y w1 w2 d -> b m d (x w1) (y w2)'),
+
+            Rearrange('b m d (w1 x) (w2 y) -> b m x y w1 w2 d',
+                      w1=window_size, w2=window_size),
+            PreNormResidual(input_dim, Attention(input_dim, dim_head, drop_out,
+                                                 agent_size, window_size)),
+            PreNormResidual(input_dim,
+                            FeedForward(input_dim, mlp_dim, drop_out)),
+            Rearrange('b m x y w1 w2 d -> b m d (w1 x) (w2 y)'),
+        )
+
+    def forward(self, x, mask=None):
+        # todo: add mask operation later for mulit-agents
+        x = self.block(x)
+        return x
+
+
+class SwapFusionEncoder(nn.Module):
+    """
+    Data rearrange -> swap block -> mlp_head
+    """
+
+    def __init__(self, args):
+        super(SwapFusionEncoder, self).__init__()
+
+        self.layers = nn.ModuleList([])
+        self.depth = args['depth']
+
+        # block related
+        input_dim = args['input_dim']
+        mlp_dim = args['mlp_dim']
+        agent_size = args['agent_size']
+        window_size = args['window_size']
+        drop_out = args['drop_out']
+        dim_head = args['dim_head']
+
+        self.mask = False
+        if 'mask' in args:
+            self.mask = args['mask']
+
+        for i in range(self.depth):
+            if self.mask:
+                block = SwapFusionBlockMask(input_dim,
+                                    mlp_dim,
+                                    dim_head,
+                                    window_size,
+                                    agent_size,
+                                    drop_out)
+
+            else:
+                block = SwapFusionBlock(input_dim,
+                                        mlp_dim,
+                                        dim_head,
+                                        window_size,
+                                        agent_size,
+                                        drop_out)
+            self.layers.append(block)
+
+        # mlp head
+        self.mlp_head = nn.Sequential(
+            Reduce('b m d h w -> b d h w', 'mean'),
+            Rearrange('b d h w -> b h w d'),
+            nn.LayerNorm(input_dim),
+            nn.Linear(input_dim, input_dim),
+            Rearrange('b h w d -> b d h w')
+        )
+
+    def forward(self, x, mask=None):
+        for stage in self.layers:
+            x = stage(x, mask=mask)
+        return self.mlp_head(x)
+
+
+if __name__ == "__main__":
+    import os
+
+    os.environ['CUDA_VISIBLE_DEVICES'] = '1'
+    args = {'input_dim': 512,
+            'mlp_dim': 512,
+            'agent_size': 4,
+            'window_size': 8,
+            'dim_head': 4,
+            'drop_out': 0.1,
+            'depth': 2,
+            'mask': True
+            }
+    block = SwapFusionEncoder(args)
+    block.cuda()
+    test_data = torch.rand(1, 4, 512, 32, 32)
+    test_data = test_data.cuda()
+    mask = torch.ones(1, 32, 32, 1, 4)
+    mask = mask.cuda()
+
+    output = block(test_data, mask)
+    print(output)
diff --git a/opencood/opencood/models/fuse_modules/v2v_fuse.py b/opencood/opencood/models/fuse_modules/v2v_fuse.py
new file mode 100644
index 00000000..ad1dafa7
--- /dev/null
+++ b/opencood/opencood/models/fuse_modules/v2v_fuse.py
@@ -0,0 +1,150 @@
+# -*- coding: utf-8 -*-
+# Author: Hao Xiang <haxiang@g.ucla.edu>
+# License: TDG-Attribution-NonCommercial-NoDistrib
+
+
+"""
+Implementation of V2VNet Fusion
+"""
+
+import torch
+import torch.nn as nn
+
+from opencood.models.sub_modules.torch_transformation_utils import \
+    get_discretized_transformation_matrix, get_transformation_matrix, \
+    warp_affine, get_rotated_roi
+from opencood.models.sub_modules.convgru import ConvGRU
+
+
+class V2VNetFusion(nn.Module):
+    def __init__(self, args):
+        super(V2VNetFusion, self).__init__()
+        
+        in_channels = args['in_channels']
+        H, W = args['conv_gru']['H'], args['conv_gru']['W']
+        kernel_size = args['conv_gru']['kernel_size']
+        num_gru_layers = args['conv_gru']['num_layers']
+
+        self.discrete_ratio = args['voxel_size'][0]
+        self.downsample_rate = args['downsample_rate']
+        self.num_iteration = args['num_iteration']
+        self.gru_flag = args['gru_flag']
+        self.agg_operator = args['agg_operator']
+
+        self.msg_cnn = nn.Conv2d(in_channels * 2, in_channels, kernel_size=3,
+                                 stride=1, padding=1)
+        self.conv_gru = ConvGRU(input_size=(H, W),
+                                input_dim=in_channels * 2,
+                                hidden_dim=[in_channels],
+                                kernel_size=kernel_size,
+                                num_layers=num_gru_layers,
+                                batch_first=True,
+                                bias=True,
+                                return_all_layers=False)
+        self.mlp = nn.Linear(in_channels, in_channels)
+
+    def regroup(self, x, record_len):
+        cum_sum_len = torch.cumsum(record_len, dim=0)
+        split_x = torch.tensor_split(x, cum_sum_len[:-1].cpu())
+        return split_x
+
+    def forward(self, x, record_len, pairwise_t_matrix):
+        """
+        Fusion forwarding.
+        
+        Parameters
+        ----------
+        x : torch.Tensor
+            input data, (B, C, H, W)
+            
+        record_len : list
+            shape: (B)
+            
+        pairwise_t_matrix : torch.Tensor
+            The transformation matrix from each cav to ego, 
+            shape: (B, L, L, 4, 4) 
+            
+        Returns
+        -------
+        Fused feature.
+        """
+        _, C, H, W = x.shape
+        B, L = pairwise_t_matrix.shape[:2]
+
+        # split x:[(L1, C, H, W), (L2, C, H, W)]
+        split_x = self.regroup(x, record_len)
+        # (B,L,L,2,3)
+        pairwise_t_matrix = get_discretized_transformation_matrix(
+            pairwise_t_matrix.reshape(-1, L, 4, 4), self.discrete_ratio,
+            self.downsample_rate).reshape(B, L, L, 2, 3)
+        # (B*L,L,1,H,W)
+        roi_mask = get_rotated_roi((B * L, L, 1, H, W),
+                                   pairwise_t_matrix.reshape(B * L * L, 2, 3))
+        roi_mask = roi_mask.reshape(B, L, L, 1, H, W)
+        batch_node_features = split_x
+        
+        # iteratively update the features for num_iteration times
+        for l in range(self.num_iteration):
+
+            batch_updated_node_features = []
+            # iterate each batch
+            for b in range(B):
+
+                # number of valid agent
+                N = record_len[b]
+                # (N,N,4,4)
+                # t_matrix[i, j]-> from i to j
+                t_matrix = pairwise_t_matrix[b][:N, :N, :, :]
+                updated_node_features = []
+                # update each node i
+                for i in range(N):
+                    # (N,1,H,W)
+                    mask = roi_mask[b, :N, i, ...]
+
+                    current_t_matrix = t_matrix[:, i, :, :]
+                    current_t_matrix = get_transformation_matrix(
+                        current_t_matrix, (H, W))
+
+                    # (N,C,H,W)
+                    neighbor_feature = warp_affine(batch_node_features[b],
+                                                   current_t_matrix,
+                                                   (H, W))
+                    # (N,C,H,W)
+                    ego_agent_feature = batch_node_features[b][i].unsqueeze(
+                        0).repeat(N, 1, 1, 1)
+                    #(N,2C,H,W)
+                    neighbor_feature = torch.cat(
+                        [neighbor_feature, ego_agent_feature], dim=1)
+                    # (N,C,H,W)
+                    message = self.msg_cnn(neighbor_feature) * mask
+
+                    # (C,H,W)
+                    if self.agg_operator=="avg":
+                        agg_feature = torch.mean(message, dim=0)
+                    elif self.agg_operator=="max":
+                        agg_feature = torch.max(message, dim=0)[0]
+                    else:
+                        raise ValueError("agg_operator has wrong value")
+                    # (2C, H, W)
+                    cat_feature = torch.cat(
+                        [batch_node_features[b][i, ...], agg_feature], dim=0)
+                    # (C,H,W)
+                    if self.gru_flag:
+                        gru_out = \
+                            self.conv_gru(cat_feature.unsqueeze(0).unsqueeze(0))[
+                                0][
+                                0].squeeze(0).squeeze(0)
+                    else:
+                        gru_out = batch_node_features[b][i, ...] + agg_feature
+                    updated_node_features.append(gru_out.unsqueeze(0))
+                # (N,C,H,W)
+                batch_updated_node_features.append(
+                    torch.cat(updated_node_features, dim=0))
+            batch_node_features = batch_updated_node_features
+        # (B,C,H,W)
+        out = torch.cat(
+            [itm[0, ...].unsqueeze(0) for itm in batch_node_features], dim=0)
+        # (B,C,H,W)
+        out = self.mlp(out.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
+
+        return out
diff --git a/opencood/opencood/models/fuse_modules/v2xvit_basic.py b/opencood/opencood/models/fuse_modules/v2xvit_basic.py
new file mode 100644
index 00000000..ab3d1362
--- /dev/null
+++ b/opencood/opencood/models/fuse_modules/v2xvit_basic.py
@@ -0,0 +1,191 @@
+import math
+
+from opencood.models.sub_modules.base_transformer import *
+from opencood.models.fuse_modules.hmsa import *
+from opencood.models.fuse_modules.mswin import *
+from opencood.models.sub_modules.torch_transformation_utils import \
+    get_transformation_matrix, warp_affine, get_roi_and_cav_mask, \
+    get_discretized_transformation_matrix
+
+
+class STTF(nn.Module):
+    def __init__(self, args):
+        super(STTF, self).__init__()
+        self.discrete_ratio = args['voxel_size'][0]
+        self.downsample_rate = args['downsample_rate']
+
+    def forward(self, x, mask, spatial_correction_matrix):
+        x = x.permute(0, 1, 4, 2, 3)
+        dist_correction_matrix = get_discretized_transformation_matrix(
+            spatial_correction_matrix, self.discrete_ratio,
+            self.downsample_rate)
+        # Only compensate non-ego vehicles
+        B, L, C, H, W = x.shape
+
+        T = get_transformation_matrix(
+            dist_correction_matrix[:, 1:, :, :].reshape(-1, 2, 3), (H, W))
+        cav_features = warp_affine(x[:, 1:, :, :, :].reshape(-1, C, H, W), T,
+                                   (H, W))
+        cav_features = cav_features.reshape(B, -1, C, H, W)
+        x = torch.cat([x[:, 0, :, :, :].unsqueeze(1), cav_features], dim=1)
+        x = x.permute(0, 1, 3, 4, 2)
+        return x
+
+
+class RelTemporalEncoding(nn.Module):
+    """
+    Implement the Temporal Encoding (Sinusoid) function.
+    """
+
+    def __init__(self, n_hid, RTE_ratio, max_len=100, dropout=0.2):
+        super(RelTemporalEncoding, self).__init__()
+        position = torch.arange(0., max_len).unsqueeze(1)
+        div_term = torch.exp(torch.arange(0, n_hid, 2) *
+                             -(math.log(10000.0) / n_hid))
+        emb = nn.Embedding(max_len, n_hid)
+        emb.weight.data[:, 0::2] = torch.sin(position * div_term) / math.sqrt(
+            n_hid)
+        emb.weight.data[:, 1::2] = torch.cos(position * div_term) / math.sqrt(
+            n_hid)
+        emb.requires_grad = False
+        self.RTE_ratio = RTE_ratio
+        self.emb = emb
+        self.lin = nn.Linear(n_hid, n_hid)
+
+    def forward(self, x, t):
+        # When t has unit of 50ms, rte_ratio=1.
+        # So we can train on 100ms but test on 50ms
+        return x + self.lin(self.emb(t * self.RTE_ratio)).unsqueeze(
+            0).unsqueeze(1)
+
+
+class RTE(nn.Module):
+    def __init__(self, dim, RTE_ratio=2):
+        super(RTE, self).__init__()
+        self.RTE_ratio = RTE_ratio
+
+        self.emb = RelTemporalEncoding(dim, RTE_ratio=self.RTE_ratio)
+
+    def forward(self, x, dts):
+        # x: (B,L,H,W,C)
+        # dts: (B,L)
+        rte_batch = []
+        for b in range(x.shape[0]):
+            rte_list = []
+            for i in range(x.shape[1]):
+                rte_list.append(
+                    self.emb(x[b, i, :, :, :], dts[b, i]).unsqueeze(0))
+            rte_batch.append(torch.cat(rte_list, dim=0).unsqueeze(0))
+        return torch.cat(rte_batch, dim=0)
+
+
+class V2XFusionBlock(nn.Module):
+    def __init__(self, num_blocks, cav_att_config, pwindow_config):
+        super().__init__()
+        # first multi-agent attention and then multi-window attention
+        self.layers = nn.ModuleList([])
+        self.num_blocks = num_blocks
+
+        for _ in range(num_blocks):
+            att = HGTCavAttention(cav_att_config['dim'],
+                                  heads=cav_att_config['heads'],
+                                  dim_head=cav_att_config['dim_head'],
+                                  dropout=cav_att_config['dropout']) if \
+                cav_att_config['use_hetero'] else \
+                CavAttention(cav_att_config['dim'],
+                             heads=cav_att_config['heads'],
+                             dim_head=cav_att_config['dim_head'],
+                             dropout=cav_att_config['dropout'])
+            self.layers.append(nn.ModuleList([
+                PreNorm(cav_att_config['dim'], att),
+                PreNorm(cav_att_config['dim'],
+                        PyramidWindowAttention(pwindow_config['dim'],
+                                               heads=pwindow_config['heads'],
+                                               dim_heads=pwindow_config[
+                                                   'dim_head'],
+                                               drop_out=pwindow_config[
+                                                   'dropout'],
+                                               window_size=pwindow_config[
+                                                   'window_size'],
+                                               relative_pos_embedding=
+                                               pwindow_config[
+                                                   'relative_pos_embedding'],
+                                               fuse_method=pwindow_config[
+                                                   'fusion_method']))]))
+
+    def forward(self, x, mask, prior_encoding):
+        for cav_attn, pwindow_attn in self.layers:
+            x = cav_attn(x, mask=mask, prior_encoding=prior_encoding) + x
+            x = pwindow_attn(x) + x
+        return x
+
+
+class V2XTEncoder(nn.Module):
+    def __init__(self, args):
+        super().__init__()
+
+        cav_att_config = args['cav_att_config']
+        pwindow_att_config = args['pwindow_att_config']
+        feed_config = args['feed_forward']
+
+        num_blocks = args['num_blocks']
+        depth = args['depth']
+        mlp_dim = feed_config['mlp_dim']
+        dropout = feed_config['dropout']
+
+        self.downsample_rate = args['sttf']['downsample_rate']
+        self.discrete_ratio = args['sttf']['voxel_size'][0]
+        self.use_roi_mask = args['use_roi_mask']
+        self.use_RTE = cav_att_config['use_RTE']
+        self.RTE_ratio = cav_att_config['RTE_ratio']
+        self.sttf = STTF(args['sttf'])
+        # adjust the channel numbers from 256+3 -> 256
+        self.prior_feed = nn.Linear(cav_att_config['dim'] + 3,
+                                    cav_att_config['dim'])
+        self.layers = nn.ModuleList([])
+        if self.use_RTE:
+            self.rte = RTE(cav_att_config['dim'], self.RTE_ratio)
+        for _ in range(depth):
+            self.layers.append(nn.ModuleList([
+                V2XFusionBlock(num_blocks, cav_att_config, pwindow_att_config),
+                PreNorm(cav_att_config['dim'],
+                        FeedForward(cav_att_config['dim'], mlp_dim,
+                                    dropout=dropout))
+            ]))
+
+    def forward(self, x, mask, spatial_correction_matrix):
+
+        # transform the features to the current timestamp
+        # velocity, time_delay, infra
+        # (B,L,H,W,3)
+        prior_encoding = x[..., -3:]
+        # (B,L,H,W,C)
+        x = x[..., :-3]
+        if self.use_RTE:
+            # dt: (B,L)
+            dt = prior_encoding[:, :, 0, 0, 1].to(torch.int)
+            x = self.rte(x, dt)
+        x = self.sttf(x, mask, spatial_correction_matrix)
+        com_mask = mask.unsqueeze(1).unsqueeze(2).unsqueeze(
+            3) if not self.use_roi_mask else get_roi_and_cav_mask(x.shape,
+                                                                  mask,
+                                                                  spatial_correction_matrix,
+                                                                  self.discrete_ratio,
+                                                                  self.downsample_rate)
+        for attn, ff in self.layers:
+            x = attn(x, mask=com_mask, prior_encoding=prior_encoding)
+            x = ff(x) + x
+        return x
+
+
+class V2XTransformer(nn.Module):
+    def __init__(self, args):
+        super(V2XTransformer, self).__init__()
+
+        encoder_args = args['encoder']
+        self.encoder = V2XTEncoder(encoder_args)
+
+    def forward(self, x, mask, spatial_correction_matrix):
+        output = self.encoder(x, mask, spatial_correction_matrix)
+        output = output[:, 0]
+        return output
diff --git a/opencood/opencood/models/fuse_modules/where2comm_fuse.py b/opencood/opencood/models/fuse_modules/where2comm_fuse.py
new file mode 100644
index 00000000..4fd36e64
--- /dev/null
+++ b/opencood/opencood/models/fuse_modules/where2comm_fuse.py
@@ -0,0 +1,208 @@
+"""
+Implementation of Where2comm fusion.
+"""
+
+import numpy as np
+import random
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from opencood.models.fuse_modules.self_attn import ScaledDotProductAttention
+
+
+class Communication(nn.Module):
+    def __init__(self, args):
+        super(Communication, self).__init__()
+        # Threshold of objectiveness
+        self.threshold = args['threshold']
+        if 'gaussian_smooth' in args:
+            # Gaussian Smooth
+            self.smooth = True
+            kernel_size = args['gaussian_smooth']['k_size']
+            c_sigma = args['gaussian_smooth']['c_sigma']
+            self.gaussian_filter = nn.Conv2d(1, 1, kernel_size=kernel_size, stride=1, padding=(kernel_size - 1) // 2)
+            self.init_gaussian_filter(kernel_size, c_sigma)
+            self.gaussian_filter.requires_grad = False
+        else:
+            self.smooth = False
+
+    def init_gaussian_filter(self, k_size=5, sigma=1.0):
+        center = k_size // 2
+        x, y = np.mgrid[0 - center: k_size - center, 0 - center: k_size - center]
+        gaussian_kernel = 1 / (2 * np.pi * sigma) * np.exp(-(np.square(x) + np.square(y)) / (2 * np.square(sigma)))
+
+        self.gaussian_filter.weight.data = torch.Tensor(gaussian_kernel).to(
+            self.gaussian_filter.weight.device).unsqueeze(0).unsqueeze(0)
+        self.gaussian_filter.bias.data.zero_()
+
+    def forward(self, batch_confidence_maps, B):
+        """
+        Args:
+            batch_confidence_maps: [(L1, H, W), (L2, H, W), ...]
+        """
+
+        _, _, H, W = batch_confidence_maps[0].shape
+
+        communication_masks = []
+        communication_rates = []
+        for b in range(B):
+            ori_communication_maps, _ = batch_confidence_maps[b].sigmoid().max(dim=1, keepdim=True)
+            if self.smooth:
+                communication_maps = self.gaussian_filter(ori_communication_maps)
+            else:
+                communication_maps = ori_communication_maps
+
+            L = communication_maps.shape[0]
+            if self.training:
+                # Official training proxy objective
+                K = int(H * W * random.uniform(0, 1))
+                communication_maps = communication_maps.reshape(L, H * W)
+                _, indices = torch.topk(communication_maps, k=K, sorted=False)
+                communication_mask = torch.zeros_like(communication_maps).to(communication_maps.device)
+                ones_fill = torch.ones(L, K, dtype=communication_maps.dtype, device=communication_maps.device)
+                communication_mask = torch.scatter(communication_mask, -1, indices, ones_fill).reshape(L, 1, H, W)
+            elif self.threshold:
+                ones_mask = torch.ones_like(communication_maps).to(communication_maps.device)
+                zeros_mask = torch.zeros_like(communication_maps).to(communication_maps.device)
+                communication_mask = torch.where(communication_maps > self.threshold, ones_mask, zeros_mask)
+            else:
+                communication_mask = torch.ones_like(communication_maps).to(communication_maps.device)
+
+            communication_rate = communication_mask.sum() / (L * H * W)
+            # Ego
+            communication_mask[0] = 1
+
+            communication_masks.append(communication_mask)
+            communication_rates.append(communication_rate)
+        communication_rates = sum(communication_rates) / B
+        communication_masks = torch.cat(communication_masks, dim=0)
+        return communication_masks, communication_rates
+
+
+class AttentionFusion(nn.Module):
+    def __init__(self, feature_dim):
+        super(AttentionFusion, self).__init__()
+        self.att = ScaledDotProductAttention(feature_dim)
+
+    def forward(self, x):
+        cav_num, C, H, W = x.shape
+        x = x.view(cav_num, C, -1).permute(2, 0, 1)  # (H*W, cav_num, C), perform self attention on each pixel
+        x = self.att(x, x, x)
+        x = x.permute(1, 2, 0).view(cav_num, C, H, W)[0]  # C, W, H before
+        return x
+
+
+class Where2comm(nn.Module):
+    def __init__(self, args):
+        super(Where2comm, self).__init__()
+        self.discrete_ratio = args['voxel_size'][0]
+        self.downsample_rate = args['downsample_rate']
+
+        self.fully = args['fully']
+        if self.fully:
+            print('constructing a fully connected communication graph')
+        else:
+            print('constructing a partially connected communication graph')
+
+        self.multi_scale = args['multi_scale']
+        if self.multi_scale:
+            layer_nums = args['layer_nums']
+            num_filters = args['num_filters']
+            self.num_levels = len(layer_nums)
+            self.fuse_modules = nn.ModuleList()
+            for idx in range(self.num_levels):
+                fuse_network = AttentionFusion(num_filters[idx])
+                self.fuse_modules.append(fuse_network)
+        else:
+            self.fuse_modules = AttentionFusion(args['in_channels'])
+
+        self.naive_communication = Communication(args['communication'])
+
+    def regroup(self, x, record_len):
+        cum_sum_len = torch.cumsum(record_len, dim=0)
+        split_x = torch.tensor_split(x, cum_sum_len[:-1].cpu())
+        return split_x
+
+    def forward(self, x, psm_single, record_len, pairwise_t_matrix, backbone=None):
+        """
+        Fusion forwarding.
+
+        Parameters:
+            x: Input data, (sum(n_cav), C, H, W).
+            record_len: List, (B).
+            pairwise_t_matrix: The transformation matrix from each cav to ego, (B, L, L, 4, 4).
+
+        Returns:
+            Fused feature.
+        """
+
+        _, C, H, W = x.shape
+        B = pairwise_t_matrix.shape[0]
+
+        if self.multi_scale:
+            ups = []
+
+            for i in range(self.num_levels):
+                x = backbone.blocks[i](x)
+
+                # 1. Communication (mask the features)
+                if i == 0:
+                    if self.fully:
+                        communication_rates = torch.tensor(1).to(x.device)
+                    else:
+                        # Prune
+                        batch_confidence_maps = self.regroup(psm_single, record_len)
+                        communication_masks, communication_rates = self.naive_communication(batch_confidence_maps, B)
+                        if x.shape[-1] != communication_masks.shape[-1]:
+                            communication_masks = F.interpolate(communication_masks, size=(x.shape[-2], x.shape[-1]),
+                                                                mode='bilinear', align_corners=False)
+                        x = x * communication_masks
+
+                # 2. Split the features
+                # split_x: [(L1, C, H, W), (L2, C, H, W), ...]
+                # For example [[2, 256, 48, 176], [1, 256, 48, 176], ...]
+                batch_node_features = self.regroup(x, record_len)
+
+                # 3. Fusion
+                x_fuse = []
+                for b in range(B):
+                    neighbor_feature = batch_node_features[b]
+                    x_fuse.append(self.fuse_modules[i](neighbor_feature))
+                x_fuse = torch.stack(x_fuse)
+
+                # 4. Deconv
+                if len(backbone.deblocks) > 0:
+                    ups.append(backbone.deblocks[i](x_fuse))
+                else:
+                    ups.append(x_fuse)
+
+            if len(ups) > 1:
+                x_fuse = torch.cat(ups, dim=1)
+            elif len(ups) == 1:
+                x_fuse = ups[0]
+
+            if len(backbone.deblocks) > self.num_levels:
+                x_fuse = backbone.deblocks[-1](x_fuse)
+        else:
+            # 1. Communication (mask the features)
+            if self.fully:
+                communication_rates = torch.tensor(1).to(x.device)
+            else:
+                # Prune
+                batch_confidence_maps = self.regroup(psm_single, record_len)
+                communication_masks, communication_rates = self.naive_communication(batch_confidence_maps, B)
+                x = x * communication_masks
+
+            # 2. Split the features
+            # split_x: [(L1, C, H, W), (L2, C, H, W), ...]
+            # For example [[2, 256, 48, 176], [1, 256, 48, 176], ...]
+            batch_node_features = self.regroup(x, record_len)
+
+            # 3. Fusion
+            x_fuse = []
+            for b in range(B):
+                neighbor_feature = batch_node_features[b]
+                x_fuse.append(self.fuse_modules(neighbor_feature))
+            x_fuse = torch.stack(x_fuse)
+        return x_fuse, communication_rates
diff --git a/opencood/opencood/models/pixor.py b/opencood/opencood/models/pixor.py
new file mode 100644
index 00000000..e29d7ff6
--- /dev/null
+++ b/opencood/opencood/models/pixor.py
@@ -0,0 +1,310 @@
+# -*- coding: utf-8 -*-
+# Author: Hao Xiang <haxiang@g.ucla.edu>
+# License: TDG-Attribution-NonCommercial-NoDistrib
+
+
+import math
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+def conv3x3(in_planes, out_planes, stride=1, bias=False):
+    """3x3 convolution with padding"""
+    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
+                     padding=1, bias=bias)
+
+
+class BasicBlock(nn.Module):
+    expansion = 1
+
+    def __init__(self, in_planes, planes, stride=1, downsample=None):
+        super(BasicBlock, self).__init__()
+        self.conv1 = conv3x3(in_planes, planes, stride, bias=True)
+        self.bn1 = nn.BatchNorm2d(planes)
+        self.relu = nn.ReLU(inplace=True)
+        self.conv2 = conv3x3(planes, planes, bias=True)
+        self.bn2 = nn.BatchNorm2d(planes)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        # out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        # out = self.bn2(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class Bottleneck(nn.Module):
+    expansion = 4
+
+    def __init__(self, in_planes, planes, stride=1, downsample=None,
+                 use_bn=True):
+        super(Bottleneck, self).__init__()
+        bias = not use_bn
+        self.use_bn = use_bn
+        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=bias)
+        self.bn1 = nn.BatchNorm2d(planes)
+        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
+                               padding=1, bias=bias)
+        self.bn2 = nn.BatchNorm2d(planes)
+        self.conv3 = nn.Conv2d(planes, self.expansion * planes, kernel_size=1,
+                               bias=bias)
+        self.bn3 = nn.BatchNorm2d(self.expansion * planes)
+        self.downsample = downsample
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        """
+        Forward pass of residual block.
+        Parameters
+        ----------
+        x : torch.Tensor
+            Shape (N, C, W, L).
+
+        Returns
+        -------
+        out : torch.Tensor
+            Shape (N, self.expansion*planes, W/stride, L/stride).
+        """
+        residual = x
+        # (N, planes, W, L)
+        out = self.conv1(x)
+        if self.use_bn:
+            out = self.bn1(out)
+        out = self.relu(out)
+        # (N, planes, W/stride, L/stride)
+        out = self.conv2(out)
+        if self.use_bn:
+            out = self.bn2(out)
+        out = self.relu(out)
+        # (N, self.expansion*planes, W/stride, L/stride)
+        out = self.conv3(out)
+        if self.use_bn:
+            out = self.bn3(out)
+
+        if self.downsample is not None:
+            # (N, self.expansion*planes, W/2, L/2)
+            residual = self.downsample(x)
+        out = self.relu(residual + out)
+        return out
+
+
+class BackBone(nn.Module):
+
+    def __init__(self, block, num_block, geom, use_bn=True):
+        super(BackBone, self).__init__()
+
+        self.use_bn = use_bn
+
+        # Block 1
+        self.conv1 = conv3x3(geom["input_shape"][-1], 32)
+        self.conv2 = conv3x3(32, 32)
+        self.bn1 = nn.BatchNorm2d(32)
+        self.bn2 = nn.BatchNorm2d(32)
+        self.relu = nn.ReLU(inplace=True)
+
+        # Block 2-5
+        self.in_planes = 32
+        self.block2 = self._make_layer(block, 24, num_blocks=num_block[0])
+        self.block3 = self._make_layer(block, 48, num_blocks=num_block[1])
+        self.block4 = self._make_layer(block, 64, num_blocks=num_block[2])
+        self.block5 = self._make_layer(block, 96, num_blocks=num_block[3])
+
+        # Lateral layers
+        self.latlayer1 = nn.Conv2d(384, 196, kernel_size=1, stride=1,
+                                   padding=0)
+        self.latlayer2 = nn.Conv2d(256, 128, kernel_size=1, stride=1,
+                                   padding=0)
+        self.latlayer3 = nn.Conv2d(192, 96, kernel_size=1, stride=1, padding=0)
+
+        # Top-down layers
+        self.deconv1 = nn.ConvTranspose2d(196, 128, kernel_size=3, stride=2,
+                                          padding=1, output_padding=1)
+        p = 0 if geom['label_shape'][1] == 175 else 1
+        self.deconv2 = nn.ConvTranspose2d(128, 96, kernel_size=3, stride=2,
+                                          padding=1, output_padding=(1, p))
+
+    def encode(self, x):
+        x = self.conv1(x)
+        if self.use_bn:
+            x = self.bn1(x)
+        x = self.relu(x)
+
+        x = self.conv2(x)
+        if self.use_bn:
+            x = self.bn2(x)
+        c1 = self.relu(x)
+
+        # bottom up layers
+        c2 = self.block2(c1)
+        c3 = self.block3(c2)
+        c4 = self.block4(c3)
+        c5 = self.block5(c4)
+
+        return c3, c4, c5
+
+    def decode(self, c3, c4, c5):
+        l5 = self.latlayer1(c5)
+        l4 = self.latlayer2(c4)
+        p5 = l4 + self.deconv1(l5)
+        l3 = self.latlayer3(c3)
+        p4 = l3 + self.deconv2(p5)
+
+        return p4
+
+    def forward(self, x):
+        c3, c4, c5 = self.encode(x)
+        p4 = self.decode(c3, c4, c5)
+
+        return p4
+
+    def _make_layer(self, block, planes, num_blocks):
+
+        if self.use_bn:
+            # downsample the H*W by 1/2
+            downsample = nn.Sequential(
+                nn.Conv2d(self.in_planes, planes * block.expansion,
+                          kernel_size=1, stride=2, bias=False),
+                nn.BatchNorm2d(planes * block.expansion)
+            )
+        else:
+            downsample = nn.Conv2d(self.in_planes, planes * block.expansion,
+                                   kernel_size=1, stride=2, bias=True)
+
+        layers = [
+            block(self.in_planes, planes, stride=2, downsample=downsample)]
+
+        self.in_planes = planes * block.expansion
+        for i in range(1, num_blocks):
+            layers.append(block(self.in_planes, planes, stride=1))
+            self.in_planes = planes * block.expansion
+        return nn.Sequential(*layers)
+
+    def _upsample_add(self, x, y):
+        """Upsample and add two feature maps.
+        Args:
+          x: (Variable) top feature map to be upsampled.
+          y: (Variable) lateral feature map.
+        Returns:
+          (Variable) added feature map.
+        Note in PyTorch, when input size is odd, the upsampled feature map
+        with `F.upsample(..., scale_factor=2, mode='nearest')`
+        maybe not equal to the lateral feature map size.
+        e.g.
+        original input size: [N,_,15,15] ->
+        conv2d feature map size: [N,_,8,8] ->
+        upsampled feature map size: [N,_,16,16]
+        So we choose bilinear upsample which supports arbitrary output sizes.
+        """
+        _, _, H, W = y.size()
+        return F.upsample(x, size=(H, W), mode='bilinear') + y
+
+
+class Header(nn.Module):
+
+    def __init__(self, use_bn=True):
+        super(Header, self).__init__()
+
+        self.use_bn = use_bn
+        bias = not use_bn
+        self.conv1 = conv3x3(96, 96, bias=bias)
+        self.bn1 = nn.BatchNorm2d(96)
+        self.conv2 = conv3x3(96, 96, bias=bias)
+        self.bn2 = nn.BatchNorm2d(96)
+        self.conv3 = conv3x3(96, 96, bias=bias)
+        self.bn3 = nn.BatchNorm2d(96)
+        self.conv4 = conv3x3(96, 96, bias=bias)
+        self.bn4 = nn.BatchNorm2d(96)
+
+        self.clshead = conv3x3(96, 1, bias=True)
+        self.reghead = conv3x3(96, 6, bias=True)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        if self.use_bn:
+            x = self.bn1(x)
+        x = self.conv2(x)
+        if self.use_bn:
+            x = self.bn2(x)
+        x = self.conv3(x)
+        if self.use_bn:
+            x = self.bn3(x)
+        x = self.conv4(x)
+        if self.use_bn:
+            x = self.bn4(x)
+
+        cls = self.clshead(x)
+        reg = self.reghead(x)
+
+        return cls, reg
+
+
+class PIXOR(nn.Module):
+    """
+    The Pixor backbone. The input of PIXOR nn module is a tensor of
+    [batch_size, height, weight, channel], The output of PIXOR nn module
+    is also a tensor of [batch_size, height/4, weight/4, channel].  Note that
+     we convert the dimensions to [C, H, W] for PyTorch's nn.Conv2d functions
+
+    Parameters
+    ----------
+    args : dict
+        The arguments of the model.
+
+    Attributes
+    ----------
+    backbone : opencood.object
+        The backbone used to extract features.
+    header : opencood.object
+        Header used to predict the classification and coordinates.
+    """
+
+    def __init__(self, args):
+        super(PIXOR, self).__init__()
+        geom = args["geometry_param"]
+        use_bn = args["use_bn"]
+        self.backbone = BackBone(Bottleneck, [3, 6, 6, 3], geom, use_bn)
+        self.header = Header(use_bn)
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
+                m.weight.data.normal_(0, math.sqrt(2. / n))
+            elif isinstance(m, nn.BatchNorm2d):
+                m.weight.data.fill_(1)
+                m.bias.data.zero_()
+
+        prior = 0.01
+        self.header.clshead.weight.data.fill_(-math.log((1.0 - prior) / prior))
+        self.header.clshead.bias.data.fill_(0)
+        self.header.reghead.weight.data.fill_(0)
+        self.header.reghead.bias.data.fill_(0)
+
+    def forward(self, data_dict):
+        bev_input = data_dict['processed_lidar']["bev_input"]
+
+        features = self.backbone(bev_input)
+        # cls -- (N, 1, W/4, L/4)
+        # reg -- (N, 6, W/4, L/4)
+        cls, reg = self.header(features)
+
+        output_dict = {
+            "cls": cls,
+            "reg": reg
+        }
+
+        return output_dict
diff --git a/opencood/opencood/models/pixor_intermediate.py b/opencood/opencood/models/pixor_intermediate.py
new file mode 100644
index 00000000..12c17dd5
--- /dev/null
+++ b/opencood/opencood/models/pixor_intermediate.py
@@ -0,0 +1,93 @@
+# -*- coding: utf-8 -*-
+# Author: Hao Xiang <haxiang@g.ucla.edu>
+# License: TDG-Attribution-NonCommercial-NoDistrib
+
+
+import math
+
+import torch.nn as nn
+
+from opencood.models.fuse_modules.self_attn import AttFusion
+from opencood.models.pixor import Bottleneck, BackBone, Header
+
+
+class BackBoneIntermediate(BackBone):
+
+    def __init__(self, block, num_block, geom, use_bn=True):
+        super(BackBoneIntermediate, self).__init__(block,
+                                                   num_block,
+                                                   geom, use_bn)
+
+        self.fusion_net3 = AttFusion(192)
+        self.fusion_net4 = AttFusion(256)
+        self.fusion_net5 = AttFusion(384)
+
+    def forward(self, x, record_len):
+        c3, c4, c5 = self.encode(x)
+
+        c5 = self.fusion_net5(c5, record_len)
+        c4 = self.fusion_net4(c4, record_len)
+        c3 = self.fusion_net3(c3, record_len)
+
+        p4 = self.decode(c3, c4, c5)
+        return p4
+
+
+class PIXORIntermediate(nn.Module):
+    """
+    The Pixor backbone. The input of PIXOR nn module is a tensor of
+    [batch_size, height, weight, channel], The output of PIXOR nn module
+    is also a tensor of [batch_size, height/4, weight/4, channel].  Note that
+     we convert the dimensions to [C, H, W] for PyTorch's nn.Conv2d functions
+
+    Parameters
+    ----------
+    args : dict
+        The arguments of the model.
+
+    Attributes
+    ----------
+    backbone : opencood.object
+        The backbone used to extract features.
+    header : opencood.object
+        Header used to predict the classification and coordinates.
+    """
+
+    def __init__(self, args):
+        super(PIXORIntermediate, self).__init__()
+        geom = args["geometry_param"]
+        use_bn = args["use_bn"]
+        self.backbone = BackBoneIntermediate(Bottleneck, [3, 6, 6, 3],
+                                             geom,
+                                             use_bn)
+        self.header = Header(use_bn)
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
+                m.weight.data.normal_(0, math.sqrt(2. / n))
+            elif isinstance(m, nn.BatchNorm2d):
+                m.weight.data.fill_(1)
+                m.bias.data.zero_()
+
+        prior = 0.01
+        self.header.clshead.weight.data.fill_(-math.log((1.0 - prior) / prior))
+        self.header.clshead.bias.data.fill_(0)
+        self.header.reghead.weight.data.fill_(0)
+        self.header.reghead.bias.data.fill_(0)
+
+    def forward(self, data_dict):
+        bev_input = data_dict['processed_lidar']["bev_input"]
+        record_len = data_dict['record_len']
+
+        features = self.backbone(bev_input, record_len)
+        # cls -- (N, 1, W/4, L/4)
+        # reg -- (N, 6, W/4, L/4)
+        cls, reg = self.header(features)
+
+        output_dict = {
+            "cls": cls,
+            "reg": reg
+        }
+
+        return output_dict
diff --git a/opencood/opencood/models/point_pillar.py b/opencood/opencood/models/point_pillar.py
new file mode 100644
index 00000000..73910d4d
--- /dev/null
+++ b/opencood/opencood/models/point_pillar.py
@@ -0,0 +1,54 @@
+# -*- coding: utf-8 -*-
+# Author: Runsheng Xu <rxx3386@ucla.edu>, OpenPCDet
+# License: TDG-Attribution-NonCommercial-NoDistrib
+
+
+import torch
+import torch.nn as nn
+
+
+from opencood.models.sub_modules.pillar_vfe import PillarVFE
+from opencood.models.sub_modules.point_pillar_scatter import PointPillarScatter
+from opencood.models.sub_modules.base_bev_backbone import BaseBEVBackbone
+
+
+class PointPillar(nn.Module):
+    def __init__(self, args):
+        super(PointPillar, self).__init__()
+
+        # PIllar VFE
+        self.pillar_vfe = PillarVFE(args['pillar_vfe'],
+                                    num_point_features=4,
+                                    voxel_size=args['voxel_size'],
+                                    point_cloud_range=args['lidar_range'])
+        self.scatter = PointPillarScatter(args['point_pillar_scatter'])
+        self.backbone = BaseBEVBackbone(args['base_bev_backbone'], 64)
+
+        self.cls_head = nn.Conv2d(128 * 3, args['anchor_number'],
+                                  kernel_size=1)
+        self.reg_head = nn.Conv2d(128 * 3, 7 * args['anchor_num'],
+                                  kernel_size=1)
+
+    def forward(self, data_dict):
+
+        voxel_features = data_dict['processed_lidar']['voxel_features']
+        voxel_coords = data_dict['processed_lidar']['voxel_coords']
+        voxel_num_points = data_dict['processed_lidar']['voxel_num_points']
+
+        batch_dict = {'voxel_features': voxel_features,
+                      'voxel_coords': voxel_coords,
+                      'voxel_num_points': voxel_num_points}
+
+        batch_dict = self.pillar_vfe(batch_dict)
+        batch_dict = self.scatter(batch_dict)
+        batch_dict = self.backbone(batch_dict)
+
+        spatial_features_2d = batch_dict['spatial_features_2d']
+
+        psm = self.cls_head(spatial_features_2d)
+        rm = self.reg_head(spatial_features_2d)
+
+        output_dict = {'psm': psm,
+                       'rm': rm}
+
+        return output_dict
\ No newline at end of file
diff --git a/opencood/opencood/models/point_pillar_cobevt.py b/opencood/opencood/models/point_pillar_cobevt.py
new file mode 100644
index 00000000..74afedab
--- /dev/null
+++ b/opencood/opencood/models/point_pillar_cobevt.py
@@ -0,0 +1,116 @@
+import torch
+import torch.nn as nn
+from einops import rearrange, repeat
+
+from opencood.models.sub_modules.pillar_vfe import PillarVFE
+from opencood.models.sub_modules.point_pillar_scatter import PointPillarScatter
+from opencood.models.sub_modules.base_bev_backbone import BaseBEVBackbone
+from opencood.models.sub_modules.downsample_conv import DownsampleConv
+from opencood.models.sub_modules.naive_compress import NaiveCompressor
+from opencood.models.fuse_modules.swap_fusion_modules import \
+    SwapFusionEncoder
+from opencood.models.fuse_modules.fuse_utils import regroup
+
+
+class PointPillarCoBEVT(nn.Module):
+    def __init__(self, args):
+        super(PointPillarCoBEVT, self).__init__()
+
+        self.max_cav = args['max_cav']
+        # PIllar VFE
+        self.pillar_vfe = PillarVFE(args['pillar_vfe'],
+                                    num_point_features=4,
+                                    voxel_size=args['voxel_size'],
+                                    point_cloud_range=args['lidar_range'])
+        self.scatter = PointPillarScatter(args['point_pillar_scatter'])
+        self.backbone = BaseBEVBackbone(args['base_bev_backbone'], 64)
+        # used to downsample the feature map for efficient computation
+        self.shrink_flag = False
+        if 'shrink_header' in args:
+            self.shrink_flag = True
+            self.shrink_conv = DownsampleConv(args['shrink_header'])
+        self.compression = False
+
+        if args['compression'] > 0:
+            self.compression = True
+            self.naive_compressor = NaiveCompressor(256, args['compression'])
+
+        self.fusion_net = SwapFusionEncoder(args['fax_fusion'])
+
+        self.cls_head = nn.Conv2d(128 * 2, args['anchor_number'],
+                                  kernel_size=1)
+        self.reg_head = nn.Conv2d(128 * 2, 7 * args['anchor_number'],
+                                  kernel_size=1)
+
+        if args['backbone_fix']:
+            self.backbone_fix()
+
+    def backbone_fix(self):
+        """
+        Fix the parameters of backbone during finetune on timedelay。
+        """
+        for p in self.pillar_vfe.parameters():
+            p.requires_grad = False
+
+        for p in self.scatter.parameters():
+            p.requires_grad = False
+
+        for p in self.backbone.parameters():
+            p.requires_grad = False
+
+        if self.compression:
+            for p in self.naive_compressor.parameters():
+                p.requires_grad = False
+        if self.shrink_flag:
+            for p in self.shrink_conv.parameters():
+                p.requires_grad = False
+
+        for p in self.cls_head.parameters():
+            p.requires_grad = False
+        for p in self.reg_head.parameters():
+            p.requires_grad = False
+
+    def forward(self, data_dict):
+        voxel_features = data_dict['processed_lidar']['voxel_features']
+        voxel_coords = data_dict['processed_lidar']['voxel_coords']
+        voxel_num_points = data_dict['processed_lidar']['voxel_num_points']
+        record_len = data_dict['record_len']
+        spatial_correction_matrix = data_dict['spatial_correction_matrix']
+
+        batch_dict = {'voxel_features': voxel_features,
+                      'voxel_coords': voxel_coords,
+                      'voxel_num_points': voxel_num_points,
+                      'record_len': record_len}
+        # n, 4 -> n, c
+        batch_dict = self.pillar_vfe(batch_dict)
+        # n, c -> N, C, H, W
+        batch_dict = self.scatter(batch_dict)
+        batch_dict = self.backbone(batch_dict)
+
+        spatial_features_2d = batch_dict['spatial_features_2d']
+        # downsample feature to reduce memory
+        if self.shrink_flag:
+            spatial_features_2d = self.shrink_conv(spatial_features_2d)
+        # compressor
+        if self.compression:
+            spatial_features_2d = self.naive_compressor(spatial_features_2d)
+
+        # N, C, H, W -> B,  L, C, H, W
+        regroup_feature, mask = regroup(spatial_features_2d,
+                                        record_len,
+                                        self.max_cav)
+        com_mask = mask.unsqueeze(1).unsqueeze(2).unsqueeze(3)
+        com_mask = repeat(com_mask,
+                          'b h w c l -> b (h new_h) (w new_w) c l',
+                          new_h=regroup_feature.shape[3],
+                          new_w=regroup_feature.shape[4])
+
+        fused_feature = self.fusion_net(regroup_feature, com_mask)
+
+        psm = self.cls_head(fused_feature)
+        rm = self.reg_head(fused_feature)
+
+        output_dict = {'psm': psm,
+                       'rm': rm}
+
+        return output_dict
diff --git a/opencood/opencood/models/point_pillar_fcooper.py b/opencood/opencood/models/point_pillar_fcooper.py
new file mode 100644
index 00000000..42693cac
--- /dev/null
+++ b/opencood/opencood/models/point_pillar_fcooper.py
@@ -0,0 +1,109 @@
+# -*- coding: utf-8 -*-
+# Author: Runsheng Xu <rxx3386@ucla.edu>
+# License: TDG-Attribution-NonCommercial-NoDistrib
+
+
+import torch.nn as nn
+
+from opencood.models.sub_modules.pillar_vfe import PillarVFE
+from opencood.models.sub_modules.point_pillar_scatter import PointPillarScatter
+from opencood.models.sub_modules.base_bev_backbone import BaseBEVBackbone
+from opencood.models.sub_modules.downsample_conv import DownsampleConv
+from opencood.models.sub_modules.naive_compress import NaiveCompressor
+from opencood.models.fuse_modules.f_cooper_fuse import SpatialFusion
+
+
+class PointPillarFCooper(nn.Module):
+    """
+    F-Cooper implementation with point pillar backbone.
+    """
+    def __init__(self, args):
+        super(PointPillarFCooper, self).__init__()
+
+        self.max_cav = args['max_cav']
+        # PIllar VFE
+        self.pillar_vfe = PillarVFE(args['pillar_vfe'],
+                                    num_point_features=4,
+                                    voxel_size=args['voxel_size'],
+                                    point_cloud_range=args['lidar_range'])
+        self.scatter = PointPillarScatter(args['point_pillar_scatter'])
+        self.backbone = BaseBEVBackbone(args['base_bev_backbone'], 64)
+        # used to downsample the feature map for efficient computation
+        self.shrink_flag = False
+        if 'shrink_header' in args:
+            self.shrink_flag = True
+            self.shrink_conv = DownsampleConv(args['shrink_header'])
+        self.compression = False
+
+        if args['compression'] > 0:
+            self.compression = True
+            self.naive_compressor = NaiveCompressor(256, args['compression'])
+
+        self.fusion_net = SpatialFusion()
+
+        self.cls_head = nn.Conv2d(128 * 2, args['anchor_number'],
+                                  kernel_size=1)
+        self.reg_head = nn.Conv2d(128 * 2, 7 * args['anchor_number'],
+                                  kernel_size=1)
+
+        if args['backbone_fix']:
+            self.backbone_fix()
+
+    def backbone_fix(self):
+        """
+        Fix the parameters of backbone during finetune on timedelay。
+        """
+        for p in self.pillar_vfe.parameters():
+            p.requires_grad = False
+
+        for p in self.scatter.parameters():
+            p.requires_grad = False
+
+        for p in self.backbone.parameters():
+            p.requires_grad = False
+
+        if self.compression:
+            for p in self.naive_compressor.parameters():
+                p.requires_grad = False
+        if self.shrink_flag:
+            for p in self.shrink_conv.parameters():
+                p.requires_grad = False
+
+        for p in self.cls_head.parameters():
+            p.requires_grad = False
+        for p in self.reg_head.parameters():
+            p.requires_grad = False
+
+    def forward(self, data_dict):
+        voxel_features = data_dict['processed_lidar']['voxel_features']
+        voxel_coords = data_dict['processed_lidar']['voxel_coords']
+        voxel_num_points = data_dict['processed_lidar']['voxel_num_points']
+        record_len = data_dict['record_len']
+
+        batch_dict = {'voxel_features': voxel_features,
+                      'voxel_coords': voxel_coords,
+                      'voxel_num_points': voxel_num_points,
+                      'record_len': record_len}
+        # n, 4 -> n, c
+        batch_dict = self.pillar_vfe(batch_dict)
+        # n, c -> N, C, H, W
+        batch_dict = self.scatter(batch_dict)
+        batch_dict = self.backbone(batch_dict)
+
+        spatial_features_2d = batch_dict['spatial_features_2d']
+        # downsample feature to reduce memory
+        if self.shrink_flag:
+            spatial_features_2d = self.shrink_conv(spatial_features_2d)
+        # compressor
+        if self.compression:
+            spatial_features_2d = self.naive_compressor(spatial_features_2d)
+
+        fused_feature = self.fusion_net(spatial_features_2d, record_len)
+
+        psm = self.cls_head(fused_feature)
+        rm = self.reg_head(fused_feature)
+
+        output_dict = {'psm': psm,
+                       'rm': rm}
+
+        return output_dict
diff --git a/opencood/opencood/models/point_pillar_intermediate.py b/opencood/opencood/models/point_pillar_intermediate.py
new file mode 100644
index 00000000..21636f55
--- /dev/null
+++ b/opencood/opencood/models/point_pillar_intermediate.py
@@ -0,0 +1,56 @@
+# -*- coding: utf-8 -*-
+# Author: Runsheng Xu <rxx3386@ucla.edu>
+# License: TDG-Attribution-NonCommercial-NoDistrib
+
+
+import torch
+import torch.nn as nn
+
+
+from opencood.models.sub_modules.pillar_vfe import PillarVFE
+from opencood.models.sub_modules.point_pillar_scatter import PointPillarScatter
+from opencood.models.sub_modules.att_bev_backbone import AttBEVBackbone
+
+
+class PointPillarIntermediate(nn.Module):
+    def __init__(self, args):
+        super(PointPillarIntermediate, self).__init__()
+
+        # PIllar VFE
+        self.pillar_vfe = PillarVFE(args['pillar_vfe'],
+                                    num_point_features=4,
+                                    voxel_size=args['voxel_size'],
+                                    point_cloud_range=args['lidar_range'])
+        self.scatter = PointPillarScatter(args['point_pillar_scatter'])
+        self.backbone = AttBEVBackbone(args['base_bev_backbone'], 64)
+
+        self.cls_head = nn.Conv2d(128 * 3, args['anchor_number'],
+                                  kernel_size=1)
+        self.reg_head = nn.Conv2d(128 * 3, 7 * args['anchor_num'],
+                                  kernel_size=1)
+
+    def forward(self, data_dict):
+
+        voxel_features = data_dict['processed_lidar']['voxel_features']
+        voxel_coords = data_dict['processed_lidar']['voxel_coords']
+        voxel_num_points = data_dict['processed_lidar']['voxel_num_points']
+        record_len = data_dict['record_len']
+
+        batch_dict = {'voxel_features': voxel_features,
+                      'voxel_coords': voxel_coords,
+                      'voxel_num_points': voxel_num_points,
+                      'record_len': record_len}
+
+        batch_dict = self.pillar_vfe(batch_dict)
+        batch_dict = self.scatter(batch_dict)
+        batch_dict = self.backbone(batch_dict)
+
+        spatial_features_2d = batch_dict['spatial_features_2d']
+
+        psm = self.cls_head(spatial_features_2d)
+        rm = self.reg_head(spatial_features_2d)
+
+        output_dict = {'psm': psm,
+                       'rm': rm}
+
+        return output_dict
\ No newline at end of file
diff --git a/opencood/opencood/models/point_pillar_transformer.py b/opencood/opencood/models/point_pillar_transformer.py
new file mode 100644
index 00000000..d1bb6846
--- /dev/null
+++ b/opencood/opencood/models/point_pillar_transformer.py
@@ -0,0 +1,122 @@
+import torch
+import torch.nn as nn
+
+from opencood.models.sub_modules.pillar_vfe import PillarVFE
+from opencood.models.sub_modules.point_pillar_scatter import PointPillarScatter
+from opencood.models.sub_modules.base_bev_backbone import BaseBEVBackbone
+from opencood.models.fuse_modules.fuse_utils import regroup
+from opencood.models.sub_modules.downsample_conv import DownsampleConv
+from opencood.models.sub_modules.naive_compress import NaiveCompressor
+from opencood.models.fuse_modules.v2xvit_basic import V2XTransformer
+
+
+class PointPillarTransformer(nn.Module):
+    def __init__(self, args):
+        super(PointPillarTransformer, self).__init__()
+
+        self.max_cav = args['max_cav']
+        # PIllar VFE
+        self.pillar_vfe = PillarVFE(args['pillar_vfe'],
+                                    num_point_features=4,
+                                    voxel_size=args['voxel_size'],
+                                    point_cloud_range=args['lidar_range'])
+        self.scatter = PointPillarScatter(args['point_pillar_scatter'])
+        self.backbone = BaseBEVBackbone(args['base_bev_backbone'], 64)
+        # used to downsample the feature map for efficient computation
+        self.shrink_flag = False
+        if 'shrink_header' in args:
+            self.shrink_flag = True
+            self.shrink_conv = DownsampleConv(args['shrink_header'])
+        self.compression = False
+
+        if args['compression'] > 0:
+            self.compression = True
+            self.naive_compressor = NaiveCompressor(256, args['compression'])
+
+        self.fusion_net = V2XTransformer(args['transformer'])
+
+        self.cls_head = nn.Conv2d(128 * 2, args['anchor_number'],
+                                  kernel_size=1)
+        self.reg_head = nn.Conv2d(128 * 2, 7 * args['anchor_number'],
+                                  kernel_size=1)
+
+        if args['backbone_fix']:
+            self.backbone_fix()
+
+    def backbone_fix(self):
+        """
+        Fix the parameters of backbone during finetune on timedelay。
+        """
+        for p in self.pillar_vfe.parameters():
+            p.requires_grad = False
+
+        for p in self.scatter.parameters():
+            p.requires_grad = False
+
+        for p in self.backbone.parameters():
+            p.requires_grad = False
+
+        if self.compression:
+            for p in self.naive_compressor.parameters():
+                p.requires_grad = False
+        if self.shrink_flag:
+            for p in self.shrink_conv.parameters():
+                p.requires_grad = False
+
+        for p in self.cls_head.parameters():
+            p.requires_grad = False
+        for p in self.reg_head.parameters():
+            p.requires_grad = False
+
+    def forward(self, data_dict):
+        voxel_features = data_dict['processed_lidar']['voxel_features']
+        voxel_coords = data_dict['processed_lidar']['voxel_coords']
+        voxel_num_points = data_dict['processed_lidar']['voxel_num_points']
+        record_len = data_dict['record_len']
+        spatial_correction_matrix = data_dict['spatial_correction_matrix']
+
+        # B, max_cav, 3(dt dv infra), 1, 1
+        prior_encoding =\
+            data_dict['prior_encoding'].unsqueeze(-1).unsqueeze(-1)
+
+        batch_dict = {'voxel_features': voxel_features,
+                      'voxel_coords': voxel_coords,
+                      'voxel_num_points': voxel_num_points,
+                      'record_len': record_len}
+        # n, 4 -> n, c
+        batch_dict = self.pillar_vfe(batch_dict)
+        # n, c -> N, C, H, W
+        batch_dict = self.scatter(batch_dict)
+        batch_dict = self.backbone(batch_dict)
+
+        spatial_features_2d = batch_dict['spatial_features_2d']
+        # downsample feature to reduce memory
+        if self.shrink_flag:
+            spatial_features_2d = self.shrink_conv(spatial_features_2d)
+        # compressor
+        if self.compression:
+            spatial_features_2d = self.naive_compressor(spatial_features_2d)
+        # N, C, H, W -> B,  L, C, H, W
+        regroup_feature, mask = regroup(spatial_features_2d,
+                                        record_len,
+                                        self.max_cav)
+        # prior encoding added
+        prior_encoding = prior_encoding.repeat(1, 1, 1,
+                                               regroup_feature.shape[3],
+                                               regroup_feature.shape[4])
+        regroup_feature = torch.cat([regroup_feature, prior_encoding], dim=2)
+
+        # b l c h w -> b l h w c
+        regroup_feature = regroup_feature.permute(0, 1, 3, 4, 2)
+        # transformer fusion
+        fused_feature = self.fusion_net(regroup_feature, mask, spatial_correction_matrix)
+        # b h w c -> b c h w
+        fused_feature = fused_feature.permute(0, 3, 1, 2)
+
+        psm = self.cls_head(fused_feature)
+        rm = self.reg_head(fused_feature)
+
+        output_dict = {'psm': psm,
+                       'rm': rm}
+
+        return output_dict
diff --git a/opencood/opencood/models/point_pillar_v2vnet.py b/opencood/opencood/models/point_pillar_v2vnet.py
new file mode 100644
index 00000000..a91c7059
--- /dev/null
+++ b/opencood/opencood/models/point_pillar_v2vnet.py
@@ -0,0 +1,109 @@
+# -*- coding: utf-8 -*-
+# Author: Hao Xiang <haxiang@g.ucla.edu>, Runsheng Xu <rxx3386@ucla.edu>
+# License: TDG-Attribution-NonCommercial-NoDistrib
+
+
+import torch.nn as nn
+
+from opencood.models.sub_modules.pillar_vfe import PillarVFE
+from opencood.models.sub_modules.point_pillar_scatter import PointPillarScatter
+from opencood.models.sub_modules.base_bev_backbone import BaseBEVBackbone
+from opencood.models.sub_modules.downsample_conv import DownsampleConv
+from opencood.models.sub_modules.naive_compress import NaiveCompressor
+from opencood.models.fuse_modules.v2v_fuse import V2VNetFusion
+
+
+class PointPillarV2VNet(nn.Module):
+    def __init__(self, args):
+        super(PointPillarV2VNet, self).__init__()
+
+        self.max_cav = args['max_cav']
+        # PIllar VFE
+        self.pillar_vfe = PillarVFE(args['pillar_vfe'],
+                                    num_point_features=4,
+                                    voxel_size=args['voxel_size'],
+                                    point_cloud_range=args['lidar_range'])
+        self.scatter = PointPillarScatter(args['point_pillar_scatter'])
+        self.backbone = BaseBEVBackbone(args['base_bev_backbone'], 64)
+        
+        # used to downsample the feature map for efficient computation
+        self.shrink_flag = False
+        if 'shrink_header' in args:
+            self.shrink_flag = True
+            self.shrink_conv = DownsampleConv(args['shrink_header'])
+        self.compression = False
+
+        if args['compression'] > 0:
+            self.compression = True
+            self.naive_compressor = NaiveCompressor(256, args['compression'])
+
+        self.fusion_net = V2VNetFusion(args['v2vfusion'])
+
+        self.cls_head = nn.Conv2d(128 * 2, args['anchor_number'],
+                                  kernel_size=1)
+        self.reg_head = nn.Conv2d(128 * 2, 7 * args['anchor_number'],
+                                  kernel_size=1)
+        if args['backbone_fix']:
+            self.backbone_fix()
+
+    def backbone_fix(self):
+        """
+        Fix the parameters of backbone during finetune on timedelay。
+        """
+        for p in self.pillar_vfe.parameters():
+            p.requires_grad = False
+
+        for p in self.scatter.parameters():
+            p.requires_grad = False
+
+        for p in self.backbone.parameters():
+            p.requires_grad = False
+
+        if self.compression:
+            for p in self.naive_compressor.parameters():
+                p.requires_grad = False
+        if self.shrink_flag:
+            for p in self.shrink_conv.parameters():
+                p.requires_grad = False
+
+        for p in self.cls_head.parameters():
+            p.requires_grad = False
+        for p in self.reg_head.parameters():
+            p.requires_grad = False
+
+    def forward(self, data_dict):
+        voxel_features = data_dict['processed_lidar']['voxel_features']
+        voxel_coords = data_dict['processed_lidar']['voxel_coords']
+        voxel_num_points = data_dict['processed_lidar']['voxel_num_points']
+        record_len = data_dict['record_len']
+
+        pairwise_t_matrix = data_dict['pairwise_t_matrix']
+
+        batch_dict = {'voxel_features': voxel_features,
+                      'voxel_coords': voxel_coords,
+                      'voxel_num_points': voxel_num_points,
+                      'record_len': record_len}
+        # n, 4 -> n, c
+        batch_dict = self.pillar_vfe(batch_dict)
+        # n, c -> N, C, H, W
+        batch_dict = self.scatter(batch_dict)
+        batch_dict = self.backbone(batch_dict)
+
+        spatial_features_2d = batch_dict['spatial_features_2d']
+        # downsample feature to reduce memory
+        if self.shrink_flag:
+            spatial_features_2d = self.shrink_conv(spatial_features_2d)
+        # compressor
+        if self.compression:
+            spatial_features_2d = self.naive_compressor(spatial_features_2d)
+        fused_feature = self.fusion_net(spatial_features_2d,
+                                        record_len,
+                                        pairwise_t_matrix)
+
+        psm = self.cls_head(fused_feature)
+        rm = self.reg_head(fused_feature)
+
+        output_dict = {'psm': psm,
+                       'rm': rm}
+
+        return output_dict
diff --git a/opencood/opencood/models/point_pillar_where2comm.py b/opencood/opencood/models/point_pillar_where2comm.py
new file mode 100644
index 00000000..b86a94e9
--- /dev/null
+++ b/opencood/opencood/models/point_pillar_where2comm.py
@@ -0,0 +1,120 @@
+import torch.nn as nn
+
+from opencood.models.sub_modules.base_bev_backbone import BaseBEVBackbone
+from opencood.models.fuse_modules.where2comm_fuse import Where2comm
+from opencood.models.sub_modules.downsample_conv import DownsampleConv
+from opencood.models.sub_modules.naive_compress import NaiveCompressor
+from opencood.models.sub_modules.pillar_vfe import PillarVFE
+from opencood.models.sub_modules.point_pillar_scatter import PointPillarScatter
+
+
+class PointPillarWhere2comm(nn.Module):
+    def __init__(self, args):
+        super(PointPillarWhere2comm, self).__init__()
+        self.max_cav = args['max_cav']
+        # Pillar VFE
+        self.pillar_vfe = PillarVFE(args['pillar_vfe'],
+                                    num_point_features=4,
+                                    voxel_size=args['voxel_size'],
+                                    point_cloud_range=args['lidar_range'])
+        self.scatter = PointPillarScatter(args['point_pillar_scatter'])
+        self.backbone = BaseBEVBackbone(args['base_bev_backbone'], 64)
+
+        # Used to down-sample the feature map for efficient computation
+        if 'shrink_header' in args:
+            self.shrink_flag = True
+            self.shrink_conv = DownsampleConv(args['shrink_header'])
+        else:
+            self.shrink_flag = False
+
+        if args['compression']:
+            self.compression = True
+            self.naive_compressor = NaiveCompressor(256, args['compression'])
+        else:
+            self.compression = False
+
+        self.fusion_net = Where2comm(args['where2comm_fusion'])
+        self.multi_scale = args['where2comm_fusion']['multi_scale']
+
+        self.cls_head = nn.Conv2d(args['head_dim'], args['anchor_number'], kernel_size=1)
+        self.reg_head = nn.Conv2d(args['head_dim'], 7 * args['anchor_number'], kernel_size=1)
+
+        if args['backbone_fix']:
+            self.backbone_fix()
+
+    def backbone_fix(self):
+        """
+        Fix the parameters of backbone during finetune on timedelay.
+        """
+
+        for p in self.pillar_vfe.parameters():
+            p.requires_grad = False
+
+        for p in self.scatter.parameters():
+            p.requires_grad = False
+
+        for p in self.backbone.parameters():
+            p.requires_grad = False
+
+        if self.compression:
+            for p in self.naive_compressor.parameters():
+                p.requires_grad = False
+        if self.shrink_flag:
+            for p in self.shrink_conv.parameters():
+                p.requires_grad = False
+
+        for p in self.cls_head.parameters():
+            p.requires_grad = False
+        for p in self.reg_head.parameters():
+            p.requires_grad = False
+
+    def forward(self, data_dict):
+        voxel_features = data_dict['processed_lidar']['voxel_features']
+        voxel_coords = data_dict['processed_lidar']['voxel_coords']
+        voxel_num_points = data_dict['processed_lidar']['voxel_num_points']
+        record_len = data_dict['record_len']
+        pairwise_t_matrix = data_dict['pairwise_t_matrix']
+
+        batch_dict = {'voxel_features': voxel_features,
+                      'voxel_coords': voxel_coords,
+                      'voxel_num_points': voxel_num_points,
+                      'record_len': record_len}
+        # n, 4 -> n, c
+        batch_dict = self.pillar_vfe(batch_dict)
+        # n, c -> N, C, H, W
+        batch_dict = self.scatter(batch_dict)
+        batch_dict = self.backbone(batch_dict)
+
+        # N, C, H', W': [N, 256, 48, 176]
+        spatial_features_2d = batch_dict['spatial_features_2d']
+        # Down-sample feature to reduce memory
+        if self.shrink_flag:
+            spatial_features_2d = self.shrink_conv(spatial_features_2d)
+
+        psm_single = self.cls_head(spatial_features_2d)
+
+        # Compressor
+        if self.compression:
+            # The ego feature is also compressed
+            spatial_features_2d = self.naive_compressor(spatial_features_2d)
+
+        if self.multi_scale:
+            # Bypass communication cost, communicate at high resolution, neither shrink nor compress
+            fused_feature, communication_rates = self.fusion_net(batch_dict['spatial_features'],
+                                                                 psm_single,
+                                                                 record_len,
+                                                                 pairwise_t_matrix,
+                                                                 self.backbone)
+            if self.shrink_flag:
+                fused_feature = self.shrink_conv(fused_feature)
+        else:
+            fused_feature, communication_rates = self.fusion_net(spatial_features_2d,
+                                                                 psm_single,
+                                                                 record_len,
+                                                                 pairwise_t_matrix)
+
+        psm = self.cls_head(fused_feature)
+        rm = self.reg_head(fused_feature)
+
+        output_dict = {'psm': psm, 'rm': rm, 'com': communication_rates}
+        return output_dict
diff --git a/opencood/opencood/models/second.py b/opencood/opencood/models/second.py
new file mode 100644
index 00000000..abfe3929
--- /dev/null
+++ b/opencood/opencood/models/second.py
@@ -0,0 +1,59 @@
+# -*- coding: utf-8 -*-
+# Author: Runsheng Xu <rxx3386@ucla.edu>, OpenPCDet
+# License: TDG-Attribution-NonCommercial-NoDistrib
+
+
+import torch.nn as nn
+
+from opencood.models.sub_modules.mean_vfe import MeanVFE
+from opencood.models.sub_modules.sparse_backbone_3d import VoxelBackBone8x
+from opencood.models.sub_modules.height_compression import HeightCompression
+from opencood.models.sub_modules.base_bev_backbone import BaseBEVBackbone
+
+
+class Second(nn.Module):
+    def __init__(self, args):
+        super(Second, self).__init__()
+
+        self.batch_size = args['batch_size']
+        # mean_vfe
+        self.mean_vfe = MeanVFE(args['mean_vfe'], 4)
+        # sparse 3d backbone
+        self.backbone_3d = VoxelBackBone8x(args['backbone_3d'],
+                                           4, args['grid_size'])
+        # height compression
+        self.height_compression = HeightCompression(args['height_compression'])
+        # base ben backbone
+        self.backbone_2d = BaseBEVBackbone(args['base_bev_backbone'], 256)
+
+        # head
+        self.cls_head = nn.Conv2d(256 * 2, args['anchor_number'],
+                                  kernel_size=1)
+        self.reg_head = nn.Conv2d(256 * 2, 7 * args['anchor_num'],
+                                  kernel_size=1)
+
+    def forward(self, data_dict):
+
+        voxel_features = data_dict['processed_lidar']['voxel_features']
+        voxel_coords = data_dict['processed_lidar']['voxel_coords']
+        voxel_num_points = data_dict['processed_lidar']['voxel_num_points']
+
+        batch_dict = {'voxel_features': voxel_features,
+                      'voxel_coords': voxel_coords,
+                      'voxel_num_points': voxel_num_points,
+                      'batch_size': self.batch_size}
+
+        batch_dict = self.mean_vfe(batch_dict)
+        batch_dict = self.backbone_3d(batch_dict)
+        batch_dict = self.height_compression(batch_dict)
+        batch_dict = self.backbone_2d(batch_dict)
+
+        spatial_features_2d = batch_dict['spatial_features_2d']
+
+        psm = self.cls_head(spatial_features_2d)
+        rm = self.reg_head(spatial_features_2d)
+
+        output_dict = {'psm': psm,
+                       'rm': rm}
+
+        return output_dict
\ No newline at end of file
diff --git a/opencood/opencood/models/second_intermediate.py b/opencood/opencood/models/second_intermediate.py
new file mode 100644
index 00000000..c86f9734
--- /dev/null
+++ b/opencood/opencood/models/second_intermediate.py
@@ -0,0 +1,62 @@
+# -*- coding: utf-8 -*-
+# Author: Runsheng Xu <rxx3386@ucla.edu>
+# License: TDG-Attribution-NonCommercial-NoDistrib
+
+
+import torch
+import torch.nn as nn
+
+from opencood.models.sub_modules.mean_vfe import MeanVFE
+from opencood.models.sub_modules.sparse_backbone_3d import VoxelBackBone8x
+from opencood.models.sub_modules.height_compression import HeightCompression
+from opencood.models.sub_modules.att_bev_backbone  import AttBEVBackbone
+
+
+class SecondIntermediate(nn.Module):
+    def __init__(self, args):
+        super(SecondIntermediate, self).__init__()
+
+        self.batch_size = args['batch_size']
+        # mean_vfe
+        self.mean_vfe = MeanVFE(args['mean_vfe'], 4)
+        # sparse 3d backbone
+        self.backbone_3d = VoxelBackBone8x(args['backbone_3d'],
+                                           4, args['grid_size'])
+        # height compression
+        self.height_compression = HeightCompression(args['height_compression'])
+        # base ben backbone
+        self.backbone_2d = AttBEVBackbone(args['base_bev_backbone'], 256)
+
+        # head
+        self.cls_head = nn.Conv2d(256 * 2, args['anchor_number'],
+                                  kernel_size=1)
+        self.reg_head = nn.Conv2d(256 * 2, 7 * args['anchor_num'],
+                                  kernel_size=1)
+
+    def forward(self, data_dict):
+
+        voxel_features = data_dict['processed_lidar']['voxel_features']
+        voxel_coords = data_dict['processed_lidar']['voxel_coords']
+        voxel_num_points = data_dict['processed_lidar']['voxel_num_points']
+        record_len = data_dict['record_len']
+
+        batch_dict = {'voxel_features': voxel_features,
+                      'voxel_coords': voxel_coords,
+                      'voxel_num_points': voxel_num_points,
+                      'batch_size': torch.sum(record_len).cpu().numpy(),
+                      'record_len': record_len}
+
+        batch_dict = self.mean_vfe(batch_dict)
+        batch_dict = self.backbone_3d(batch_dict)
+        batch_dict = self.height_compression(batch_dict)
+        batch_dict = self.backbone_2d(batch_dict)
+
+        spatial_features_2d = batch_dict['spatial_features_2d']
+
+        psm = self.cls_head(spatial_features_2d)
+        rm = self.reg_head(spatial_features_2d)
+
+        output_dict = {'psm': psm,
+                       'rm': rm}
+
+        return output_dict
\ No newline at end of file
diff --git a/opencood/opencood/models/sub_modules/att_bev_backbone.py b/opencood/opencood/models/sub_modules/att_bev_backbone.py
new file mode 100644
index 00000000..4b9d7fda
--- /dev/null
+++ b/opencood/opencood/models/sub_modules/att_bev_backbone.py
@@ -0,0 +1,145 @@
+import numpy as np
+import torch
+import torch.nn as nn
+
+from opencood.models.fuse_modules.self_attn import AttFusion
+from opencood.models.sub_modules.auto_encoder import AutoEncoder
+
+
+class AttBEVBackbone(nn.Module):
+    def __init__(self, model_cfg, input_channels):
+        super().__init__()
+        self.model_cfg = model_cfg
+        self.compress = False
+
+        if 'compression' in model_cfg and model_cfg['compression'] > 0:
+            self.compress = True
+            self.compress_layer = model_cfg['compression']
+
+        if 'layer_nums' in self.model_cfg:
+
+            assert len(self.model_cfg['layer_nums']) == \
+                   len(self.model_cfg['layer_strides']) == \
+                   len(self.model_cfg['num_filters'])
+
+            layer_nums = self.model_cfg['layer_nums']
+            layer_strides = self.model_cfg['layer_strides']
+            num_filters = self.model_cfg['num_filters']
+        else:
+            layer_nums = layer_strides = num_filters = []
+
+        if 'upsample_strides' in self.model_cfg:
+            assert len(self.model_cfg['upsample_strides']) \
+                   == len(self.model_cfg['num_upsample_filter'])
+
+            num_upsample_filters = self.model_cfg['num_upsample_filter']
+            upsample_strides = self.model_cfg['upsample_strides']
+
+        else:
+            upsample_strides = num_upsample_filters = []
+
+        num_levels = len(layer_nums)
+        c_in_list = [input_channels, *num_filters[:-1]]
+
+        self.blocks = nn.ModuleList()
+        self.fuse_modules = nn.ModuleList()
+        self.deblocks = nn.ModuleList()
+
+        if self.compress:
+            self.compression_modules = nn.ModuleList()
+
+        for idx in range(num_levels):
+            cur_layers = [
+                nn.ZeroPad2d(1),
+                nn.Conv2d(
+                    c_in_list[idx], num_filters[idx], kernel_size=3,
+                    stride=layer_strides[idx], padding=0, bias=False
+                ),
+                nn.BatchNorm2d(num_filters[idx], eps=1e-3, momentum=0.01),
+                nn.ReLU()
+            ]
+
+            fuse_network = AttFusion(num_filters[idx])
+            self.fuse_modules.append(fuse_network)
+            if self.compress and self.compress_layer - idx > 0:
+                self.compression_modules.append(AutoEncoder(num_filters[idx],
+                                                            self.compress_layer-idx))
+
+            for k in range(layer_nums[idx]):
+                cur_layers.extend([
+                    nn.Conv2d(num_filters[idx], num_filters[idx],
+                              kernel_size=3, padding=1, bias=False),
+                    nn.BatchNorm2d(num_filters[idx], eps=1e-3, momentum=0.01),
+                    nn.ReLU()
+                ])
+
+            self.blocks.append(nn.Sequential(*cur_layers))
+            if len(upsample_strides) > 0:
+                stride = upsample_strides[idx]
+                if stride >= 1:
+                    self.deblocks.append(nn.Sequential(
+                        nn.ConvTranspose2d(
+                            num_filters[idx], num_upsample_filters[idx],
+                            upsample_strides[idx],
+                            stride=upsample_strides[idx], bias=False
+                        ),
+                        nn.BatchNorm2d(num_upsample_filters[idx],
+                                       eps=1e-3, momentum=0.01),
+                        nn.ReLU()
+                    ))
+                else:
+                    stride = np.round(1 / stride).astype(np.int)
+                    self.deblocks.append(nn.Sequential(
+                        nn.Conv2d(
+                            num_filters[idx], num_upsample_filters[idx],
+                            stride,
+                            stride=stride, bias=False
+                        ),
+                        nn.BatchNorm2d(num_upsample_filters[idx], eps=1e-3,
+                                       momentum=0.01),
+                        nn.ReLU()
+                    ))
+
+        c_in = sum(num_upsample_filters)
+        if len(upsample_strides) > num_levels:
+            self.deblocks.append(nn.Sequential(
+                nn.ConvTranspose2d(c_in, c_in, upsample_strides[-1],
+                                   stride=upsample_strides[-1], bias=False),
+                nn.BatchNorm2d(c_in, eps=1e-3, momentum=0.01),
+                nn.ReLU(),
+            ))
+
+        self.num_bev_features = c_in
+
+    def forward(self, data_dict):
+        spatial_features = data_dict['spatial_features']
+        record_len = data_dict['record_len']
+
+        ups = []
+        ret_dict = {}
+        x = spatial_features
+
+        for i in range(len(self.blocks)):
+            x = self.blocks[i](x)
+            if self.compress and i < len(self.compression_modules):
+                x = self.compression_modules[i](x)
+            x_fuse = self.fuse_modules[i](x, record_len)
+
+            stride = int(spatial_features.shape[2] / x.shape[2])
+            ret_dict['spatial_features_%dx' % stride] = x
+
+            if len(self.deblocks) > 0:
+                ups.append(self.deblocks[i](x_fuse))
+            else:
+                ups.append(x_fuse)
+
+        if len(ups) > 1:
+            x = torch.cat(ups, dim=1)
+        elif len(ups) == 1:
+            x = ups[0]
+
+        if len(self.deblocks) > len(self.blocks):
+            x = self.deblocks[-1](x)
+
+        data_dict['spatial_features_2d'] = x
+        return data_dict
diff --git a/opencood/opencood/models/sub_modules/auto_encoder.py b/opencood/opencood/models/sub_modules/auto_encoder.py
new file mode 100644
index 00000000..a3c548e4
--- /dev/null
+++ b/opencood/opencood/models/sub_modules/auto_encoder.py
@@ -0,0 +1,67 @@
+import torch
+import torch.nn as nn
+
+
+class AutoEncoder(nn.Module):
+    def __init__(self, feature_num, layer_num):
+        super().__init__()
+        self.feature_num = feature_num
+        self.feature_stride = 2
+
+        self.encoder = nn.ModuleList()
+        self.decoder = nn.ModuleList()
+
+        for i in range(layer_num):
+            cur_layers = [
+                nn.ZeroPad2d(1),
+                nn.Conv2d(
+                    feature_num, feature_num, kernel_size=3,
+                    stride=2, padding=0, bias=False
+                ),
+                nn.BatchNorm2d(feature_num, eps=1e-3, momentum=0.01),
+                nn.ReLU()]
+
+            cur_layers.extend([
+                nn.Conv2d(feature_num, feature_num // self.feature_stride,
+                          kernel_size=3, padding=1, bias=False),
+                nn.BatchNorm2d(feature_num // self.feature_stride,
+                               eps=1e-3, momentum=0.01),
+                nn.ReLU()
+            ])
+
+            self.encoder.append(nn.Sequential(*cur_layers))
+            feature_num = feature_num // self.feature_stride
+
+        feature_num = self.feature_num
+        for i in range(layer_num):
+            cur_layers = [nn.Sequential(
+                nn.ConvTranspose2d(
+                    feature_num // 2, feature_num,
+                    kernel_size=2,
+                    stride=2, bias=False
+                ),
+                nn.BatchNorm2d(feature_num,
+                               eps=1e-3, momentum=0.01),
+                nn.ReLU()
+            )]
+
+            cur_layers.extend([nn.Sequential(
+                nn.Conv2d(
+                    feature_num, feature_num, kernel_size=3,
+                    stride=1, bias=False, padding=1
+                ),
+                nn.BatchNorm2d(feature_num, eps=1e-3,
+                               momentum=0.01),
+                nn.ReLU()
+            )])
+            self.decoder.append(nn.Sequential(*cur_layers))
+            feature_num //= 2
+
+    def forward(self, x):
+        for i in range(len(self.encoder)):
+            x = self.encoder[i](x)
+
+        for i in range(len(self.decoder)-1, -1, -1):
+            x = self.decoder[i](x)
+
+        return x
\ No newline at end of file
diff --git a/opencood/opencood/models/sub_modules/base_bev_backbone.py b/opencood/opencood/models/sub_modules/base_bev_backbone.py
new file mode 100644
index 00000000..2db16e39
--- /dev/null
+++ b/opencood/opencood/models/sub_modules/base_bev_backbone.py
@@ -0,0 +1,122 @@
+import numpy as np
+import torch
+import torch.nn as nn
+
+
+class BaseBEVBackbone(nn.Module):
+    def __init__(self, model_cfg, input_channels):
+        super().__init__()
+        self.model_cfg = model_cfg
+
+        if 'layer_nums' in self.model_cfg:
+
+            assert len(self.model_cfg['layer_nums']) == \
+                   len(self.model_cfg['layer_strides']) == \
+                   len(self.model_cfg['num_filters'])
+
+            layer_nums = self.model_cfg['layer_nums']
+            layer_strides = self.model_cfg['layer_strides']
+            num_filters = self.model_cfg['num_filters']
+        else:
+            layer_nums = layer_strides = num_filters = []
+
+        if 'upsample_strides' in self.model_cfg:
+            assert len(self.model_cfg['upsample_strides']) \
+                   == len(self.model_cfg['num_upsample_filter'])
+
+            num_upsample_filters = self.model_cfg['num_upsample_filter']
+            upsample_strides = self.model_cfg['upsample_strides']
+
+        else:
+            upsample_strides = num_upsample_filters = []
+
+        num_levels = len(layer_nums)
+        c_in_list = [input_channels, *num_filters[:-1]]
+
+        self.blocks = nn.ModuleList()
+        self.deblocks = nn.ModuleList()
+
+        for idx in range(num_levels):
+            cur_layers = [
+                nn.ZeroPad2d(1),
+                nn.Conv2d(
+                    c_in_list[idx], num_filters[idx], kernel_size=3,
+                    stride=layer_strides[idx], padding=0, bias=False
+                ),
+                nn.BatchNorm2d(num_filters[idx], eps=1e-3, momentum=0.01),
+                nn.ReLU()
+            ]
+            for k in range(layer_nums[idx]):
+                cur_layers.extend([
+                    nn.Conv2d(num_filters[idx], num_filters[idx],
+                              kernel_size=3, padding=1, bias=False),
+                    nn.BatchNorm2d(num_filters[idx], eps=1e-3, momentum=0.01),
+                    nn.ReLU()
+                ])
+
+            self.blocks.append(nn.Sequential(*cur_layers))
+            if len(upsample_strides) > 0:
+                stride = upsample_strides[idx]
+                if stride >= 1:
+                    self.deblocks.append(nn.Sequential(
+                        nn.ConvTranspose2d(
+                            num_filters[idx], num_upsample_filters[idx],
+                            upsample_strides[idx],
+                            stride=upsample_strides[idx], bias=False
+                        ),
+                        nn.BatchNorm2d(num_upsample_filters[idx],
+                                       eps=1e-3, momentum=0.01),
+                        nn.ReLU()
+                    ))
+                else:
+                    stride = np.round(1 / stride).astype(np.int)
+                    self.deblocks.append(nn.Sequential(
+                        nn.Conv2d(
+                            num_filters[idx], num_upsample_filters[idx],
+                            stride,
+                            stride=stride, bias=False
+                        ),
+                        nn.BatchNorm2d(num_upsample_filters[idx], eps=1e-3,
+                                       momentum=0.01),
+                        nn.ReLU()
+                    ))
+
+        c_in = sum(num_upsample_filters)
+        if len(upsample_strides) > num_levels:
+            self.deblocks.append(nn.Sequential(
+                nn.ConvTranspose2d(c_in, c_in, upsample_strides[-1],
+                                   stride=upsample_strides[-1], bias=False),
+                nn.BatchNorm2d(c_in, eps=1e-3, momentum=0.01),
+                nn.ReLU(),
+            ))
+
+        self.num_bev_features = c_in
+
+    def forward(self, data_dict):
+        spatial_features = data_dict['spatial_features']
+
+        ups = []
+        ret_dict = {}
+        x = spatial_features
+
+        for i in range(len(self.blocks)):
+            x = self.blocks[i](x)
+
+            stride = int(spatial_features.shape[2] / x.shape[2])
+            ret_dict['spatial_features_%dx' % stride] = x
+
+            if len(self.deblocks) > 0:
+                ups.append(self.deblocks[i](x))
+            else:
+                ups.append(x)
+
+        if len(ups) > 1:
+            x = torch.cat(ups, dim=1)
+        elif len(ups) == 1:
+            x = ups[0]
+
+        if len(self.deblocks) > len(self.blocks):
+            x = self.deblocks[-1](x)
+
+        data_dict['spatial_features_2d'] = x
+        return data_dict
diff --git a/opencood/opencood/models/sub_modules/base_transformer.py b/opencood/opencood/models/sub_modules/base_transformer.py
new file mode 100644
index 00000000..bc9fddaf
--- /dev/null
+++ b/opencood/opencood/models/sub_modules/base_transformer.py
@@ -0,0 +1,134 @@
+import torch
+from torch import nn
+
+from einops import rearrange
+
+
+class PreNormResidual(nn.Module):
+    def __init__(self, dim, fn):
+        super().__init__()
+        self.norm = nn.LayerNorm(dim)
+        self.fn = fn
+
+    def forward(self, x, **kwargs):
+        return self.fn(self.norm(x), **kwargs) + x
+
+
+class PreNorm(nn.Module):
+    def __init__(self, dim, fn):
+        super().__init__()
+        self.norm = nn.LayerNorm(dim)
+        self.fn = fn
+
+    def forward(self, x, **kwargs):
+        return self.fn(self.norm(x), **kwargs)
+
+
+class FeedForward(nn.Module):
+    def __init__(self, dim, hidden_dim, dropout=0.):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.Linear(dim, hidden_dim),
+            nn.GELU(),
+            nn.Dropout(dropout),
+            nn.Linear(hidden_dim, dim),
+            nn.Dropout(dropout)
+        )
+
+    def forward(self, x):
+        return self.net(x)
+
+
+class CavAttention(nn.Module):
+    """
+    Vanilla CAV attention.
+    """
+    def __init__(self, dim, heads, dim_head=64, dropout=0.1):
+        super().__init__()
+        inner_dim = heads * dim_head
+
+        self.heads = heads
+        self.scale = dim_head ** -0.5
+
+        self.attend = nn.Softmax(dim=-1)
+        self.to_qkv = nn.Linear(dim, inner_dim * 3, bias=False)
+
+        self.to_out = nn.Sequential(
+            nn.Linear(inner_dim, dim),
+            nn.Dropout(dropout)
+        )
+
+    def forward(self, x, mask, prior_encoding):
+        # x: (B, L, H, W, C) -> (B, H, W, L, C)
+        # mask: (B, L)
+        x = x.permute(0, 2, 3, 1, 4)
+        # mask: (B, 1, H, W, L, 1)
+        mask = mask.unsqueeze(1)
+
+        # qkv: [(B, H, W, L, C_inner) *3]
+        qkv = self.to_qkv(x).chunk(3, dim=-1)
+        # q: (B, M, H, W, L, C)
+        q, k, v = map(lambda t: rearrange(t, 'b h w l (m c) -> b m h w l c',
+                                          m=self.heads), qkv)
+
+        # attention, (B, M, H, W, L, L)
+        att_map = torch.einsum('b m h w i c, b m h w j c -> b m h w i j',
+                               q, k) * self.scale
+        # add mask
+        att_map = att_map.masked_fill(mask == 0, -float('inf'))
+        # softmax
+        att_map = self.attend(att_map)
+
+        # out:(B, M, H, W, L, C_head)
+        out = torch.einsum('b m h w i j, b m h w j c -> b m h w i c', att_map,
+                           v)
+        out = rearrange(out, 'b m h w l c -> b h w l (m c)',
+                        m=self.heads)
+        out = self.to_out(out)
+        # (B L H W C)
+        out = out.permute(0, 3, 1, 2, 4)
+        return out
+
+
+class BaseEncoder(nn.Module):
+    def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout=0.):
+        super().__init__()
+        self.layers = nn.ModuleList([])
+        for _ in range(depth):
+            self.layers.append(nn.ModuleList([
+                PreNorm(dim, CavAttention(dim,
+                                          heads=heads,
+                                          dim_head=dim_head,
+                                          dropout=dropout)),
+                PreNorm(dim, FeedForward(dim, mlp_dim, dropout=dropout))
+            ]))
+
+    def forward(self, x, mask):
+        for attn, ff in self.layers:
+            x = attn(x, mask=mask) + x
+            x = ff(x) + x
+        return x
+
+
+class BaseTransformer(nn.Module):
+    def __init__(self, args):
+        super().__init__()
+
+        dim = args['dim']
+        depth = args['depth']
+        heads = args['heads']
+        dim_head = args['dim_head']
+        mlp_dim = args['mlp_dim']
+        dropout = args['dropout']
+        max_cav = args['max_cav']
+
+        self.encoder = BaseEncoder(dim, depth, heads, dim_head, mlp_dim,
+                                   dropout)
+
+    def forward(self, x, mask):
+        # B, L, H, W, C
+        output = self.encoder(x, mask)
+        # B, H, W, C
+        output = output[:, 0]
+
+        return output
\ No newline at end of file
diff --git a/opencood/opencood/models/sub_modules/cia_ssd_utils.py b/opencood/opencood/models/sub_modules/cia_ssd_utils.py
new file mode 100644
index 00000000..697901cc
--- /dev/null
+++ b/opencood/opencood/models/sub_modules/cia_ssd_utils.py
@@ -0,0 +1,100 @@
+import torch
+from torch import nn
+
+
+
+class SSFA(nn.Module):
+    def __init__(self, args):
+        super(SSFA, self).__init__()
+        self._num_input_features = args['feature_num']  # 128
+
+        seq = [nn.ZeroPad2d(1)] + get_conv_layers('Conv2d', 128, 128, n_layers=3, kernel_size=[3, 3, 3],
+                                                  stride=[1, 1, 1], padding=[0, 1, 1], sequential=False)
+        self.bottom_up_block_0 = nn.Sequential(*seq)
+        self.bottom_up_block_1 = get_conv_layers('Conv2d', 128, 256, n_layers=3, kernel_size=[3, 3, 3],
+                                                  stride=[2, 1, 1], padding=[1, 1, 1])
+
+        self.trans_0 = get_conv_layers('Conv2d', 128, 128, n_layers=1, kernel_size=[1], stride=[1], padding=[0])
+        self.trans_1 = get_conv_layers('Conv2d', 256, 256, n_layers=1, kernel_size=[1], stride=[1], padding=[0])
+
+        self.deconv_block_0 = get_conv_layers('ConvTranspose2d', 256, 128, n_layers=1, kernel_size=[3], stride=[2],
+                                              padding=[1], output_padding=[1])
+        self.deconv_block_1 = get_conv_layers('ConvTranspose2d', 256, 128, n_layers=1, kernel_size=[3], stride=[2],
+                                              padding=[1], output_padding=[1])
+
+        self.conv_0 = get_conv_layers('Conv2d', 128, 128, n_layers=1, kernel_size=[3], stride=[1], padding=[1])
+        self.conv_1 = get_conv_layers('Conv2d', 128, 128, n_layers=1, kernel_size=[3], stride=[1], padding=[1])
+
+        self.w_0 = get_conv_layers('Conv2d', 128, 1, n_layers=1, kernel_size=[1], stride=[1], padding=[0], relu_last=False)
+        self.w_1 = get_conv_layers('Conv2d', 128, 1, n_layers=1, kernel_size=[1], stride=[1], padding=[0], relu_last=False)
+
+    # default init_weights for conv(msra) and norm in ConvModule
+    def init_weights(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.xavier_normal_(m.weight, gain=1)
+                if hasattr(m, "bias") and m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+
+    def forward(self, x):
+        x_0 = self.bottom_up_block_0(x)
+        x_1 = self.bottom_up_block_1(x_0)
+        x_trans_0 = self.trans_0(x_0)
+        x_trans_1 = self.trans_1(x_1)
+        x_middle_0 = self.deconv_block_0(x_trans_1) + x_trans_0
+        x_middle_1 = self.deconv_block_1(x_trans_1)
+        x_output_0 = self.conv_0(x_middle_0)
+        x_output_1 = self.conv_1(x_middle_1)
+
+        x_weight_0 = self.w_0(x_output_0)
+        x_weight_1 = self.w_1(x_output_1)
+        x_weight = torch.softmax(torch.cat([x_weight_0, x_weight_1], dim=1), dim=1)
+        x_output = x_output_0 * x_weight[:, 0:1, :, :] + x_output_1 * x_weight[:, 1:, :, :]
+
+        return x_output.contiguous()
+
+
+def get_conv_layers(conv_name, in_channels, out_channels, n_layers, kernel_size, stride,
+                    padding, relu_last=True, sequential=True, **kwargs):
+    """
+    Build convolutional layers. kernel_size, stride and padding should be a list with the lengths that match n_layers
+    """
+    seq = []
+    for i in range(n_layers):
+        seq.extend([getattr(nn, conv_name)(in_channels, out_channels, kernel_size[i], stride=stride[i],
+                                           padding=padding[i], bias=False, **{k: v[i] for k, v in kwargs.items()}),
+                    nn.BatchNorm2d(out_channels, eps=1e-3, momentum=0.01)])
+        if i < n_layers - 1 or relu_last:
+            seq.append(nn.ReLU())
+        in_channels = out_channels
+    if sequential:
+        return nn.Sequential(*seq)
+    else:
+        return seq
+
+
+class Head(nn.Module):
+    def __init__(self, num_input, num_pred, num_cls, num_iou=2, use_dir=False, num_dir=1):
+        super(Head, self).__init__()
+        self.use_dir = use_dir
+
+        self.conv_box = nn.Conv2d(num_input, num_pred, 1)  # 128 -> 14
+        self.conv_cls = nn.Conv2d(num_input, num_cls, 1)   # 128 -> 2
+        self.conv_iou = nn.Conv2d(num_input, num_iou, 1, bias=False)
+
+        if self.use_dir:
+            self.conv_dir = nn.Conv2d(num_input, num_dir, 1)  # 128 -> 4
+
+    def forward(self, x):
+        box_preds = self.conv_box(x)
+        cls_preds = self.conv_cls(x)
+        ret_dict = {"box_preds": box_preds, "cls_preds": cls_preds}
+        if self.use_dir:
+            dir_preds = self.conv_dir(x)  # dir_preds.shape=[8, w, h, 4]
+            ret_dict["dir_cls_preds"] = dir_preds
+        else:
+            ret_dict["dir_cls_preds"] = torch.zeros((len(box_preds), 1, 2))
+
+        ret_dict["iou_preds"] = self.conv_iou(x)
+
+        return ret_dict
\ No newline at end of file
diff --git a/opencood/opencood/models/sub_modules/convgru.py b/opencood/opencood/models/sub_modules/convgru.py
new file mode 100644
index 00000000..a489157a
--- /dev/null
+++ b/opencood/opencood/models/sub_modules/convgru.py
@@ -0,0 +1,197 @@
+import os
+import torch
+from torch import nn
+from torch.autograd import Variable
+
+
+class ConvGRUCell(nn.Module):
+    def __init__(self, input_size, input_dim, hidden_dim, kernel_size, bias):
+        """
+        Initialize the ConvLSTM cell
+        :param input_size: (int, int)
+            Height and width of input tensor as (height, width).
+        :param input_dim: int
+            Number of channels of input tensor.
+        :param hidden_dim: int
+            Number of channels of hidden state.
+        :param kernel_size: (int, int)
+            Size of the convolutional kernel.
+        :param bias: bool
+            Whether or not to add the bias.
+        :param dtype: torch.cuda.FloatTensor or torch.FloatTensor
+            Whether or not to use cuda.
+        """
+        super(ConvGRUCell, self).__init__()
+        self.height, self.width = input_size
+        self.padding = kernel_size[0] // 2, kernel_size[1] // 2
+        self.hidden_dim = hidden_dim
+        self.bias = bias
+
+        self.conv_gates = nn.Conv2d(in_channels=input_dim + hidden_dim,
+                                    out_channels=2 * self.hidden_dim,
+                                    # for update_gate,reset_gate respectively
+                                    kernel_size=kernel_size,
+                                    padding=self.padding,
+                                    bias=self.bias)
+
+        self.conv_can = nn.Conv2d(in_channels=input_dim + hidden_dim,
+                                  out_channels=self.hidden_dim,
+                                  # for candidate neural memory
+                                  kernel_size=kernel_size,
+                                  padding=self.padding,
+                                  bias=self.bias)
+
+    def init_hidden(self, batch_size):
+        return (Variable(
+            torch.zeros(batch_size, self.hidden_dim, self.height, self.width)))
+
+    def forward(self, input_tensor, h_cur):
+        """
+        :param self:
+        :param input_tensor: (b, c, h, w)
+            input is actually the target_model
+        :param h_cur: (b, c_hidden, h, w)
+            current hidden and cell states respectively
+        :return: h_next,
+            next hidden state
+        """
+        combined = torch.cat([input_tensor, h_cur], dim=1)
+        combined_conv = self.conv_gates(combined)
+
+        gamma, beta = torch.split(combined_conv, self.hidden_dim, dim=1)
+        reset_gate = torch.sigmoid(gamma)
+        update_gate = torch.sigmoid(beta)
+
+        combined = torch.cat([input_tensor, reset_gate * h_cur], dim=1)
+        cc_cnm = self.conv_can(combined)
+        cnm = torch.tanh(cc_cnm)
+
+        h_next = (1 - update_gate) * h_cur + update_gate * cnm
+        return h_next
+
+
+class ConvGRU(nn.Module):
+    def __init__(self, input_size, input_dim, hidden_dim, kernel_size,
+                 num_layers,
+                 batch_first=False, bias=True, return_all_layers=False):
+        """
+        :param input_size: (int, int)
+            Height and width of input tensor as (height, width).
+        :param input_dim: int e.g. 256
+            Number of channels of input tensor.
+        :param hidden_dim: int e.g. 1024
+            Number of channels of hidden state.
+        :param kernel_size: (int, int)
+            Size of the convolutional kernel.
+        :param num_layers: int
+            Number of ConvLSTM layers
+        :param dtype: torch.cuda.FloatTensor or torch.FloatTensor
+            Whether or not to use cuda.
+        :param alexnet_path: str
+            pretrained alexnet parameters
+        :param batch_first: bool
+            if the first position of array is batch or not
+        :param bias: bool
+            Whether or not to add the bias.
+        :param return_all_layers: bool
+            if return hidden and cell states for all layers
+        """
+        super(ConvGRU, self).__init__()
+
+        # Make sure that both `kernel_size` and
+        # `hidden_dim` are lists having len == num_layers
+        kernel_size = self._extend_for_multilayer(kernel_size, num_layers)
+        hidden_dim = self._extend_for_multilayer(hidden_dim, num_layers)
+        if not len(kernel_size) == len(hidden_dim) == num_layers:
+            raise ValueError('Inconsistent list length.')
+
+        self.height, self.width = input_size
+        self.input_dim = input_dim
+        self.hidden_dim = hidden_dim
+        self.kernel_size = kernel_size
+        self.num_layers = num_layers
+        self.batch_first = batch_first
+        self.bias = bias
+        self.return_all_layers = return_all_layers
+
+        cell_list = []
+        for i in range(0, self.num_layers):
+            cur_input_dim = input_dim if i == 0 else hidden_dim[i - 1]
+            cell_list.append(ConvGRUCell(input_size=(self.height, self.width),
+                                         input_dim=cur_input_dim,
+                                         hidden_dim=self.hidden_dim[i],
+                                         kernel_size=self.kernel_size[i],
+                                         bias=self.bias))
+
+        # convert python list to pytorch module
+        self.cell_list = nn.ModuleList(cell_list)
+
+    def forward(self, input_tensor, hidden_state=None):
+        """
+        :param input_tensor: (b, t, c, h, w) or (t,b,c,h,w)
+            depends on if batch first or not extracted features from alexnet
+        :param hidden_state:
+        :return: layer_output_list, last_state_list
+        """
+        if not self.batch_first:
+            # (t, b, c, h, w) -> (b, t, c, h, w)
+            input_tensor = input_tensor.permute(1, 0, 2, 3, 4)
+
+        # Implement stateful ConvLSTM
+        if hidden_state is not None:
+            raise NotImplementedError()
+        else:
+            hidden_state = self._init_hidden(batch_size=input_tensor.size(0),
+                                             device=input_tensor.device,
+                                             dtype=input_tensor.dtype)
+
+        layer_output_list = []
+        last_state_list = []
+
+        seq_len = input_tensor.size(1)
+        cur_layer_input = input_tensor
+
+        for layer_idx in range(self.num_layers):
+            h = hidden_state[layer_idx]
+            output_inner = []
+            for t in range(seq_len):
+                # input current hidden and cell state
+                # then compute the next hidden
+                # and cell state through ConvLSTMCell forward function
+                h = self.cell_list[layer_idx](
+                    input_tensor=cur_layer_input[:, t, :, :, :],  # (b,t,c,h,w)
+                    h_cur=h)
+                output_inner.append(h)
+
+            layer_output = torch.stack(output_inner, dim=1)
+            cur_layer_input = layer_output
+
+            layer_output_list.append(layer_output)
+            last_state_list.append([h])
+
+        if not self.return_all_layers:
+            layer_output_list = layer_output_list[-1:]
+            last_state_list = last_state_list[-1:]
+
+        return layer_output_list, last_state_list
+
+    def _init_hidden(self, batch_size, device=None, dtype=None):
+        init_states = []
+        for i in range(self.num_layers):
+            init_states.append(
+                self.cell_list[i].init_hidden(batch_size).to(device).to(dtype))
+        return init_states
+
+    @staticmethod
+    def _check_kernel_size_consistency(kernel_size):
+        if not (isinstance(kernel_size, tuple) or
+                (isinstance(kernel_size, list) and all(
+                    [isinstance(elem, tuple) for elem in kernel_size]))):
+            raise ValueError('`kernel_size` must be tuple or list of tuples')
+
+    @staticmethod
+    def _extend_for_multilayer(param, num_layers):
+        if not isinstance(param, list):
+            param = [param] * num_layers
+        return param
+
diff --git a/opencood/opencood/models/sub_modules/downsample_conv.py b/opencood/opencood/models/sub_modules/downsample_conv.py
new file mode 100644
index 00000000..ca9550a7
--- /dev/null
+++ b/opencood/opencood/models/sub_modules/downsample_conv.py
@@ -0,0 +1,50 @@
+"""
+Class used to downsample features by 3*3 conv
+"""
+import torch.nn as nn
+
+
+class DoubleConv(nn.Module):
+    """
+    Double convoltuion
+    Args:
+        in_channels: input channel num
+        out_channels: output channel num
+    """
+
+    def __init__(self, in_channels, out_channels, kernel_size,
+                 stride, padding):
+        super().__init__()
+        self.double_conv = nn.Sequential(
+            nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size,
+                      stride=stride, padding=padding),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
+            nn.ReLU(inplace=True)
+        )
+
+    def forward(self, x):
+        return self.double_conv(x)
+
+
+class DownsampleConv(nn.Module):
+    def __init__(self, config):
+        super(DownsampleConv, self).__init__()
+        self.layers = nn.ModuleList([])
+        input_dim = config['input_dim']
+
+        for (ksize, dim, stride, padding) in zip(config['kernal_size'],
+                                                 config['dim'],
+                                                 config['stride'],
+                                                 config['padding']):
+            self.layers.append(DoubleConv(input_dim,
+                                          dim,
+                                          kernel_size=ksize,
+                                          stride=stride,
+                                          padding=padding))
+            input_dim = dim
+
+    def forward(self, x):
+        for i in range(len(self.layers)):
+            x = self.layers[i](x)
+        return x
\ No newline at end of file
diff --git a/opencood/opencood/models/sub_modules/height_compression.py b/opencood/opencood/models/sub_modules/height_compression.py
new file mode 100644
index 00000000..1d7f38ed
--- /dev/null
+++ b/opencood/opencood/models/sub_modules/height_compression.py
@@ -0,0 +1,27 @@
+import torch.nn as nn
+
+
+class HeightCompression(nn.Module):
+    def __init__(self, model_cfg, **kwargs):
+        super().__init__()
+        self.model_cfg = model_cfg
+        self.num_bev_features = self.model_cfg['feature_num']
+
+    def forward(self, batch_dict):
+        """
+        Args:
+            batch_dict:
+                encoded_spconv_tensor: sparse tensor
+        Returns:
+            batch_dict:
+                spatial_features:
+
+        """
+        encoded_spconv_tensor = batch_dict['encoded_spconv_tensor']
+        spatial_features = encoded_spconv_tensor.dense()
+        N, C, D, H, W = spatial_features.shape
+        spatial_features = spatial_features.view(N, C * D, H, W)
+        batch_dict['spatial_features'] = spatial_features
+        batch_dict['spatial_features_stride'] = \
+            batch_dict['encoded_spconv_tensor_stride']
+        return batch_dict
diff --git a/opencood/opencood/models/sub_modules/matcher.py b/opencood/opencood/models/sub_modules/matcher.py
new file mode 100644
index 00000000..d7fe424a
--- /dev/null
+++ b/opencood/opencood/models/sub_modules/matcher.py
@@ -0,0 +1,132 @@
+import torch
+from torch import nn
+
+from opencood.pcdet_utils.iou3d_nms.iou3d_nms_utils import boxes_iou3d_gpu
+
+pi = 3.141592653
+
+
+def limit_period(val, offset=0.5, period=2 * pi):
+    return val - torch.floor(val / period + offset) * period
+
+
+class Matcher(nn.Module):
+    """Correct localization error and use Algorithm 1:
+     BBox matching with scores to fuse the proposal BBoxes"""
+
+    def __init__(self, cfg, pc_range):
+        super(Matcher, self).__init__()
+        self.pc_range = pc_range
+
+    @torch.no_grad()
+    def forward(self, data_dict):
+        clusters, scores = self.clustering(data_dict)
+        data_dict['boxes_fused'], data_dict[
+            'scores_fused'] = self.cluster_fusion(clusters, scores)
+        self.merge_keypoints(data_dict)
+        return data_dict
+
+    def clustering(self, data_dict):
+        """
+        Assign predicted boxes to clusters according to their ious with each other
+        """
+        clusters_batch = []
+        scores_batch = []
+        record_len = [int(l) for l in data_dict['record_len']]
+        for i, l in enumerate(record_len):
+            cur_boxes_list = data_dict['det_boxes'][sum(record_len[:i]):sum(record_len[:i])+l]
+            cur_scores_list = data_dict['det_scores'][sum(record_len[:i]):sum(record_len[:i])+l]
+            cur_boxes_list = [b for b in cur_boxes_list if len(b) > 0]
+            cur_scores_list = [s for s in cur_scores_list if len(s) > 0]
+            if len(cur_scores_list) == 0:
+                clusters_batch.append([torch.Tensor([0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.57]).
+                                      to(torch.device('cuda:0')).view(1, 7)])
+                scores_batch.append([torch.Tensor([0.01]).to(torch.device('cuda:0')).view(-1)])
+                continue
+
+            pred_boxes_cat = torch.cat(cur_boxes_list, dim=0)
+            pred_boxes_cat[:, -1] = limit_period(pred_boxes_cat[:, -1])
+            pred_scores_cat = torch.cat(cur_scores_list, dim=0)
+
+            ious = boxes_iou3d_gpu(pred_boxes_cat, pred_boxes_cat)
+            cluster_indices = torch.zeros(len(ious)).int() # gt assignments of preds
+            cur_cluster_id = 1
+            while torch.any(cluster_indices == 0):
+                cur_idx = torch.where(cluster_indices == 0)[0][0] # find the idx of the first pred which is not assigned yet
+                cluster_indices[torch.where(ious[cur_idx] > 0.1)[0]] = cur_cluster_id
+                cur_cluster_id += 1
+            clusters = []
+            scores = []
+            for j in range(1, cur_cluster_id):
+                clusters.append(pred_boxes_cat[cluster_indices==j])
+                scores.append(pred_scores_cat[cluster_indices==j])
+            clusters_batch.append(clusters)
+            scores_batch.append(scores)
+
+        return clusters_batch, scores_batch
+
+    def cluster_fusion(self, clusters, scores):
+        """
+        Merge boxes in each cluster with scores as weights for merging
+        """
+        boxes_fused = []
+        scores_fused = []
+        for cl, sl in zip(clusters, scores):
+            for c, s in zip(cl, sl):
+                # reverse direction for non-dominant direction of boxes
+                dirs = c[:, -1]
+                max_score_idx = torch.argmax(s)
+                dirs_diff = torch.abs(dirs - dirs[max_score_idx].item())
+                lt_pi = (dirs_diff > pi).int()
+                dirs_diff = dirs_diff * (1 - lt_pi) + (
+                            2 * pi - dirs_diff) * lt_pi
+                score_lt_half_pi = s[dirs_diff > pi / 2].sum()  # larger than
+                score_set_half_pi = s[
+                    dirs_diff <= pi / 2].sum()  # small equal than
+                # select larger scored direction as final direction
+                if score_lt_half_pi <= score_set_half_pi:
+                    dirs[dirs_diff > pi / 2] += pi
+                else:
+                    dirs[dirs_diff <= pi / 2] += pi
+                dirs = limit_period(dirs)
+                s_normalized = s / s.sum()
+                sint = torch.sin(dirs) * s_normalized
+                cost = torch.cos(dirs) * s_normalized
+                theta = torch.atan2(sint.sum(), cost.sum()).view(1, )
+                center_dim = c[:, :-1] * s_normalized[:, None]
+                boxes_fused.append(torch.cat([center_dim.sum(dim=0), theta]))
+                s_sorted = torch.sort(s, descending=True).values
+                s_fused = 0
+                for i, ss in enumerate(s_sorted):
+                    s_fused += ss ** (i + 1)
+                s_fused = torch.tensor([min(s_fused, 1.0)], device=s.device)
+                scores_fused.append(s_fused)
+
+        assert len(boxes_fused) > 0
+        boxes_fused = torch.stack(boxes_fused, dim=0)
+        len_records = [len(c) for c in clusters]
+        boxes_fused = [
+            boxes_fused[sum(len_records[:i]):sum(len_records[:i]) + l] for i, l
+            in enumerate(len_records)]
+        scores_fused = torch.stack(scores_fused, dim=0)
+        scores_fused = [
+            scores_fused[sum(len_records[:i]):sum(len_records[:i]) + l] for
+            i, l in enumerate(len_records)]
+
+        return boxes_fused, scores_fused
+
+    def merge_keypoints(self, data_dict):
+        # merge keypoints
+        kpts_feat_out = []
+        kpts_coor_out = []
+        keypoints_features = data_dict['point_features']
+        keypoints_coords = data_dict['point_coords']
+        idx = 0
+        for l in data_dict['record_len']:
+            kpts_coor_out.append(
+                torch.cat(keypoints_coords[idx:l + idx], dim=0))
+            kpts_feat_out.append(
+                torch.cat(keypoints_features[idx:l + idx], dim=0))
+            idx += l
+        data_dict['point_features'] = kpts_feat_out
+        data_dict['point_coords'] = kpts_coor_out
diff --git a/opencood/opencood/models/sub_modules/mean_vfe.py b/opencood/opencood/models/sub_modules/mean_vfe.py
new file mode 100644
index 00000000..58a1d344
--- /dev/null
+++ b/opencood/opencood/models/sub_modules/mean_vfe.py
@@ -0,0 +1,34 @@
+import torch
+import torch.nn as nn
+
+
+class MeanVFE(nn.Module):
+    def __init__(self, model_cfg, num_point_features, **kwargs):
+        super().__init__()
+        self.model_cfg = model_cfg
+        self.num_point_features = num_point_features
+
+    def get_output_feature_dim(self):
+        return self.num_point_features
+
+    def forward(self, batch_dict, **kwargs):
+        """
+        Args:
+            batch_dict:
+                voxels: (num_voxels, max_points_per_voxel, C)
+                voxel_num_points: optional (num_voxels)
+            **kwargs:
+
+        Returns:
+            vfe_features: (num_voxels, C)
+        """
+        voxel_features, voxel_num_points = batch_dict['voxel_features'], \
+                                           batch_dict['voxel_num_points']
+        points_mean = voxel_features[:, :, :].sum(dim=1, keepdim=False)
+        normalizer = torch.clamp_min(voxel_num_points.view(-1, 1), min=1.0).\
+            type_as(voxel_features)
+        points_mean = points_mean / normalizer
+        batch_dict['voxel_features'] = points_mean.contiguous()
+
+        return batch_dict
+
diff --git a/opencood/opencood/models/sub_modules/naive_compress.py b/opencood/opencood/models/sub_modules/naive_compress.py
new file mode 100644
index 00000000..49842af9
--- /dev/null
+++ b/opencood/opencood/models/sub_modules/naive_compress.py
@@ -0,0 +1,32 @@
+import torch
+import torch.nn as nn
+
+
+class NaiveCompressor(nn.Module):
+    """
+    A very naive compression that only compress on the channel.
+    """
+    def __init__(self, input_dim, compress_raito):
+        super().__init__()
+        self.encoder = nn.Sequential(
+            nn.Conv2d(input_dim, input_dim//compress_raito, kernel_size=3,
+                      stride=1, padding=1),
+            nn.BatchNorm2d(input_dim//compress_raito, eps=1e-3, momentum=0.01),
+            nn.ReLU()
+        )
+        self.decoder = nn.Sequential(
+            nn.Conv2d(input_dim//compress_raito, input_dim, kernel_size=3,
+                      stride=1, padding=1),
+            nn.BatchNorm2d(input_dim, eps=1e-3, momentum=0.01),
+            nn.ReLU(),
+            nn.Conv2d(input_dim, input_dim, kernel_size=3, stride=1, padding=1),
+            nn.BatchNorm2d(input_dim, eps=1e-3,
+                           momentum=0.01),
+            nn.ReLU()
+        )
+
+    def forward(self, x):
+        x = self.encoder(x)
+        x = self.decoder(x)
+
+        return x
\ No newline at end of file
diff --git a/opencood/opencood/models/sub_modules/pillar_vfe.py b/opencood/opencood/models/sub_modules/pillar_vfe.py
new file mode 100644
index 00000000..0a7c6014
--- /dev/null
+++ b/opencood/opencood/models/sub_modules/pillar_vfe.py
@@ -0,0 +1,146 @@
+"""
+Pillar VFE, credits to OpenPCDet.
+"""
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class PFNLayer(nn.Module):
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 use_norm=True,
+                 last_layer=False):
+        super().__init__()
+
+        self.last_vfe = last_layer
+        self.use_norm = use_norm
+        if not self.last_vfe:
+            out_channels = out_channels // 2
+
+        if self.use_norm:
+            self.linear = nn.Linear(in_channels, out_channels, bias=False)
+            self.norm = nn.BatchNorm1d(out_channels, eps=1e-3, momentum=0.01)
+        else:
+            self.linear = nn.Linear(in_channels, out_channels, bias=True)
+
+        self.part = 50000
+
+    def forward(self, inputs):
+        if inputs.shape[0] > self.part:
+            # nn.Linear performs randomly when batch size is too large
+            num_parts = inputs.shape[0] // self.part
+            part_linear_out = [self.linear(
+                inputs[num_part * self.part:(num_part + 1) * self.part])
+                for num_part in range(num_parts + 1)]
+            x = torch.cat(part_linear_out, dim=0)
+        else:
+            x = self.linear(inputs)
+        torch.backends.cudnn.enabled = False
+        x = self.norm(x.permute(0, 2, 1)).permute(0, 2,
+                                                  1) if self.use_norm else x
+        torch.backends.cudnn.enabled = True
+        x = F.relu(x)
+        x_max = torch.max(x, dim=1, keepdim=True)[0]
+
+        if self.last_vfe:
+            return x_max
+        else:
+            x_repeat = x_max.repeat(1, inputs.shape[1], 1)
+            x_concatenated = torch.cat([x, x_repeat], dim=2)
+            return x_concatenated
+
+
+class PillarVFE(nn.Module):
+    def __init__(self, model_cfg, num_point_features, voxel_size,
+                 point_cloud_range):
+        super().__init__()
+        self.model_cfg = model_cfg
+
+        self.use_norm = self.model_cfg['use_norm']
+        self.with_distance = self.model_cfg['with_distance']
+
+        self.use_absolute_xyz = self.model_cfg['use_absolute_xyz']
+        num_point_features += 6 if self.use_absolute_xyz else 3
+        if self.with_distance:
+            num_point_features += 1
+
+        self.num_filters = self.model_cfg['num_filters']
+        assert len(self.num_filters) > 0
+        num_filters = [num_point_features] + list(self.num_filters)
+
+        pfn_layers = []
+        for i in range(len(num_filters) - 1):
+            in_filters = num_filters[i]
+            out_filters = num_filters[i + 1]
+            pfn_layers.append(
+                PFNLayer(in_filters, out_filters, self.use_norm,
+                         last_layer=(i >= len(num_filters) - 2))
+            )
+        self.pfn_layers = nn.ModuleList(pfn_layers)
+
+        self.voxel_x = voxel_size[0]
+        self.voxel_y = voxel_size[1]
+        self.voxel_z = voxel_size[2]
+        self.x_offset = self.voxel_x / 2 + point_cloud_range[0]
+        self.y_offset = self.voxel_y / 2 + point_cloud_range[1]
+        self.z_offset = self.voxel_z / 2 + point_cloud_range[2]
+
+    def get_output_feature_dim(self):
+        return self.num_filters[-1]
+
+    @staticmethod
+    def get_paddings_indicator(actual_num, max_num, axis=0):
+        actual_num = torch.unsqueeze(actual_num, axis + 1)
+        max_num_shape = [1] * len(actual_num.shape)
+        max_num_shape[axis + 1] = -1
+        max_num = torch.arange(max_num,
+                               dtype=torch.int,
+                               device=actual_num.device).view(max_num_shape)
+        paddings_indicator = actual_num.int() > max_num
+        return paddings_indicator
+
+    def forward(self, batch_dict):
+
+        voxel_features, voxel_num_points, coords = \
+            batch_dict['voxel_features'], batch_dict['voxel_num_points'], \
+            batch_dict['voxel_coords']
+        points_mean = \
+            voxel_features[:, :, :3].sum(dim=1, keepdim=True) / \
+            voxel_num_points.type_as(voxel_features).view(-1, 1, 1)
+        f_cluster = voxel_features[:, :, :3] - points_mean
+
+        f_center = torch.zeros_like(voxel_features[:, :, :3])
+        f_center[:, :, 0] = voxel_features[:, :, 0] - (
+                coords[:, 3].to(voxel_features.dtype).unsqueeze(
+                    1) * self.voxel_x + self.x_offset)
+        f_center[:, :, 1] = voxel_features[:, :, 1] - (
+                coords[:, 2].to(voxel_features.dtype).unsqueeze(
+                    1) * self.voxel_y + self.y_offset)
+        f_center[:, :, 2] = voxel_features[:, :, 2] - (
+                coords[:, 1].to(voxel_features.dtype).unsqueeze(
+                    1) * self.voxel_z + self.z_offset)
+
+        if self.use_absolute_xyz:
+            features = [voxel_features, f_cluster, f_center]
+        else:
+            features = [voxel_features[..., 3:], f_cluster, f_center]
+
+        if self.with_distance:
+            points_dist = torch.norm(voxel_features[:, :, :3], 2, 2,
+                                     keepdim=True)
+            features.append(points_dist)
+        features = torch.cat(features, dim=-1)
+
+        voxel_count = features.shape[1]
+        mask = self.get_paddings_indicator(voxel_num_points, voxel_count,
+                                           axis=0)
+        mask = torch.unsqueeze(mask, -1).type_as(voxel_features)
+        features *= mask
+        for pfn in self.pfn_layers:
+            features = pfn(features)
+        features = features.squeeze()
+        batch_dict['pillar_features'] = features
+        return batch_dict
diff --git a/opencood/opencood/models/sub_modules/point_pillar_scatter.py b/opencood/opencood/models/sub_modules/point_pillar_scatter.py
new file mode 100644
index 00000000..166ae283
--- /dev/null
+++ b/opencood/opencood/models/sub_modules/point_pillar_scatter.py
@@ -0,0 +1,48 @@
+import torch
+import torch.nn as nn
+
+
+class PointPillarScatter(nn.Module):
+    def __init__(self, model_cfg):
+        super().__init__()
+
+        self.model_cfg = model_cfg
+        self.num_bev_features = self.model_cfg['num_features']
+        self.nx, self.ny, self.nz = model_cfg['grid_size']
+        assert self.nz == 1
+
+    def forward(self, batch_dict):
+        pillar_features, coords = batch_dict['pillar_features'], batch_dict[
+            'voxel_coords']
+        batch_spatial_features = []
+        batch_size = coords[:, 0].max().int().item() + 1
+
+        for batch_idx in range(batch_size):
+            spatial_feature = torch.zeros(
+                self.num_bev_features,
+                self.nz * self.nx * self.ny,
+                dtype=pillar_features.dtype,
+                device=pillar_features.device)
+
+            batch_mask = coords[:, 0] == batch_idx
+            this_coords = coords[batch_mask, :]
+
+            indices = this_coords[:, 1] + \
+                      this_coords[:, 2] * self.nx + \
+                      this_coords[:, 3]
+            indices = indices.type(torch.long)
+
+            pillars = pillar_features[batch_mask, :]
+            pillars = pillars.t()
+            spatial_feature[:, indices] = pillars
+            batch_spatial_features.append(spatial_feature)
+
+        batch_spatial_features = \
+            torch.stack(batch_spatial_features, 0)
+        batch_spatial_features = \
+            batch_spatial_features.view(batch_size, self.num_bev_features *
+                                        self.nz, self.ny, self.nx)
+        batch_dict['spatial_features'] = batch_spatial_features
+
+        return batch_dict
+
diff --git a/opencood/opencood/models/sub_modules/roi_head.py b/opencood/opencood/models/sub_modules/roi_head.py
new file mode 100644
index 00000000..4b2564af
--- /dev/null
+++ b/opencood/opencood/models/sub_modules/roi_head.py
@@ -0,0 +1,280 @@
+import copy
+
+import torch.nn as nn
+import torch
+import numpy as np
+from opencood.pcdet_utils.pointnet2.pointnet2_stack import \
+    pointnet2_modules as pointnet2_stack_modules
+from opencood.utils import common_utils
+from opencood.pcdet_utils.iou3d_nms.iou3d_nms_utils import boxes_iou3d_gpu
+from opencood.utils import box_utils
+
+
+class RoIHead(nn.Module):
+    def __init__(self, model_cfg):
+        super().__init__()
+        self.model_cfg = model_cfg
+        input_channels = model_cfg['in_channels']
+        self.code_size = 7
+
+        mlps = copy.copy(self.model_cfg['roi_grid_pool']['mlps'])
+        for k in range(len(mlps)):
+            mlps[k] = [input_channels] + mlps[k]
+
+        self.roi_grid_pool_layer = pointnet2_stack_modules.StackSAModuleMSG(
+            radii=self.model_cfg['roi_grid_pool']['pool_radius'],
+            nsamples=self.model_cfg['roi_grid_pool']['n_sample'],
+            mlps=mlps,
+            use_xyz=True,
+            pool_method=self.model_cfg['roi_grid_pool']['pool_method'],
+        )
+
+        grid_size = self.model_cfg['roi_grid_pool']['grid_size']
+        self.grid_size = grid_size
+        c_out = sum([x[-1] for x in mlps])
+        pre_channel = grid_size * grid_size * grid_size * c_out
+        fc_layers = [self.model_cfg['n_fc_neurons']] * 2
+        self.shared_fc_layers, pre_channel = self._make_fc_layers(pre_channel,
+                                                                  fc_layers)
+
+        self.cls_layers, pre_channel = self._make_fc_layers(pre_channel,
+                                                            fc_layers,
+                                                            output_channels=
+                                                            self.model_cfg[
+                                                                'num_cls'])
+        self.iou_layers, _ = self._make_fc_layers(pre_channel, fc_layers,
+                                                  output_channels=
+                                                  self.model_cfg['num_cls'])
+        self.reg_layers, _ = self._make_fc_layers(pre_channel, fc_layers,
+                                                  output_channels=
+                                                  self.model_cfg[
+                                                      'num_cls'] * 7)
+
+        self._init_weights(weight_init='xavier')
+
+    def _init_weights(self, weight_init='xavier'):
+        if weight_init == 'kaiming':
+            init_func = nn.init.kaiming_normal_
+        elif weight_init == 'xavier':
+            init_func = nn.init.xavier_normal_
+        elif weight_init == 'normal':
+            init_func = nn.init.normal_
+        else:
+            raise NotImplementedError
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d) or isinstance(m, nn.Conv1d):
+                if weight_init == 'normal':
+                    init_func(m.weight, mean=0, std=0.001)
+                else:
+                    init_func(m.weight)
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+        nn.init.normal_(self.reg_layers[-1].weight, mean=0, std=0.001)
+
+    def _make_fc_layers(self, input_channels, fc_list, output_channels=None):
+        fc_layers = []
+        pre_channel = input_channels
+        for k in range(len(fc_list)):
+            fc_layers.extend([
+                nn.Conv1d(pre_channel, fc_list[k], kernel_size=1, bias=False),
+                # nn.BatchNorm1d(fc_list[k]),
+                nn.ReLU()
+            ])
+            pre_channel = fc_list[k]
+            if self.model_cfg['dp_ratio'] > 0:
+                fc_layers.append(nn.Dropout(self.model_cfg['dp_ratio']))
+        if output_channels is not None:
+            fc_layers.append(
+                nn.Conv1d(pre_channel, output_channels, kernel_size=1,
+                          bias=True))
+        fc_layers = nn.Sequential(*fc_layers)
+        return fc_layers, pre_channel
+
+    def get_global_grid_points_of_roi(self, rois):
+        rois = rois.view(-1, rois.shape[-1])
+        batch_size_rcnn = rois.shape[0]
+
+        # (B, 6x6x6, 3)
+        local_roi_grid_points = self.get_dense_grid_points(rois,
+                                                           batch_size_rcnn,
+                                                           self.grid_size)
+        global_roi_grid_points = common_utils.rotate_points_along_z(
+            local_roi_grid_points.clone(), rois[:, 6]
+        ).squeeze(dim=1)
+        global_center = rois[:, 0:3].clone()
+        global_roi_grid_points += global_center.unsqueeze(dim=1)
+        return global_roi_grid_points, local_roi_grid_points
+
+    @staticmethod
+    def get_dense_grid_points(rois, batch_size_rcnn, grid_size):
+        """
+        Get the local coordinates of each grid point of a roi in the coordinate
+        system of the roi(origin lies in the center of this roi.
+        """
+        faked_features = rois.new_ones((grid_size, grid_size, grid_size))
+        dense_idx = torch.stack(torch.where(faked_features),
+                                dim=1)  # (N, 3) [x_idx, y_idx, z_idx]
+        dense_idx = dense_idx.repeat(batch_size_rcnn, 1,
+                                     1).float()  # (B, 6x6x6, 3)
+
+        local_roi_size = rois.view(batch_size_rcnn, -1)[:, 3:6]
+        roi_grid_points = (
+                                  dense_idx + 0.5) / grid_size * local_roi_size.unsqueeze(
+            dim=1) \
+                          - (local_roi_size.unsqueeze(
+            dim=1) / 2)  # (B, 6x6x6, 3)
+        return roi_grid_points
+
+    def assign_targets(self, batch_dict):
+        batch_dict['rcnn_label_dict'] = {
+            'rois': [],
+            'gt_of_rois': [],
+            'gt_of_rois_src': [],
+            'cls_tgt': [],
+            'reg_tgt': [],
+            'iou_tgt': [],
+            'rois_anchor': [],
+            'record_len': []
+        }
+        # pred_boxes = [boxes[:, [0, 1, 2, 5, 4, 3, 6]] for boxes in batch_dict['boxes_fused']]
+        pred_boxes = batch_dict['boxes_fused']
+        gt_boxes = [b[m][:, [0, 1, 2, 5, 4, 3, 6]].float() for b, m in
+                    zip(batch_dict['object_bbx_center'],
+                        batch_dict['object_bbx_mask'].bool())]
+        for rois, gts in zip(pred_boxes, gt_boxes):
+            gts[:, -1] *= 1
+            ious = boxes_iou3d_gpu(rois, gts)
+            max_ious, gt_inds = ious.max(dim=1)
+            gt_of_rois = gts[gt_inds]
+            rcnn_labels = (max_ious > 0.3).float()
+            mask = torch.logical_not(rcnn_labels.bool())
+
+            # set negative samples back to rois, no correction in stage2 for them
+            gt_of_rois[mask] = rois[mask]
+            gt_of_rois_src = gt_of_rois.clone().detach()
+
+            # canoical transformation
+            roi_center = rois[:, 0:3]
+            # TODO: roi_ry > 0 in pcdet
+            roi_ry = rois[:, 6] % (2 * np.pi)
+            gt_of_rois[:, 0:3] = gt_of_rois[:, 0:3] - roi_center
+            gt_of_rois[:, 6] = gt_of_rois[:, 6] - roi_ry
+
+            # transfer LiDAR coords to local coords
+            gt_of_rois = common_utils.rotate_points_along_z(
+                points=gt_of_rois.view(-1, 1, gt_of_rois.shape[-1]),
+                angle=-roi_ry.view(-1)
+            ).view(-1, gt_of_rois.shape[-1])
+
+            # flip orientation if rois have opposite orientation
+            heading_label = (gt_of_rois[:, 6] + (
+                    torch.div(torch.abs(gt_of_rois[:, 6].min()),
+                              (2 * np.pi), rounding_mode='trunc')
+                    + 1) * 2 * np.pi) % (2 * np.pi)  # 0 ~ 2pi
+            opposite_flag = (heading_label > np.pi * 0.5) & (
+                    heading_label < np.pi * 1.5)
+
+            # (0 ~ pi/2, 3pi/2 ~ 2pi)
+            heading_label[opposite_flag] = (heading_label[
+                                                opposite_flag] + np.pi) % (
+                                                   2 * np.pi)
+            flag = heading_label > np.pi
+            heading_label[flag] = heading_label[
+                                      flag] - np.pi * 2  # (-pi/2, pi/2)
+            heading_label = torch.clamp(heading_label, min=-np.pi / 2,
+                                        max=np.pi / 2)
+            gt_of_rois[:, 6] = heading_label
+
+            # generate regression target
+            rois_anchor = rois.clone().detach().view(-1, self.code_size)
+            rois_anchor[:, 0:3] = 0
+            rois_anchor[:, 6] = 0
+
+            reg_targets = box_utils.box_encode(
+                gt_of_rois.view(-1, self.code_size), rois_anchor
+            )
+
+            batch_dict['rcnn_label_dict']['rois'].append(rois)
+            batch_dict['rcnn_label_dict']['gt_of_rois'].append(gt_of_rois)
+            batch_dict['rcnn_label_dict']['gt_of_rois_src'].append(
+                gt_of_rois_src)
+            batch_dict['rcnn_label_dict']['cls_tgt'].append(rcnn_labels)
+            batch_dict['rcnn_label_dict']['reg_tgt'].append(reg_targets)
+            batch_dict['rcnn_label_dict']['iou_tgt'].append(max_ious)
+            batch_dict['rcnn_label_dict']['rois_anchor'].append(rois_anchor)
+            batch_dict['rcnn_label_dict']['record_len'].append(rois.shape[0])
+
+        # cat list to tensor
+        for k, v in batch_dict['rcnn_label_dict'].items():
+            if k == 'record_len':
+                continue
+            batch_dict['rcnn_label_dict'][k] = torch.cat(v, dim=0)
+
+        return batch_dict
+
+    def roi_grid_pool(self, batch_dict):
+        batch_size = len(batch_dict['record_len'])
+        rois = batch_dict['rcnn_label_dict']['rois']
+        point_coords = batch_dict['point_coords']
+        point_features = batch_dict['point_features']
+        label_record_len = batch_dict['rcnn_label_dict']['record_len']
+
+        point_features = torch.cat(point_features, dim=0)
+        # (BxN, 6x6x6, 3)
+        global_roi_grid_points, local_roi_grid_points = \
+            self.get_global_grid_points_of_roi(rois)
+        # (B, Nx6x6x6, 3)
+        global_roi_grid_points = global_roi_grid_points.view(batch_size, -1, 3)
+
+        xyz = torch.cat(point_coords, dim=0)
+        xyz_batch_cnt = xyz.new_zeros(batch_size).int()
+        for bs_idx in range(batch_size):
+            xyz_batch_cnt[bs_idx] = len(point_coords[bs_idx])
+        new_xyz = global_roi_grid_points.view(-1, 3)
+        new_xyz_batch_cnt = xyz.new_zeros(batch_size).int()
+        for bs_idx in range(batch_size):
+            new_xyz_batch_cnt[bs_idx] = label_record_len[
+                                            bs_idx] * self.grid_size ** 3
+
+        pooled_points, pooled_features = self.roi_grid_pool_layer(
+            xyz=xyz[:, :3].contiguous(),
+            xyz_batch_cnt=xyz_batch_cnt,
+            new_xyz=new_xyz[:, :3].contiguous(),
+            new_xyz_batch_cnt=new_xyz_batch_cnt,
+            features=point_features.contiguous(),  # weighted point features
+        )  # (M1 + M2 ..., C)
+        # (BxN, 6x6x6, C)
+        pooled_features = pooled_features.view(-1, self.grid_size ** 3,
+                                               pooled_features.shape[-1])
+
+        return pooled_features
+
+    def forward(self, batch_dict):
+        batch_dict = self.assign_targets(batch_dict)
+        # RoI aware pooling
+        pooled_features = self.roi_grid_pool(batch_dict)  # (BxN, 6x6x6, C)
+
+        batch_size_rcnn = pooled_features.shape[0]
+        pooled_features = pooled_features.permute(0, 2, 1). \
+            contiguous().view(batch_size_rcnn, -1, self.grid_size,
+                              self.grid_size,
+                              self.grid_size)  # (BxN, C, 6, 6, 6)
+        shared_features = self.shared_fc_layers(
+            pooled_features.view(batch_size_rcnn, -1, 1))
+        rcnn_cls = self.cls_layers(shared_features).transpose(1,
+                                                              2).contiguous().squeeze(
+            dim=1)  # (B, 1 or 2)
+        rcnn_iou = self.iou_layers(shared_features).transpose(1,
+                                                              2).contiguous().squeeze(
+            dim=1)  # (B, 1)
+        rcnn_reg = self.reg_layers(shared_features).transpose(1,
+                                                              2).contiguous().squeeze(
+            dim=1)  # (B, C)
+
+        batch_dict['fpvrcnn_out'] = {
+            'rcnn_cls': rcnn_cls,
+            'rcnn_iou': rcnn_iou,
+            'rcnn_reg': rcnn_reg,
+        }
+        return batch_dict
diff --git a/opencood/opencood/models/sub_modules/sparse_backbone_3d.py b/opencood/opencood/models/sub_modules/sparse_backbone_3d.py
new file mode 100644
index 00000000..b9d6aa21
--- /dev/null
+++ b/opencood/opencood/models/sub_modules/sparse_backbone_3d.py
@@ -0,0 +1,146 @@
+from functools import partial
+
+import spconv
+import torch.nn as nn
+
+try: # spconv1
+    from spconv import SparseSequential, SubMConv3d, SparseConv3d, SparseInverseConv3d, SparseConvTensor
+except: # spconv2
+    from spconv.pytorch import  SparseSequential, SubMConv3d, SparseConv3d, SparseInverseConv3d, SparseConvTensor
+
+def post_act_block(in_channels, out_channels, kernel_size, indice_key=None, stride=1, padding=0,
+                   conv_type='subm', norm_fn=None):
+
+    if conv_type == 'subm':
+        conv = SubMConv3d(in_channels, out_channels, kernel_size, bias=False, indice_key=indice_key)
+    elif conv_type == 'spconv':
+        conv = SparseConv3d(in_channels, out_channels, kernel_size, stride=stride, padding=padding,
+                                   bias=False, indice_key=indice_key)
+    elif conv_type == 'inverseconv':
+        conv = SparseInverseConv3d(in_channels, out_channels, kernel_size, indice_key=indice_key, bias=False)
+    else:
+        raise NotImplementedError
+
+    m = SparseSequential(
+        conv,
+        norm_fn(out_channels),
+        nn.ReLU(),
+    )
+
+    return m
+
+
+class VoxelBackBone8x(nn.Module):
+    def __init__(self, model_cfg, input_channels, grid_size, **kwargs):
+        super().__init__()
+        self.model_cfg = model_cfg
+        norm_fn = partial(nn.BatchNorm1d, eps=1e-3, momentum=0.01)
+
+        self.sparse_shape = grid_size[::-1] + [1, 0, 0]
+
+        self.conv_input = SparseSequential(
+            SubMConv3d(input_channels, 16, 3, padding=1, bias=False, indice_key='subm1'),
+            norm_fn(16),
+            nn.ReLU(),
+        )
+        block = post_act_block
+
+        self.conv1 = SparseSequential(
+            block(16, 16, 3, norm_fn=norm_fn, padding=1, indice_key='subm1'),
+        )
+
+        self.conv2 = SparseSequential(
+            # [1600, 1408, 41] <- [800, 704, 21]
+            block(16, 32, 3, norm_fn=norm_fn, stride=2, padding=1, indice_key='spconv2', conv_type='spconv'),
+            block(32, 32, 3, norm_fn=norm_fn, padding=1, indice_key='subm2'),
+            block(32, 32, 3, norm_fn=norm_fn, padding=1, indice_key='subm2'),
+        )
+
+        self.conv3 = SparseSequential(
+            # [800, 704, 21] <- [400, 352, 11]
+            block(32, 64, 3, norm_fn=norm_fn, stride=2, padding=1, indice_key='spconv3', conv_type='spconv'),
+            block(64, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm3'),
+            block(64, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm3'),
+        )
+
+        self.conv4 = SparseSequential(
+            # [400, 352, 11] <- [200, 176, 5]
+            block(64, 64, 3, norm_fn=norm_fn, stride=2, padding=(0, 1, 1), indice_key='spconv4', conv_type='spconv'),
+            block(64, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm4'),
+            block(64, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm4'),
+        )
+
+        last_pad = 0
+        if 'num_features_out' in self.model_cfg:
+            self.num_point_features = self.model_cfg['num_features_out']
+        else:
+            self.num_point_features = 128
+        self.conv_out = SparseSequential(
+            # [200, 150, 5] -> [200, 150, 2]
+            SparseConv3d(64, self.num_point_features, (3, 1, 1), stride=(2, 1, 1), padding=last_pad,
+                                bias=False, indice_key='spconv_down2'),
+            norm_fn(self.num_point_features),
+            nn.ReLU(),
+        )
+
+        self.backbone_channels = {
+            'x_conv1': 16,
+            'x_conv2': 32,
+            'x_conv3': 64,
+            'x_conv4': 64
+        }
+
+    def forward(self, batch_dict):
+        """
+        Args:
+            batch_dict:
+                batch_size: int
+                vfe_features: (num_voxels, C)
+                voxel_coords: (num_voxels, 4), [batch_idx, z_idx, y_idx, x_idx]
+        Returns:
+            batch_dict:
+                encoded_spconv_tensor: sparse tensor
+        """
+        voxel_features, voxel_coords = batch_dict['voxel_features'], \
+                                       batch_dict['voxel_coords']
+        batch_size = batch_dict['batch_size']
+        input_sp_tensor = SparseConvTensor(
+            features=voxel_features,
+            indices=voxel_coords.int(),
+            spatial_shape=self.sparse_shape,
+            batch_size=batch_size
+        )
+
+        x = self.conv_input(input_sp_tensor)
+
+        x_conv1 = self.conv1(x)
+        x_conv2 = self.conv2(x_conv1)
+        x_conv3 = self.conv3(x_conv2)
+        x_conv4 = self.conv4(x_conv3)
+
+        # for detection head
+        # [200, 176, 5] -> [200, 176, 2]
+        out = self.conv_out(x_conv4)
+
+        batch_dict.update({
+            'encoded_spconv_tensor': out,
+            'encoded_spconv_tensor_stride': 8
+        })
+        batch_dict.update({
+            'multi_scale_3d_features': {
+                'x_conv1': x_conv1,
+                'x_conv2': x_conv2,
+                'x_conv3': x_conv3,
+                'x_conv4': x_conv4,
+            }
+        })
+        batch_dict.update({
+            'multi_scale_3d_strides': {
+                'x_conv1': 1,
+                'x_conv2': 2,
+                'x_conv3': 4,
+                'x_conv4': 8,
+            }
+        })
+
+        return batch_dict
diff --git a/opencood/opencood/models/sub_modules/split_attn.py b/opencood/opencood/models/sub_modules/split_attn.py
new file mode 100644
index 00000000..d064d517
--- /dev/null
+++ b/opencood/opencood/models/sub_modules/split_attn.py
@@ -0,0 +1,63 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class RadixSoftmax(nn.Module):
+    def __init__(self, radix, cardinality):
+        super(RadixSoftmax, self).__init__()
+        self.radix = radix
+        self.cardinality = cardinality
+
+    def forward(self, x):
+        # x: (B, L, 1, 1, 3C)
+        batch = x.size(0)
+        cav_num = x.size(1)
+
+        if self.radix > 1:
+            # x: (B, L, 1, 3, C)
+            x = x.view(batch,
+                       cav_num,
+                       self.cardinality, self.radix, -1)
+            x = F.softmax(x, dim=3)
+            # B, 3LC
+            x = x.reshape(batch, -1)
+        else:
+            x = torch.sigmoid(x)
+        return x
+
+
+class SplitAttn(nn.Module):
+    def __init__(self, input_dim):
+        super(SplitAttn, self).__init__()
+        self.input_dim = input_dim
+
+        self.fc1 = nn.Linear(input_dim, input_dim, bias=False)
+        self.bn1 = nn.LayerNorm(input_dim)
+        self.act1 = nn.ReLU()
+        self.fc2 = nn.Linear(input_dim, input_dim * 3, bias=False)
+
+        self.rsoftmax = RadixSoftmax(3, 1)
+
+    def forward(self, window_list):
+        # window list: [(B, L, H, W, C) * 3]
+        assert len(window_list) == 3, 'only 3 windows are supported'
+
+        sw, mw, bw = window_list[0], window_list[1], window_list[2]
+        B, L = sw.shape[0], sw.shape[1]
+
+        # global average pooling, B, L, H, W, C
+        x_gap = sw + mw + bw
+        # B, L, 1, 1, C
+        x_gap = x_gap.mean((2, 3), keepdim=True)
+        x_gap = self.act1(self.bn1(self.fc1(x_gap)))
+        # B, L, 1, 1, 3C
+        x_attn = self.fc2(x_gap)
+        # B L 1 1 3C
+        x_attn = self.rsoftmax(x_attn).view(B, L, 1, 1, -1)
+
+        out = sw * x_attn[:, :, :, :, 0:self.input_dim] + \
+              mw * x_attn[:, :, :, :, self.input_dim:2*self.input_dim] +\
+              bw * x_attn[:, :, :, :, self.input_dim*2:]
+
+        return out
diff --git a/opencood/opencood/models/sub_modules/torch_transformation_utils.py b/opencood/opencood/models/sub_modules/torch_transformation_utils.py
new file mode 100644
index 00000000..321ab4a3
--- /dev/null
+++ b/opencood/opencood/models/sub_modules/torch_transformation_utils.py
@@ -0,0 +1,426 @@
+"""
+torch_transformation_utils.py
+"""
+import os
+
+import torch
+import torch.nn.functional as F
+import numpy as np
+import matplotlib.pyplot as plt
+
+def get_roi_and_cav_mask(shape, cav_mask, spatial_correction_matrix,
+                         discrete_ratio, downsample_rate):
+    """
+    Get mask for the combination of cav_mask and rorated ROI mask.
+    Parameters
+    ----------
+    shape : tuple
+        Shape of (B, L, H, W, C).
+    cav_mask : torch.Tensor
+        Shape of (B, L).
+    spatial_correction_matrix : torch.Tensor
+        Shape of (B, L, 4, 4)
+    discrete_ratio : float
+        Discrete ratio.
+    downsample_rate : float
+        Downsample rate.
+
+    Returns
+    -------
+    com_mask : torch.Tensor
+        Combined mask with shape (B, H, W, L, 1).
+
+    """
+    B, L, H, W, C = shape
+    C = 1
+    # (B,L,4,4)
+    dist_correction_matrix = get_discretized_transformation_matrix(
+        spatial_correction_matrix, discrete_ratio,
+        downsample_rate)
+    # (B*L,2,3)
+    T = get_transformation_matrix(
+        dist_correction_matrix.reshape(-1, 2, 3), (H, W))
+    # (B,L,1,H,W)
+    roi_mask = get_rotated_roi((B, L, C, H, W), T)
+    # (B,L,1,H,W)
+    com_mask = combine_roi_and_cav_mask(roi_mask, cav_mask)
+    # (B,H,W,1,L)
+    com_mask = com_mask.permute(0,3,4,2,1)
+    return com_mask
+
+
+def combine_roi_and_cav_mask(roi_mask, cav_mask):
+    """
+    Combine ROI mask and CAV mask
+
+    Parameters
+    ----------
+    roi_mask : torch.Tensor
+        Mask for ROI region after considering the spatial transformation/correction.
+    cav_mask : torch.Tensor
+        Mask for CAV to remove padded 0.
+
+    Returns
+    -------
+    com_mask : torch.Tensor
+        Combined mask.
+    """
+    # (B, L, 1, 1, 1)
+    cav_mask = cav_mask.unsqueeze(2).unsqueeze(3).unsqueeze(4)
+    # (B, L, C, H, W)
+    cav_mask = cav_mask.expand(roi_mask.shape)
+    # (B, L, C, H, W)
+    com_mask = roi_mask * cav_mask
+    return com_mask
+
+
+def get_rotated_roi(shape, correction_matrix):
+    """
+    Get rorated ROI mask.
+
+    Parameters
+    ----------
+    shape : tuple
+        Shape of (B,L,C,H,W).
+    correction_matrix : torch.Tensor
+        Correction matrix with shape (N,2,3).
+
+    Returns
+    -------
+    roi_mask : torch.Tensor
+        Roated ROI mask with shape (N,2,3).
+
+    """
+    B, L, C, H, W = shape
+    # To reduce the computation, we only need to calculate the
+    # mask for the first channel.
+    # (B,L,1,H,W)
+    x = torch.ones((B, L, 1, H, W)).to(correction_matrix.dtype).to(
+        correction_matrix.device)
+    # (B*L,1,H,W)
+    roi_mask = warp_affine(x.reshape(-1, 1, H, W), correction_matrix,
+                           dsize=(H, W), mode="nearest")
+    # (B,L,C,H,W)
+    roi_mask = torch.repeat_interleave(roi_mask, C, dim=1).reshape(B, L, C, H,
+                                                                   W)
+    return roi_mask
+
+
+def get_discretized_transformation_matrix(matrix, discrete_ratio,
+                                          downsample_rate):
+    """
+    Get disretized transformation matrix.
+    Parameters
+    ----------
+    matrix : torch.Tensor
+        Shape -- (B, L, 4, 4) where B is the batch size, L is the max cav
+        number.
+    discrete_ratio : float
+        Discrete ratio.
+    downsample_rate : float/int
+        downsample_rate
+
+    Returns
+    -------
+    matrix : torch.Tensor
+        Output transformation matrix in 2D with shape (B, L, 2, 3),
+        including 2D transformation and 2D rotation.
+
+    """
+    matrix = matrix[:, :, [0, 1], :][:, :, :, [0, 1, 3]]
+    # normalize the x,y transformation
+    matrix[:, :, :, -1] = matrix[:, :, :, -1] \
+                          / (discrete_ratio * downsample_rate)
+
+    return matrix.type(dtype=torch.float)
+
+
+def _torch_inverse_cast(input):
+    r"""
+    Helper function to make torch.inverse work with other than fp32/64.
+    The function torch.inverse is only implemented for fp32/64 which makes
+    impossible to be used by fp16 or others. What this function does,
+    is cast input data type to fp32, apply torch.inverse,
+    and cast back to the input dtype.
+    Args:
+        input : torch.Tensor
+            Tensor to be inversed.
+
+    Returns:
+        out : torch.Tensor
+            Inversed Tensor.
+
+    """
+    dtype = input.dtype
+    if dtype not in (torch.float32, torch.float64):
+        dtype = torch.float32
+    out = torch.inverse(input.to(dtype)).to(input.dtype)
+    return out
+
+
+def normal_transform_pixel(
+        height, width, device, dtype, eps=1e-14):
+    r"""
+    Compute the normalization matrix from image size in pixels to [-1, 1].
+    Args:
+        height : int
+            Image height.
+        width : int
+            Image width.
+        device : torch.device
+            Output tensor devices.
+        dtype : torch.dtype
+            Output tensor data type.
+        eps : float
+            Epsilon to prevent divide-by-zero errors.
+
+    Returns:
+        tr_mat : torch.Tensor
+            Normalized transform with shape :math:`(1, 3, 3)`.
+    """
+    tr_mat = torch.tensor(
+        [[1.0, 0.0, -1.0], [0.0, 1.0, -1.0], [0.0, 0.0, 1.0]], device=device,
+        dtype=dtype)  # 3x3
+
+    # prevent divide by zero bugs
+    width_denom = eps if width == 1 else width - 1.0
+    height_denom = eps if height == 1 else height - 1.0
+
+    tr_mat[0, 0] = tr_mat[0, 0] * 2.0 / width_denom
+    tr_mat[1, 1] = tr_mat[1, 1] * 2.0 / height_denom
+
+    return tr_mat.unsqueeze(0)  # 1x3x3
+
+
+def eye_like(n, B, device, dtype):
+    r"""
+    Return a 2-D tensor with ones on the diagonal and
+    zeros elsewhere with the same batch size as the input.
+    Args:
+        n : int
+            The number of rows :math:`(n)`.
+        B : int
+            Btach size.
+        device : torch.device
+            Devices of the output tensor.
+        dtype : torch.dtype
+            Data type of the output tensor.
+
+    Returns:
+       The identity matrix with the shape :math:`(B, n, n)`.
+    """
+
+    identity = torch.eye(n, device=device, dtype=dtype)
+    return identity[None].repeat(B, 1, 1)
+
+
+def normalize_homography(dst_pix_trans_src_pix, dsize_src, dsize_dst=None):
+    r"""
+    Normalize a given homography in pixels to [-1, 1].
+    Args:
+        dst_pix_trans_src_pix : torch.Tensor
+            Homography/ies from source to destination to be normalized with
+            shape :math:`(B, 3, 3)`.
+        dsize_src : Tuple[int, int]
+            Size of the source image (height, width).
+        dsize_dst : Tuple[int, int]
+            Size of the destination image (height, width).
+
+    Returns:
+        dst_norm_trans_src_norm : torch.Tensor
+            The normalized homography of shape :math:`(B, 3, 3)`.
+    """
+    if dsize_dst is None:
+        dsize_dst = dsize_src
+    # source and destination sizes
+    src_h, src_w = dsize_src
+    dst_h, dst_w = dsize_dst
+    device = dst_pix_trans_src_pix.device
+    dtype = dst_pix_trans_src_pix.dtype
+    # compute the transformation pixel/norm for src/dst
+    src_norm_trans_src_pix = normal_transform_pixel(src_h, src_w, device,
+                                                    dtype).to(
+        dst_pix_trans_src_pix)
+
+    src_pix_trans_src_norm = _torch_inverse_cast(src_norm_trans_src_pix)
+    dst_norm_trans_dst_pix = normal_transform_pixel(dst_h, dst_w, device,
+                                                    dtype).to(
+        dst_pix_trans_src_pix)
+    # compute chain transformations
+    dst_norm_trans_src_norm: torch.Tensor = dst_norm_trans_dst_pix @ (
+            dst_pix_trans_src_pix @ src_pix_trans_src_norm)
+    return dst_norm_trans_src_norm
+
+
+def get_rotation_matrix2d(M, dsize):
+    r"""
+    Return rotation matrix for torch.affine_grid based on transformation matrix.
+    Args:
+        M : torch.Tensor
+            Transformation matrix with shape :math:`(B, 2, 3)`.
+        dsize : Tuple[int, int]
+            Size of the source image (height, width).
+
+    Returns:
+        R : torch.Tensor
+            Rotation matrix with shape :math:`(B, 2, 3)`.
+    """
+    H, W = dsize
+    B = M.shape[0]
+    center = torch.Tensor([W / 2, H / 2]).to(M.dtype).to(M.device).unsqueeze(0)
+    shift_m = eye_like(3, B, M.device, M.dtype)
+    shift_m[:, :2, 2] = center
+
+    shift_m_inv = eye_like(3, B, M.device, M.dtype)
+    shift_m_inv[:, :2, 2] = -center
+
+    rotat_m = eye_like(3, B, M.device, M.dtype)
+    rotat_m[:, :2, :2] = M[:, :2, :2]
+    affine_m = shift_m @ rotat_m @ shift_m_inv
+    return affine_m[:, :2, :]  # Bx2x3
+
+
+def get_transformation_matrix(M, dsize):
+    r"""
+    Return transformation matrix for torch.affine_grid.
+    Args:
+        M : torch.Tensor
+            Transformation matrix with shape :math:`(N, 2, 3)`.
+        dsize : Tuple[int, int]
+            Size of the source image (height, width).
+
+    Returns:
+        T : torch.Tensor
+            Transformation matrix with shape :math:`(N, 2, 3)`.
+    """
+    T = get_rotation_matrix2d(M, dsize)
+    T[..., 2] += M[..., 2]
+    return T
+
+
+def convert_affinematrix_to_homography(A):
+    r"""
+    Convert to homography coordinates
+    Args:
+        A : torch.Tensor
+            The affine matrix with shape :math:`(B,2,3)`.
+
+    Returns:
+        H : torch.Tensor
+            The homography matrix with shape of :math:`(B,3,3)`.
+    """
+    H: torch.Tensor = torch.nn.functional.pad(A, [0, 0, 0, 1], "constant",
+                                              value=0.0)
+    H[..., -1, -1] += 1.0
+    return H
+
+
+def warp_affine(
+        src, M, dsize,
+        mode='bilinear',
+        padding_mode='zeros',
+        align_corners=True):
+    r"""
+    Transform the src based on transformation matrix M.
+    Args:
+        src : torch.Tensor
+            Input feature map with shape :math:`(B,C,H,W)`.
+        M : torch.Tensor
+            Transformation matrix with shape :math:`(B,2,3)`.
+        dsize : tuple
+            Tuple of output image H_out and W_out.
+        mode : str
+            Interpolation methods for F.grid_sample.
+        padding_mode : str
+            Padding methods for F.grid_sample.
+        align_corners : boolean
+            Parameter of F.affine_grid.
+
+    Returns:
+        Transformed features with shape :math:`(B,C,H,W)`.
+    """
+
+    B, C, H, W = src.size()
+
+    # we generate a 3x3 transformation matrix from 2x3 affine
+    M_3x3 = convert_affinematrix_to_homography(M)
+    dst_norm_trans_src_norm = normalize_homography(M_3x3, (H, W), dsize)
+
+    # src_norm_trans_dst_norm = torch.inverse(dst_norm_trans_src_norm)
+    src_norm_trans_dst_norm = _torch_inverse_cast(dst_norm_trans_src_norm)
+    grid = F.affine_grid(src_norm_trans_dst_norm[:, :2, :],
+                         [B, C, dsize[0], dsize[1]],
+                         align_corners=align_corners)
+
+    return F.grid_sample(src.half() if grid.dtype==torch.half else src, 
+                         grid, align_corners=align_corners, mode=mode,
+                         padding_mode=padding_mode)
+
+
+class Test:
+    """
+    Test the transformation in this file.
+    The methods in this class are not supposed to be used outside of this file.
+    """
+
+    def __init__(self):
+        pass
+
+    @staticmethod
+    def load_img():
+        torch.manual_seed(0)
+        x = torch.randn(1, 5, 16, 400, 200) * 100
+        # x = torch.ones(1, 5, 16, 400, 200)
+        return x
+
+    @staticmethod
+    def load_raw_transformation_matrix(N):
+        a = 90 / 180 * np.pi
+        matrix = torch.Tensor([[np.cos(a), -np.sin(a), 10],
+                               [np.sin(a), np.cos(a), 10]])
+        matrix = torch.repeat_interleave(matrix.unsqueeze(0).unsqueeze(0), N,
+                                         dim=1)
+        return matrix
+
+    @staticmethod
+    def load_raw_transformation_matrix2(N, alpha):
+        a = alpha / 180 * np.pi
+        matrix = torch.Tensor([[np.cos(a), -np.sin(a), 0, 0],
+                               [np.sin(a), np.cos(a), 0, 0]])
+        matrix = torch.repeat_interleave(matrix.unsqueeze(0).unsqueeze(0), N,
+                                         dim=1)
+        return matrix
+
+    @staticmethod
+    def test():
+        img = Test.load_img()
+        B, L, C, H, W = img.shape
+        raw_T = Test.load_raw_transformation_matrix(5)
+        T = get_transformation_matrix(raw_T.reshape(-1, 2, 3), (H, W))
+        img_rot = warp_affine(img.reshape(-1, C, H, W), T, (H, W))
+        print(img_rot[0, 0, :, :])
+        plt.matshow(img_rot[0, 0, :, :])
+        plt.show()
+
+    @staticmethod
+    def test_combine_roi_and_cav_mask():
+        B = 2
+        L = 5
+        C = 16
+        H = 300
+        W = 400
+        # 2, 5
+        cav_mask = torch.Tensor([[1, 1, 1, 0, 0], [1, 0, 0, 0, 0]])
+        x = torch.zeros(B, L, C, H, W)
+        correction_matrix = Test.load_raw_transformation_matrix2(5, 10)
+        correction_matrix = torch.cat([correction_matrix, correction_matrix],
+                                      dim=0)
+        mask = get_roi_and_cav_mask((B, L, H, W, C), cav_mask, 
+                                    correction_matrix, 0.4, 4)
+        plt.matshow(mask[0, :, :, 0, 0])
+        plt.show()
+
+
+if __name__ == "__main__":
+    os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'
+    Test.test_combine_roi_and_cav_mask()
diff --git a/opencood/opencood/models/sub_modules/vsa.py b/opencood/opencood/models/sub_modules/vsa.py
new file mode 100644
index 00000000..12826997
--- /dev/null
+++ b/opencood/opencood/models/sub_modules/vsa.py
@@ -0,0 +1,277 @@
+import copy
+import random
+
+import torch
+import torch.nn as nn
+
+from opencood.pcdet_utils.pointnet2.pointnet2_stack import pointnet2_modules as pointnet2_stack_modules
+from opencood.pcdet_utils.pointnet2.pointnet2_stack import pointnet2_utils as pointnet2_stack_utils
+from opencood.pcdet_utils.roiaware_pool3d.roiaware_pool3d_utils import points_in_boxes_gpu
+from opencood.utils import common_utils
+
+
+def bilinear_interpolate_torch(im, x, y):
+    """
+    Args:
+        im: (H, W, C) [y, x]
+        x: (N)
+        y: (N)
+
+    Returns:
+
+    """
+    x0 = torch.floor(x).long()
+    x1 = x0 + 1
+
+    y0 = torch.floor(y).long()
+    y1 = y0 + 1
+
+    x0 = torch.clamp(x0, 0, im.shape[1] - 1)
+    x1 = torch.clamp(x1, 0, im.shape[1] - 1)
+    y0 = torch.clamp(y0, 0, im.shape[0] - 1)
+    y1 = torch.clamp(y1, 0, im.shape[0] - 1)
+
+    Ia = im[y0, x0]
+    Ib = im[y1, x0]
+    Ic = im[y0, x1]
+    Id = im[y1, x1]
+
+    wa = (x1.type_as(x) - x) * (y1.type_as(y) - y)
+    wb = (x1.type_as(x) - x) * (y - y0.type_as(y))
+    wc = (x - x0.type_as(x)) * (y1.type_as(y) - y)
+    wd = (x - x0.type_as(x)) * (y - y0.type_as(y))
+    ans = torch.t((torch.t(Ia) * wa)) + torch.t(torch.t(Ib) * wb) + torch.t(torch.t(Ic) * wc) + torch.t(torch.t(Id) * wd)
+    return ans
+
+
+class VoxelSetAbstraction(nn.Module):
+    def __init__(self, model_cfg, voxel_size, point_cloud_range, num_bev_features=None,
+                 num_rawpoint_features=None, **kwargs):
+        super().__init__()
+        self.model_cfg = model_cfg
+        self.voxel_size = voxel_size
+        self.point_cloud_range = point_cloud_range
+
+        SA_cfg = self.model_cfg['sa_layer']
+
+        self.SA_layers = nn.ModuleList()
+        self.SA_layer_names = []
+        self.downsample_times_map = {}
+        c_in = 0
+        for src_name in self.model_cfg['features_source']:
+            if src_name in ['bev', 'raw_points']:
+                continue
+            self.downsample_times_map[src_name] = SA_cfg[src_name]['downsample_factor']
+            mlps = copy.copy(SA_cfg[src_name]['mlps'])
+            for k in range(len(mlps)):
+                mlps[k] = [mlps[k][0]] + mlps[k]
+            cur_layer = pointnet2_stack_modules.StackSAModuleMSG(
+                radii=SA_cfg[src_name]['pool_radius'],
+                nsamples=SA_cfg[src_name]['n_sample'],
+                mlps=mlps,
+                use_xyz=True,
+                pool_method='max_pool',
+            )
+            self.SA_layers.append(cur_layer)
+            self.SA_layer_names.append(src_name)
+
+            c_in += sum([x[-1] for x in mlps])
+
+        if 'bev' in self.model_cfg['features_source']:
+            c_bev = num_bev_features
+            c_in += c_bev
+
+        if 'raw_points' in self.model_cfg['features_source']:
+            mlps = copy.copy(SA_cfg['raw_points']['mlps'])
+            for k in range(len(mlps)):
+                mlps[k] = [num_rawpoint_features - 3] + mlps[k]
+
+            self.SA_rawpoints = pointnet2_stack_modules.StackSAModuleMSG(
+                radii=SA_cfg['raw_points']['pool_radius'],
+                nsamples=SA_cfg['raw_points']['n_sample'],
+                mlps=mlps,
+                use_xyz=True,
+                pool_method='max_pool'
+            )
+            c_in += sum([x[-1] for x in mlps])
+
+        self.vsa_point_feature_fusion = nn.Sequential(
+            nn.Linear(c_in, self.model_cfg['num_out_features'], bias=False),
+            nn.BatchNorm1d(self.model_cfg['num_out_features']),
+            nn.ReLU(),
+        )
+        self.num_point_features = self.model_cfg['num_out_features']
+        self.num_point_features_before_fusion = c_in
+
+    def interpolate_from_bev_features(self, keypoints, bev_features, batch_size, bev_stride):
+        x_idxs = (keypoints[:, :, 0] - self.point_cloud_range[0]) / self.voxel_size[0]
+        y_idxs = (keypoints[:, :, 1] - self.point_cloud_range[1]) / self.voxel_size[1]
+        x_idxs = x_idxs / bev_stride
+        y_idxs = y_idxs / bev_stride
+
+        point_bev_features_list = []
+        for k in range(batch_size):
+            cur_x_idxs = x_idxs[k]
+            cur_y_idxs = y_idxs[k]
+            cur_bev_features = bev_features[k].permute(1, 2, 0)  # (H, W, C)
+            point_bev_features = bilinear_interpolate_torch(cur_bev_features, cur_x_idxs, cur_y_idxs)
+            point_bev_features_list.append(point_bev_features.unsqueeze(dim=0))
+
+        point_bev_features = torch.cat(point_bev_features_list, dim=0)  # (B, N, C0)
+        return point_bev_features
+
+    def get_sampled_points(self, batch_dict):
+        batch_size = batch_dict['batch_size']
+        if self.model_cfg['point_source'] == 'raw_points':
+            src_points = batch_dict['origin_lidar'][:, 1:]
+            batch_indices = batch_dict['origin_lidar'][:, 0].long()
+        elif self.model_cfg['point_source'] == 'voxel_centers':
+            src_points = common_utils.get_voxel_centers(
+                batch_dict['voxel_coords'][:, 1:4],
+                downsample_times=1,
+                voxel_size=self.voxel_size,
+                point_cloud_range=self.point_cloud_range
+            )
+            batch_indices = batch_dict['voxel_coords'][:, 0].long()
+        else:
+            raise NotImplementedError
+
+        keypoints_batch = torch.randn((batch_size, self.model_cfg['num_keypoints'], 4), device=src_points.device)
+        keypoints_batch[..., 0] = keypoints_batch[..., 0] * 140
+        keypoints_batch[..., 1] = keypoints_batch[..., 0] * 40
+        # points with height flag 10 are padding/invalid, for later filtering
+        keypoints_batch[..., 2] = 10.0
+        for bs_idx in range(batch_size):
+            bs_mask = (batch_indices == bs_idx)
+            sampled_points = src_points[bs_mask].unsqueeze(dim=0)  # (1, N, 3)
+            # sample points with FPS
+            # some cropped pcd may have very few points, select various number
+            # of points to ensure similar sample density
+            # 50000 is approximately the number of points in one full pcd
+            num_kpts = int(self.model_cfg['num_keypoints'] * sampled_points.shape[1] / 50000) + 1
+            num_kpts = min(num_kpts, self.model_cfg['num_keypoints'])
+            cur_pt_idxs = pointnet2_stack_utils.furthest_point_sample(
+                sampled_points[:, :, 0:3].contiguous(), num_kpts
+            ).long()
+
+            if sampled_points.shape[1] < num_kpts:
+                empty_num = num_kpts - sampled_points.shape[1]
+                cur_pt_idxs[0, -empty_num:] = cur_pt_idxs[0, :empty_num]
+
+            keypoints = sampled_points[0][cur_pt_idxs[0]].unsqueeze(dim=0)
+
+            keypoints_batch[bs_idx, :len(keypoints[0]), :] = keypoints
+
+        # keypoints = torch.cat(keypoints_list, dim=0)  # (B, M, 3)
+        return keypoints_batch
+
+    def forward(self, batch_dict):
+        """
+        Args:
+            batch_dict:
+                batch_size:
+                keypoints: (B, num_keypoints, 3)
+                multi_scale_3d_features: {
+                        'x_conv4': ...
+                    }
+                points: optional (N, 1 + 3 + C) [bs_idx, x, y, z, ...]
+                spatial_features: optional
+                spatial_features_stride: optional
+
+        Returns:
+            point_features: (N, C)
+            point_coords: (N, 4)
+
+        """
+        keypoints = self.get_sampled_points(batch_dict) # BxNx4
+        kpt_mask1 = torch.logical_and(keypoints[..., 2] > -2.8, keypoints[..., 2] < 1.0)
+        kpt_mask2 = None
+        # Only select the points that are in the predicted bounding boxes
+        if 'det_boxes' in batch_dict:
+            dets_list = batch_dict['det_boxes']
+            max_len = max([len(dets) for dets in dets_list])
+            boxes = torch.zeros((len(dets_list), max_len, 7), dtype=dets_list[0].dtype,
+                                device=dets_list[0].device)
+            for i, dets in enumerate(dets_list):
+                if len(dets)==0:
+                    continue
+                cur_dets = dets.clone()
+                if self.model_cfg['enlarge_selection_boxes']:
+                    cur_dets[:, 3:6] += 0.5
+                boxes[i, :len(dets)] = cur_dets
+            # mask out some keypoints to spare the GPU storage
+            kpt_mask2 = points_in_boxes_gpu(keypoints[..., :3], boxes) >= 0
+
+        kpt_mask = torch.logical_and(kpt_mask1, kpt_mask2) if kpt_mask2 is not None else kpt_mask1
+        # Ensure there are more than 2 points are selected to satisfy the
+        # condition of batch norm in the FC layers of feature fusion module
+        if (kpt_mask).sum() < 2:
+            kpt_mask[0, torch.randint(0, 1024, (2,))] = True
+
+        point_features_list = []
+        if 'bev' in self.model_cfg['features_source']:
+            point_bev_features = self.interpolate_from_bev_features(
+                keypoints[..., :3], batch_dict['spatial_features'], batch_dict['batch_size'],
+                bev_stride=batch_dict['spatial_features_stride']
+            )
+            point_features_list.append(point_bev_features[kpt_mask])
+
+        batch_size, num_keypoints, _ = keypoints.shape
+
+        new_xyz = keypoints[kpt_mask]
+        new_xyz_batch_cnt = torch.tensor([(mask).sum() for mask in kpt_mask], device=new_xyz.device).int()
+
+        if 'raw_points' in self.model_cfg['features_source']:
+            raw_points = batch_dict['origin_lidar']
+            xyz = raw_points[:, 1:4]
+            xyz_batch_cnt = xyz.new_zeros(batch_size).int()
+            indices = raw_points[:, 0].long()
+            for bs_idx in range(batch_size):
+                xyz_batch_cnt[bs_idx] = (indices == bs_idx).sum()
+            point_features = None
+
+            pooled_points, pooled_features = self.SA_rawpoints(
+                xyz=xyz.contiguous(),
+                xyz_batch_cnt=xyz_batch_cnt,
+                new_xyz=new_xyz[:, :3].contiguous(),
+                new_xyz_batch_cnt=new_xyz_batch_cnt,
+                features=point_features,
+            )
+            point_features_list.append(pooled_features)
+
+        for k, src_name in enumerate(self.SA_layer_names):
+            cur_coords = batch_dict['multi_scale_3d_features'][src_name].indices
+            xyz = common_utils.get_voxel_centers(
+                cur_coords[:, 1:4],
+                downsample_times=self.downsample_times_map[src_name],
+                voxel_size=self.voxel_size,
+                point_cloud_range=self.point_cloud_range
+            )
+            xyz_batch_cnt = xyz.new_zeros(batch_size).int()
+            for bs_idx in range(batch_size):
+                xyz_batch_cnt[bs_idx] = (cur_coords[:, 0] == bs_idx).sum()
+
+            pooled_points, pooled_features = self.SA_layers[k](
+                xyz=xyz.contiguous(),
+                xyz_batch_cnt=xyz_batch_cnt,
+                new_xyz=new_xyz[:, :3].contiguous(),
+                new_xyz_batch_cnt=new_xyz_batch_cnt,
+                features=batch_dict['multi_scale_3d_features'][src_name].features.contiguous(),
+            )
+
+            point_features_list.append(pooled_features)
+
+        point_features = torch.cat(point_features_list, dim=1)
+
+        batch_dict['point_features_before_fusion'] = point_features.view(-1, point_features.shape[-1])
+        point_features = self.vsa_point_feature_fusion(point_features.view(-1, point_features.shape[-1]))
+
+        cur_idx = 0
+        batch_dict['point_features'] = []
+        batch_dict['point_coords'] = []
+        for num in new_xyz_batch_cnt:
+            batch_dict['point_features'].append(point_features[cur_idx:cur_idx + num])
+            batch_dict['point_coords'].append(new_xyz[cur_idx:cur_idx + num])
+            cur_idx += num
+
+        return batch_dict
diff --git a/opencood/opencood/models/voxel_net.py b/opencood/opencood/models/voxel_net.py
new file mode 100644
index 00000000..78116a2c
--- /dev/null
+++ b/opencood/opencood/models/voxel_net.py
@@ -0,0 +1,234 @@
+# -*- coding: utf-8 -*-
+# Author: Runsheng Xu <rxx3386@ucla.edu>, OpenPCDet
+# License: TDG-Attribution-NonCommercial-NoDistrib
+
+
+import torch.nn as nn
+import torch.nn.functional as F
+import torch
+from torch.autograd import Variable
+
+from opencood.models.sub_modules.pillar_vfe import PillarVFE
+from opencood.utils.common_utils import torch_tensor_to_numpy
+
+
+# conv2d + bn + relu
+class Conv2d(nn.Module):
+
+    def __init__(self, in_channels, out_channels, k, s, p, activation=True,
+                 batch_norm=True):
+        super(Conv2d, self).__init__()
+        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=k,
+                              stride=s, padding=p)
+        if batch_norm:
+            self.bn = nn.BatchNorm2d(out_channels)
+        else:
+            self.bn = None
+        self.activation = activation
+
+    def forward(self, x):
+        x = self.conv(x)
+        if self.bn is not None:
+            x = self.bn(x)
+        if self.activation:
+            return F.relu(x, inplace=True)
+        else:
+            return x
+
+
+# conv3d + bn + relu
+class Conv3d(nn.Module):
+
+    def __init__(self, in_channels, out_channels, k, s, p, batch_norm=True):
+        super(Conv3d, self).__init__()
+        self.conv = nn.Conv3d(in_channels, out_channels, kernel_size=k,
+                              stride=s, padding=p)
+        if batch_norm:
+            self.bn = nn.BatchNorm3d(out_channels)
+        else:
+            self.bn = None
+
+    def forward(self, x):
+        x = self.conv(x)
+        if self.bn is not None:
+            x = self.bn(x)
+
+        return F.relu(x, inplace=True)
+
+
+# Fully Connected Network
+class FCN(nn.Module):
+
+    def __init__(self, cin, cout):
+        super(FCN, self).__init__()
+        self.cout = cout
+        self.linear = nn.Linear(cin, cout)
+        self.bn = nn.BatchNorm1d(cout)
+
+    def forward(self, x):
+        # KK is the stacked k across batch
+        kk, t, _ = x.shape
+        x = self.linear(x.view(kk * t, -1))
+        x = F.relu(self.bn(x))
+        return x.view(kk, t, -1)
+
+
+# Voxel Feature Encoding layer
+class VFE(nn.Module):
+
+    def __init__(self, cin, cout, T):
+        super(VFE, self).__init__()
+        assert cout % 2 == 0
+        self.units = cout // 2
+        self.fcn = FCN(cin, self.units)
+        self.T = T
+
+    def forward(self, x, mask):
+        # point-wise feature
+        pwf = self.fcn(x)
+        # locally aggregated feature
+        laf = torch.max(pwf, 1)[0].unsqueeze(1).repeat(1, self.T, 1)
+        # point-wise concat feature
+        pwcf = torch.cat((pwf, laf), dim=2)
+        # apply mask
+        mask = mask.unsqueeze(2).repeat(1, 1, self.units * 2)
+        pwcf = pwcf * mask.float()
+
+        return pwcf
+
+
+# Stacked Voxel Feature Encoding
+class SVFE(nn.Module):
+
+    def __init__(self, T):
+        super(SVFE, self).__init__()
+        self.vfe_1 = VFE(7, 32, T)
+        self.vfe_2 = VFE(32, 128, T)
+        self.fcn = FCN(128, 128)
+
+    def forward(self, x):
+        mask = torch.ne(torch.max(x, 2)[0], 0)
+        x = self.vfe_1(x, mask)
+        x = self.vfe_2(x, mask)
+        x = self.fcn(x)
+        # element-wise max pooling
+        x = torch.max(x, 1)[0]
+        return x
+
+
+# Convolutional Middle Layer
+class CML(nn.Module):
+    def __init__(self):
+        super(CML, self).__init__()
+        self.conv3d_1 = Conv3d(64, 64, 3, s=(2, 1, 1), p=(1, 1, 1))
+        self.conv3d_2 = Conv3d(64, 64, 3, s=(1, 1, 1), p=(0, 1, 1))
+        self.conv3d_3 = Conv3d(64, 64, 3, s=(2, 1, 1), p=(1, 1, 1))
+
+    def forward(self, x):
+        x = self.conv3d_1(x)
+        x = self.conv3d_2(x)
+        x = self.conv3d_3(x)
+        return x
+
+
+# Region Proposal Network
+class RPN(nn.Module):
+    def __init__(self, anchor_num=2):
+        super(RPN, self).__init__()
+        self.anchor_num = anchor_num
+
+        self.block_1 = [Conv2d(128, 128, 3, 2, 1)]
+        self.block_1 += [Conv2d(128, 128, 3, 1, 1) for _ in range(3)]
+        self.block_1 = nn.Sequential(*self.block_1)
+
+        self.block_2 = [Conv2d(128, 128, 3, 2, 1)]
+        self.block_2 += [Conv2d(128, 128, 3, 1, 1) for _ in range(5)]
+        self.block_2 = nn.Sequential(*self.block_2)
+
+        self.block_3 = [Conv2d(128, 256, 3, 2, 1)]
+        self.block_3 += [nn.Conv2d(256, 256, 3, 1, 1) for _ in range(5)]
+        self.block_3 = nn.Sequential(*self.block_3)
+
+        self.deconv_1 = nn.Sequential(nn.ConvTranspose2d(256, 256, 4, 4, 0),
+                                      nn.BatchNorm2d(256))
+        self.deconv_2 = nn.Sequential(nn.ConvTranspose2d(128, 256, 2, 2, 0),
+                                      nn.BatchNorm2d(256))
+        self.deconv_3 = nn.Sequential(nn.ConvTranspose2d(128, 256, 1, 1, 0),
+                                      nn.BatchNorm2d(256))
+
+        self.score_head = Conv2d(768, self.anchor_num, 1, 1, 0,
+                                 activation=False, batch_norm=False)
+        self.reg_head = Conv2d(768, 7 * self.anchor_num, 1, 1, 0,
+                               activation=False, batch_norm=False)
+
+    def forward(self, x):
+        x = self.block_1(x)
+        x_skip_1 = x
+        x = self.block_2(x)
+        x_skip_2 = x
+        x = self.block_3(x)
+        x_0 = self.deconv_1(x)
+        x_1 = self.deconv_2(x_skip_2)
+        x_2 = self.deconv_3(x_skip_1)
+        x = torch.cat((x_0, x_1, x_2), 1)
+        return self.score_head(x), self.reg_head(x)
+
+
+class VoxelNet(nn.Module):
+    def __init__(self, args):
+        super(VoxelNet, self).__init__()
+        self.svfe = PillarVFE(args['pillar_vfe'],
+                              num_point_features=4,
+                              voxel_size=args['voxel_size'],
+                              point_cloud_range=args['lidar_range'])
+
+        # self.svfe = SVFE(args['T'])
+        self.cml = CML()
+        self.rpn = RPN(args['anchor_num'])
+
+        self.N = args['N']
+        self.D = args['D']
+        self.H = args['H']
+        self.W = args['W']
+        self.T = args['T']
+        self.anchor_num = args['anchor_num']
+
+    def voxel_indexing(self, sparse_features, coords):
+        dim = sparse_features.shape[-1]
+
+        dense_feature = Variable(
+            torch.zeros(dim, self.N, self.D, self.H, self.W).cuda())
+
+        dense_feature[:, coords[:, 0], coords[:, 1], coords[:, 2],
+        coords[:, 3]] = sparse_features.transpose(0, 1)
+
+        return dense_feature.transpose(0, 1)
+
+    def forward(self, data_dict):
+        voxel_features = data_dict['processed_lidar']['voxel_features']
+        voxel_coords = data_dict['processed_lidar']['voxel_coords']
+        voxel_num_points = data_dict['processed_lidar']['voxel_num_points']
+
+        batch_dict = {'voxel_features': voxel_features,
+                      'voxel_coords': voxel_coords,
+                      'voxel_num_points': voxel_num_points}
+
+        # feature learning network
+        vwfs = self.svfe(batch_dict)['pillar_features']
+
+        voxel_coords = torch_tensor_to_numpy(voxel_coords)
+        vwfs = self.voxel_indexing(vwfs, voxel_coords)
+
+        # convolutional middle network
+        vwfs = self.cml(vwfs)
+
+        # region proposal network
+
+        # merge the depth and feature dim into one, output probability score
+        # map and regression map
+        psm, rm = self.rpn(vwfs.view(self.N, -1, self.H, self.W))
+
+        output_dict = {'psm': psm,
+                       'rm': rm}
+
+        return output_dict
diff --git a/opencood/opencood/models/voxel_net_intermediate.py b/opencood/opencood/models/voxel_net_intermediate.py
new file mode 100644
index 00000000..db4c1a28
--- /dev/null
+++ b/opencood/opencood/models/voxel_net_intermediate.py
@@ -0,0 +1,183 @@
+# -*- coding: utf-8 -*-
+# Author: Runsheng Xu <rxx3386@ucla.edu>
+# License: TDG-Attribution-NonCommercial-NoDistrib
+
+
+"""
+VoxelNet for intermediate fusion
+"""
+import torch.nn as nn
+import torch.nn.functional as F
+import torch
+import numpy as np
+from torch.autograd import Variable
+
+from opencood.models.voxel_net import RPN, CML
+from opencood.models.sub_modules.pillar_vfe import PillarVFE
+from opencood.utils.common_utils import torch_tensor_to_numpy
+from opencood.models.fuse_modules.self_attn import AttFusion
+from opencood.models.sub_modules.auto_encoder import AutoEncoder
+
+
+# conv2d + bn + relu
+class Conv2d(nn.Module):
+
+    def __init__(self, in_channels, out_channels, k, s, p, activation=True,
+                 batch_norm=True, bias=True):
+        super(Conv2d, self).__init__()
+        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=k,
+                              stride=s, padding=p, bias=bias)
+        if batch_norm:
+            self.bn = nn.BatchNorm2d(out_channels)
+        else:
+            self.bn = None
+        self.activation = activation
+
+    def forward(self, x):
+        x = self.conv(x)
+        if self.bn is not None:
+            x = self.bn(x)
+        if self.activation:
+            return F.relu(x, inplace=True)
+        else:
+            return x
+
+
+class NaiveFusion(nn.Module):
+
+    def __init__(self):
+        super(NaiveFusion, self).__init__()
+        self.conv1 = Conv2d(128 * 5, 256, 3, 1, 1,
+                            batch_norm=False, bias=False)
+        self.conv2 = Conv2d(256, 128, 3, 1, 1)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.conv2(x)
+
+        return x
+
+
+class VoxelNetIntermediate(nn.Module):
+    def __init__(self, args):
+        super(VoxelNetIntermediate, self).__init__()
+        self.svfe = PillarVFE(args['pillar_vfe'],
+                              num_point_features=4,
+                              voxel_size=args['voxel_size'],
+                              point_cloud_range=args['lidar_range'])
+        self.cml = CML()
+        self.fusion_net = AttFusion(128)
+        self.rpn = RPN(args['anchor_num'])
+
+        self.N = args['N']
+        self.D = args['D']
+        self.H = args['H']
+        self.W = args['W']
+        self.T = args['T']
+        self.anchor_num = args['anchor_num']
+
+        self.compression = False
+        if 'compression' in args and args['compression'] > 0:
+            self.compression = True
+            self.compression_layer = AutoEncoder(128, args['compression'])
+
+    def voxel_indexing(self, sparse_features, coords):
+        dim = sparse_features.shape[-1]
+
+        dense_feature = Variable(
+            torch.zeros(dim, self.N, self.D, self.H, self.W).cuda())
+
+        dense_feature[:, coords[:, 0], coords[:, 1], coords[:, 2],
+        coords[:, 3]] = sparse_features.transpose(0, 1)
+
+        return dense_feature.transpose(0, 1)
+
+    def regroup(self, dense_feature, record_len):
+        """
+        Regroup the data based on the record_len.
+
+        Parameters
+        ----------
+        dense_feature : torch.Tensor
+            N, C, H, W
+        record_len : list
+            [sample1_len, sample2_len, ...]
+
+        Returns
+        -------
+        regroup_feature : torch.Tensor
+            B, 5C, H, W
+        """
+        cum_sum_len = list(np.cumsum(record_len))
+        split_features = torch.tensor_split(dense_feature,
+                                            cum_sum_len[:-1])
+        regroup_features = []
+
+        for split_feature in split_features:
+            # M, C, H, W
+            feature_shape = split_feature.shape
+
+            # the maximum M is 5 as most 5 cavs
+            padding_len = 5 - feature_shape[0]
+            padding_tensor = torch.zeros(padding_len, feature_shape[1],
+                                         feature_shape[2], feature_shape[3])
+            padding_tensor = padding_tensor.to(split_feature.device)
+
+            split_feature = torch.cat([split_feature, padding_tensor],
+                                      dim=0)
+
+            # 1, 5C, H, W
+            split_feature = split_feature.view(-1,
+                                               feature_shape[2],
+                                               feature_shape[3]).unsqueeze(0)
+            regroup_features.append(split_feature)
+
+        # B, 5C, H, W
+        regroup_features = torch.cat(regroup_features, dim=0)
+
+        return regroup_features
+
+    def forward(self, data_dict):
+        voxel_features = data_dict['processed_lidar']['voxel_features']
+        voxel_coords = data_dict['processed_lidar']['voxel_coords']
+        voxel_num_points = data_dict['processed_lidar']['voxel_num_points']
+        record_len = data_dict['record_len']
+
+        batch_dict = {'voxel_features': voxel_features,
+                      'voxel_coords': voxel_coords,
+                      'voxel_num_points': voxel_num_points}
+
+        if voxel_coords.is_cuda:
+            record_len_tmp = record_len.cpu()
+
+        record_len_tmp = list(record_len_tmp.numpy())
+
+        self.N = sum(record_len_tmp)
+
+        # feature learning network
+        vwfs = self.svfe(batch_dict)['pillar_features']
+
+        voxel_coords = torch_tensor_to_numpy(voxel_coords)
+        vwfs = self.voxel_indexing(vwfs, voxel_coords)
+
+        # convolutional middle network
+        vwfs = self.cml(vwfs)
+        # convert from 3d to 2d N C H W
+        vmfs = vwfs.view(self.N, -1, self.H, self.W)
+
+        # compression layer
+        if self.compression:
+            vmfs = self.compression_layer(vmfs)
+
+        # information naive fusion
+        vmfs_fusion = self.fusion_net(vmfs, record_len)
+
+        # region proposal network
+        # merge the depth and feature dim into one, output probability score
+        # map and regression map
+        psm, rm = self.rpn(vmfs_fusion)
+
+        output_dict = {'psm': psm,
+                       'rm': rm}
+
+        return output_dict
diff --git a/opencood/opencood/pcdet_utils/__init__.py b/opencood/opencood/pcdet_utils/__init__.py
new file mode 100644
index 00000000..ab71ffb1
--- /dev/null
+++ b/opencood/opencood/pcdet_utils/__init__.py
@@ -0,0 +1,2 @@
+"""This is used to save the utility functions for OpenPCDet
+"""
\ No newline at end of file
diff --git a/opencood/opencood/pcdet_utils/iou3d_nms/iou3d_nms_utils.py b/opencood/opencood/pcdet_utils/iou3d_nms/iou3d_nms_utils.py
new file mode 100644
index 00000000..59812887
--- /dev/null
+++ b/opencood/opencood/pcdet_utils/iou3d_nms/iou3d_nms_utils.py
@@ -0,0 +1,289 @@
+"""
+3D IoU Calculation and Rotated NMS
+Written by Shaoshuai Shi
+All Rights Reserved 2019-2020.
+"""
+import torch
+import numpy as np
+
+from opencood.utils.common_utils import check_numpy_to_torch
+from opencood.pcdet_utils.iou3d_nms import iou3d_nms_cuda
+
+
+def boxes_bev_iou_cpu(boxes_a, boxes_b):
+    """
+    Args:
+        boxes_a: (N, 7) [x, y, z, dx, dy, dz, heading]
+        boxes_b: (N, 7) [x, y, z, dx, dy, dz, heading]
+
+    Returns:
+
+    """
+    boxes_a, is_numpy = check_numpy_to_torch(boxes_a)
+    boxes_b, is_numpy = check_numpy_to_torch(boxes_b)
+    assert not (boxes_a.is_cuda or boxes_b.is_cuda), 'Only support CPU tensors'
+    assert boxes_a.shape[1] == 7 and boxes_b.shape[1] == 7
+    ans_iou = boxes_a.new_zeros(torch.Size((boxes_a.shape[0], boxes_b.shape[0])))
+    iou3d_nms_cuda.boxes_iou_bev_cpu(boxes_a.contiguous(), boxes_b.contiguous(), ans_iou)
+
+    return ans_iou.numpy() if is_numpy else ans_iou
+
+
+def boxes_iou_bev(boxes_a, boxes_b):
+    """
+    Args:
+        boxes_a: (N, 7) [x, y, z, dx, dy, dz, heading]
+        boxes_b: (N, 7) [x, y, z, dx, dy, dz, heading]
+
+    Returns:
+        ans_iou: (N, M)
+    """
+    assert boxes_a.shape[1] == boxes_b.shape[1] == 7
+    ans_iou = torch.cuda.FloatTensor(torch.Size((boxes_a.shape[0], boxes_b.shape[0]))).zero_()
+
+    iou3d_nms_cuda.boxes_iou_bev_gpu(boxes_a.contiguous(), boxes_b.contiguous(), ans_iou)
+
+    return ans_iou
+
+
+def decode_boxes_and_iou3d(boxes_a, boxes_b, pc_range, box_mean, box_std):
+    """
+    Transform the boxes format back to [x, y, z, dx, dy, dz, heading] and calculate iou
+    :param boxes_a: (N, 7) [x_n, y_n, z_n, dx_n, dy_n, dz_n, heading_n] normalized
+    :param boxes_b: (M, 7) [x_n, y_n, z_n, dx_n, dy_n, dz_n, heading_n]
+    :param pc_range: point cloud range
+    :param object_ave_size: average object size
+    :return: ans_iou: (N, M)
+    """
+    boxes_a_dec = decode_boxes(boxes_a, pc_range, box_mean, box_std)
+    boxes_b_dec = decode_boxes(boxes_b, pc_range, box_mean, box_std)
+    iou = boxes_iou3d_gpu(boxes_a_dec, boxes_b_dec)
+
+    return iou
+
+
+def decode_boxes(boxes, pc_range, box_mean, box_std):
+    assert len(boxes.shape)==2
+    assert boxes.shape[1]==8
+    if isinstance(box_mean, list):
+        box_mean = torch.tensor(box_mean, device=boxes.device)
+    if isinstance(box_std, list):
+        box_std = torch.tensor(box_std, device=boxes.device)
+    boxes = boxes * box_std[None, :] + box_mean[None, :]
+    boxes_out = torch.zeros((boxes.shape[0], 7), dtype=boxes.dtype, device=boxes.device)
+    for i in range(3):
+        boxes_out[:, i] = boxes[:, i] * (pc_range[i + 3] - pc_range[i]) + pc_range[i]
+    boxes_out[:, 3:6] = boxes[:, 3:6].exp()
+    boxes_out[:, 6] = torch.atan2(boxes[:, 6], boxes[:, 7])
+    return boxes_out
+
+
+def decode_boxes_and_giou3d(boxes_a, boxes_b, pc_range, box_mean, box_std):
+    boxes_a_dec = decode_boxes(boxes_a, pc_range, box_mean, box_std)
+    boxes_b_dec = decode_boxes(boxes_b, pc_range, box_mean, box_std)
+    corners_a = centroid_to_corners(boxes_a_dec)
+    corners_b = centroid_to_corners(boxes_b_dec)
+    iou, union = boxes_iou3d_gpu(boxes_a_dec, boxes_b_dec, return_union=True)
+    lwh = torch.max(corners_a.max(dim=1)[0][:, None, :], corners_b.max(dim=1)[0]) \
+          -torch.min(corners_a.min(dim=1)[0][:, None, :], corners_b.min(dim=1)[0])
+    volume = lwh[..., 0] * lwh[..., 1] * lwh[..., 2]
+
+    giou = iou - (volume - union) / volume
+
+    return giou
+
+
+def giou3d(boxes_a_dec, boxes_b_dec):
+    corners_a = centroid_to_corners(boxes_a_dec)
+    corners_b = centroid_to_corners(boxes_b_dec)
+    iou, union = boxes_iou3d_gpu(boxes_a_dec, boxes_b_dec, return_union=True)
+    lwh = torch.max(corners_a.max(dim=1)[0][:, None, :], corners_b.max(dim=1)[0]) \
+          -torch.min(corners_a.min(dim=1)[0][:, None, :], corners_b.min(dim=1)[0])
+    volume = lwh[..., 0] * lwh[..., 1] * lwh[..., 2]
+
+    giou = iou - (volume - union) / volume
+
+    return giou
+
+
+def aligned_boxes_iou3d_gpu(boxes_a, boxes_b, return_union=False):
+    """
+    Args:
+        boxes_a: (N, 7) [x, y, z, dx, dy, dz, heading]
+        boxes_b: (N, 7) [x, y, z, dx, dy, dz, heading]
+
+    Returns:
+        ans_iou: (N, 1)
+    """
+    assert boxes_a.shape[1] == boxes_b.shape[1] == 7
+    assert boxes_a.shape[0] == boxes_b.shape[0]
+    # height overlap
+    boxes_a_height_max = (boxes_a[:, 2] + boxes_a[:, 5] / 2).view(-1, 1)
+    boxes_a_height_min = (boxes_a[:, 2] - boxes_a[:, 5] / 2).view(-1, 1)
+    boxes_b_height_max = (boxes_b[:, 2] + boxes_b[:, 5] / 2).view(-1, 1)
+    boxes_b_height_min = (boxes_b[:, 2] - boxes_b[:, 5] / 2).view(-1, 1)
+
+    # bev overlap
+    overlaps_bev = torch.cuda.FloatTensor(torch.Size((boxes_a.shape[0], boxes_b.shape[0]))).zero_()  # (N, M)
+    iou3d_nms_cuda.boxes_overlap_bev_gpu(boxes_a.contiguous(), boxes_b.contiguous(), overlaps_bev)
+    overlaps_bev = torch.diagonal(overlaps_bev).reshape(-1, 1)
+
+    max_of_min = torch.max(boxes_a_height_min, boxes_b_height_min)
+    min_of_max = torch.min(boxes_a_height_max, boxes_b_height_max)
+    overlaps_h = torch.clamp(min_of_max - max_of_min, min=0)
+
+    # 3d iou
+    overlaps_3d = overlaps_bev * overlaps_h
+
+    vol_a = (boxes_a[:, 3] * boxes_a[:, 4] * boxes_a[:, 5]).view(-1, 1)
+    vol_b = (boxes_b[:, 3] * boxes_b[:, 4] * boxes_b[:, 5]).view(-1, 1)
+    union = torch.clamp(vol_a + vol_b - overlaps_3d, min=1e-6)
+    iou3d = overlaps_3d / union
+    if return_union:
+        return iou3d, union
+    return iou3d
+
+
+def boxes_iou3d_gpu(boxes_a, boxes_b, return_union=False):
+    """
+    Args:
+        boxes_a: (N, 7) [x, y, z, dx, dy, dz, heading]
+        boxes_b: (N, 7) [x, y, z, dx, dy, dz, heading]
+
+    Returns:
+        ans_iou: (N, M)
+    """
+    assert boxes_a.shape[1] == boxes_b.shape[1] == 7
+
+    # height overlap
+    boxes_a_height_max = (boxes_a[:, 2] + boxes_a[:, 5] / 2).view(-1, 1)
+    boxes_a_height_min = (boxes_a[:, 2] - boxes_a[:, 5] / 2).view(-1, 1)
+    boxes_b_height_max = (boxes_b[:, 2] + boxes_b[:, 5] / 2).view(1, -1)
+    boxes_b_height_min = (boxes_b[:, 2] - boxes_b[:, 5] / 2).view(1, -1)
+
+    # bev overlap
+    overlaps_bev = torch.cuda.FloatTensor(torch.Size((boxes_a.shape[0], boxes_b.shape[0]))).zero_()  # (N, M)
+    iou3d_nms_cuda.boxes_overlap_bev_gpu(boxes_a.contiguous(), boxes_b.contiguous(), overlaps_bev)
+
+    max_of_min = torch.max(boxes_a_height_min, boxes_b_height_min)
+    min_of_max = torch.min(boxes_a_height_max, boxes_b_height_max)
+    overlaps_h = torch.clamp(min_of_max - max_of_min, min=0)
+
+    # 3d iou
+    overlaps_3d = overlaps_bev * overlaps_h
+
+    vol_a = (boxes_a[:, 3] * boxes_a[:, 4] * boxes_a[:, 5]).view(-1, 1)
+    vol_b = (boxes_b[:, 3] * boxes_b[:, 4] * boxes_b[:, 5]).view(1, -1)
+    union = torch.clamp(vol_a + vol_b - overlaps_3d, min=1e-6)
+    iou3d = overlaps_3d / union
+    if return_union:
+        return iou3d, union
+    return iou3d
+
+
+def centroid_to_corners(boxes):
+    if isinstance(boxes, np.ndarray):
+        corners = _centroid_to_corners_np(boxes)
+    elif isinstance(boxes, torch.Tensor):
+        corners = _centroid_to_corners_torch(boxes)
+    else:
+        raise TypeError('Input boxes should either be numpy array or torch tensor.')
+
+    return corners
+
+
+def _centroid_to_corners_torch(boxes):
+    '''Convert boxes from centroid format to corners
+    :param boxes: [N, 7]
+    :return: corners: [N, 8, 3]
+    '''
+    corners = torch.zeros((boxes.shape[0], 8, 3), dtype=boxes.dtype, device=boxes.device)
+    sin_t = torch.sin(boxes[:, -1])
+    cos_t = torch.cos(boxes[:, -1])
+    corners[:,  ::4, 0] = torch.stack([boxes[:, 0] + boxes[:, 3] / 2 * cos_t - boxes[:, 4] / 2 * sin_t] * 2, dim=1)  # lfx
+    corners[:,  ::4, 1] = torch.stack([boxes[:, 1] + boxes[:, 3] / 2 * sin_t + boxes[:, 4] / 2 * cos_t] * 2, dim=1)  # lfy
+    corners[:, 1::4, 0] = torch.stack([boxes[:, 0] - boxes[:, 3] / 2 * cos_t - boxes[:, 4] / 2 * sin_t] * 2, dim=1)  # lbx
+    corners[:, 1::4, 1] = torch.stack([boxes[:, 1] - boxes[:, 3] / 2 * sin_t + boxes[:, 4] / 2 * cos_t] * 2, dim=1)  # lby
+    corners[:, 2::4, 0] = torch.stack([boxes[:, 0] - boxes[:, 3] / 2 * cos_t + boxes[:, 4] / 2 * sin_t] * 2, dim=1)  # rbx
+    corners[:, 2::4, 1] = torch.stack([boxes[:, 1] - boxes[:, 3] / 2 * sin_t - boxes[:, 4] / 2 * cos_t] * 2, dim=1)  # rby
+    corners[:, 3::4, 0] = torch.stack([boxes[:, 0] + boxes[:, 3] / 2 * cos_t + boxes[:, 4] / 2 * sin_t] * 2, dim=1) # rfx
+    corners[:, 3::4, 1] = torch.stack([boxes[:, 1] + boxes[:, 3] / 2 * sin_t - boxes[:, 4] / 2 * cos_t] * 2, dim=1)  # rfy
+    corners[:, :, 2] = torch.cat([torch.stack([boxes[:, 2] - boxes[:, 5] / 2] * 4, dim=1),
+                                       torch.stack([boxes[:, 2] + boxes[:, 5] / 2] * 4, dim=1)], dim=1)
+
+    return corners
+
+
+def _centroid_to_corners_np(boxes):
+    '''Convert boxes from centroid format to corners
+    :param boxes: [N, 7]
+    :return: corners: [N, 8, 3]
+    '''
+    corners = np.zeros((boxes.shape[0], 8, 3), dtype=boxes.dtype)
+    sin_t = np.sin(boxes[:, -1])
+    cos_t = np.cos(boxes[:, -1])
+    corners[:,  ::4, 0] = np.stack([boxes[:, 0] + boxes[:, 3] / 2 * cos_t - boxes[:, 4] / 2 * sin_t] * 2, axis=1)  # lfx
+    corners[:,  ::4, 1] = np.stack([boxes[:, 1] + boxes[:, 3] / 2 * sin_t + boxes[:, 4] / 2 * cos_t] * 2, axis=1)  # lfy
+    corners[:, 1::4, 0] = np.stack([boxes[:, 0] - boxes[:, 3] / 2 * cos_t - boxes[:, 4] / 2 * sin_t] * 2, axis=1)  # lbx
+    corners[:, 1::4, 1] = np.stack([boxes[:, 1] - boxes[:, 3] / 2 * sin_t + boxes[:, 4] / 2 * cos_t] * 2, axis=1)  # lby
+    corners[:, 2::4, 0] = np.stack([boxes[:, 0] - boxes[:, 3] / 2 * cos_t + boxes[:, 4] / 2 * sin_t] * 2, axis=1)  # rbx
+    corners[:, 2::4, 1] = np.stack([boxes[:, 1] - boxes[:, 3] / 2 * sin_t - boxes[:, 4] / 2 * cos_t] * 2, axis=1)  # rby
+    corners[:, 3::4, 0] = np.stack([boxes[:, 0] + boxes[:, 3] / 2 * cos_t + boxes[:, 4] / 2 * sin_t] * 2, axis=1) # rfx
+    corners[:, 3::4, 1] = np.stack([boxes[:, 1] + boxes[:, 3] / 2 * sin_t - boxes[:, 4] / 2 * cos_t] * 2, axis=1)  # rfy
+    corners[:, :, 2] = np.concatenate([np.stack([boxes[:, 2] - boxes[:, 5] / 2] * 4, axis=1),
+                                       np.stack([boxes[:, 2] + boxes[:, 5] / 2] * 4, axis=1)], axis=1)
+
+    return corners
+
+def rotate_weighted_nms_gpu(
+    box_preds,
+    rbboxes,
+    dir_labels,
+    labels_preds,
+    scores,
+    iou_preds,
+    anchors,
+    pre_max_size=None,
+    post_max_size=None,
+    iou_threshold=0.5,
+):
+    """Original definition can be found in CIA_SSD paper"""
+    if pre_max_size is not None:
+        num_keeped_scores = scores.shape[0]
+
+
+def nms_gpu(boxes, scores, thresh, pre_maxsize=None, **kwargs):
+    """
+    Operate on rotated bev boxes[x,y,dx,dy,heading]
+    :param boxes: (N, 7) [x, y, z, dx, dy, dz, heading]
+    :param scores: (N)
+    :param thresh:
+    :return:
+    """
+    assert boxes.shape[1] == 7
+    order = scores.sort(0, descending=True)[1]
+    if pre_maxsize is not None:
+        order = order[:pre_maxsize]
+
+    boxes = boxes[order].contiguous()
+    keep = torch.LongTensor(boxes.size(0))
+    num_out = iou3d_nms_cuda.nms_gpu(boxes, keep, thresh)
+    return order[keep[:num_out].cuda()].contiguous(), None
+
+
+def nms_normal_gpu(boxes, scores, thresh, **kwargs):
+    """
+    Ignore heading and operate on bev boxes[x,y,dx,dy]
+    :param boxes: (N, 7) [x, y, z, dx, dy, dz, heading]
+    :param scores: (N)
+    :param thresh:
+    :return:
+    """
+    assert boxes.shape[1] == 7
+    order = scores.sort(0, descending=True)[1]
+
+    boxes = boxes[order].contiguous()
+
+    keep = torch.LongTensor(boxes.size(0))
+    num_out = iou3d_nms_cuda.nms_normal_gpu(boxes, keep, thresh)
+    return order[keep[:num_out].cuda()].contiguous(), None
diff --git a/opencood/opencood/pcdet_utils/iou3d_nms/src/iou3d_cpu.cpp b/opencood/opencood/pcdet_utils/iou3d_nms/src/iou3d_cpu.cpp
new file mode 100755
index 00000000..d528ad91
--- /dev/null
+++ b/opencood/opencood/pcdet_utils/iou3d_nms/src/iou3d_cpu.cpp
@@ -0,0 +1,252 @@
+/*
+3D Rotated IoU Calculation (CPU)
+Written by Shaoshuai Shi
+All Rights Reserved 2020.
+*/
+
+#include <stdio.h>
+#include <math.h>
+#include <torch/serialize/tensor.h>
+#include <torch/extension.h>
+#include <vector>
+#include <cuda.h>
+#include <cuda_runtime_api.h>
+#include "iou3d_cpu.h"
+
+#define CHECK_CUDA(x) do { \
+  if (!x.type().is_cuda()) { \
+    fprintf(stderr, "%s must be CUDA tensor at %s:%d\n", #x, __FILE__, __LINE__); \
+    exit(-1); \
+  } \
+} while (0)
+#define CHECK_CONTIGUOUS(x) do { \
+  if (!x.is_contiguous()) { \
+    fprintf(stderr, "%s must be contiguous tensor at %s:%d\n", #x, __FILE__, __LINE__); \
+    exit(-1); \
+  } \
+} while (0)
+#define CHECK_INPUT(x) CHECK_CUDA(x);CHECK_CONTIGUOUS(x)
+
+inline float min(float a, float b){
+    return a > b ? b : a;
+}
+
+inline float max(float a, float b){
+    return a > b ? a : b;
+}
+
+const float EPS = 1e-8;
+struct Point {
+    float x, y;
+    __device__ Point() {}
+    __device__ Point(double _x, double _y){
+        x = _x, y = _y;
+    }
+
+    __device__ void set(float _x, float _y){
+        x = _x; y = _y;
+    }
+
+    __device__ Point operator +(const Point &b)const{
+        return Point(x + b.x, y + b.y);
+    }
+
+    __device__ Point operator -(const Point &b)const{
+        return Point(x - b.x, y - b.y);
+    }
+};
+
+inline float cross(const Point &a, const Point &b){
+    return a.x * b.y - a.y * b.x;
+}
+
+inline float cross(const Point &p1, const Point &p2, const Point &p0){
+    return (p1.x - p0.x) * (p2.y - p0.y) - (p2.x - p0.x) * (p1.y - p0.y);
+}
+
+inline int check_rect_cross(const Point &p1, const Point &p2, const Point &q1, const Point &q2){
+    int ret = min(p1.x,p2.x) <= max(q1.x,q2.x)  &&
+              min(q1.x,q2.x) <= max(p1.x,p2.x) &&
+              min(p1.y,p2.y) <= max(q1.y,q2.y) &&
+              min(q1.y,q2.y) <= max(p1.y,p2.y);
+    return ret;
+}
+
+inline int check_in_box2d(const float *box, const Point &p){
+    //params: (7) [x, y, z, dx, dy, dz, heading]
+    const float MARGIN = 1e-2;
+
+    float center_x = box[0], center_y = box[1];
+    float angle_cos = cos(-box[6]), angle_sin = sin(-box[6]);  // rotate the point in the opposite direction of box
+    float rot_x = (p.x - center_x) * angle_cos + (p.y - center_y) * (-angle_sin);
+    float rot_y = (p.x - center_x) * angle_sin + (p.y - center_y) * angle_cos;
+
+    return (fabs(rot_x) < box[3] / 2 + MARGIN && fabs(rot_y) < box[4] / 2 + MARGIN);
+}
+
+inline int intersection(const Point &p1, const Point &p0, const Point &q1, const Point &q0, Point &ans){
+    // fast exclusion
+    if (check_rect_cross(p0, p1, q0, q1) == 0) return 0;
+
+    // check cross standing
+    float s1 = cross(q0, p1, p0);
+    float s2 = cross(p1, q1, p0);
+    float s3 = cross(p0, q1, q0);
+    float s4 = cross(q1, p1, q0);
+
+    if (!(s1 * s2 > 0 && s3 * s4 > 0)) return 0;
+
+    // calculate intersection of two lines
+    float s5 = cross(q1, p1, p0);
+    if(fabs(s5 - s1) > EPS){
+        ans.x = (s5 * q0.x - s1 * q1.x) / (s5 - s1);
+        ans.y = (s5 * q0.y - s1 * q1.y) / (s5 - s1);
+
+    }
+    else{
+        float a0 = p0.y - p1.y, b0 = p1.x - p0.x, c0 = p0.x * p1.y - p1.x * p0.y;
+        float a1 = q0.y - q1.y, b1 = q1.x - q0.x, c1 = q0.x * q1.y - q1.x * q0.y;
+        float D = a0 * b1 - a1 * b0;
+
+        ans.x = (b0 * c1 - b1 * c0) / D;
+        ans.y = (a1 * c0 - a0 * c1) / D;
+    }
+
+    return 1;
+}
+
+inline void rotate_around_center(const Point &center, const float angle_cos, const float angle_sin, Point &p){
+    float new_x = (p.x - center.x) * angle_cos + (p.y - center.y) * (-angle_sin) + center.x;
+    float new_y = (p.x - center.x) * angle_sin + (p.y - center.y) * angle_cos + center.y;
+    p.set(new_x, new_y);
+}
+
+inline int point_cmp(const Point &a, const Point &b, const Point &center){
+    return atan2(a.y - center.y, a.x - center.x) > atan2(b.y - center.y, b.x - center.x);
+}
+
+inline float box_overlap(const float *box_a, const float *box_b){
+    // params: box_a (7) [x, y, z, dx, dy, dz, heading]
+    // params: box_b (7) [x, y, z, dx, dy, dz, heading]
+
+//    float a_x1 = box_a[0], a_y1 = box_a[1], a_x2 = box_a[2], a_y2 = box_a[3], a_angle = box_a[4];
+//    float b_x1 = box_b[0], b_y1 = box_b[1], b_x2 = box_b[2], b_y2 = box_b[3], b_angle = box_b[4];
+    float a_angle = box_a[6], b_angle = box_b[6];
+    float a_dx_half = box_a[3] / 2, b_dx_half = box_b[3] / 2, a_dy_half = box_a[4] / 2, b_dy_half = box_b[4] / 2;
+    float a_x1 = box_a[0] - a_dx_half, a_y1 = box_a[1] - a_dy_half;
+    float a_x2 = box_a[0] + a_dx_half, a_y2 = box_a[1] + a_dy_half;
+    float b_x1 = box_b[0] - b_dx_half, b_y1 = box_b[1] - b_dy_half;
+    float b_x2 = box_b[0] + b_dx_half, b_y2 = box_b[1] + b_dy_half;
+
+    Point center_a(box_a[0], box_a[1]);
+    Point center_b(box_b[0], box_b[1]);
+
+    Point box_a_corners[5];
+    box_a_corners[0].set(a_x1, a_y1);
+    box_a_corners[1].set(a_x2, a_y1);
+    box_a_corners[2].set(a_x2, a_y2);
+    box_a_corners[3].set(a_x1, a_y2);
+
+    Point box_b_corners[5];
+    box_b_corners[0].set(b_x1, b_y1);
+    box_b_corners[1].set(b_x2, b_y1);
+    box_b_corners[2].set(b_x2, b_y2);
+    box_b_corners[3].set(b_x1, b_y2);
+
+    // get oriented corners
+    float a_angle_cos = cos(a_angle), a_angle_sin = sin(a_angle);
+    float b_angle_cos = cos(b_angle), b_angle_sin = sin(b_angle);
+
+    for (int k = 0; k < 4; k++){
+        rotate_around_center(center_a, a_angle_cos, a_angle_sin, box_a_corners[k]);
+        rotate_around_center(center_b, b_angle_cos, b_angle_sin, box_b_corners[k]);
+    }
+
+    box_a_corners[4] = box_a_corners[0];
+    box_b_corners[4] = box_b_corners[0];
+
+    // get intersection of lines
+    Point cross_points[16];
+    Point poly_center;
+    int cnt = 0, flag = 0;
+
+    poly_center.set(0, 0);
+    for (int i = 0; i < 4; i++){
+        for (int j = 0; j < 4; j++){
+            flag = intersection(box_a_corners[i + 1], box_a_corners[i], box_b_corners[j + 1], box_b_corners[j], cross_points[cnt]);
+            if (flag){
+                poly_center = poly_center + cross_points[cnt];
+                cnt++;
+            }
+        }
+    }
+
+    // check corners
+    for (int k = 0; k < 4; k++){
+        if (check_in_box2d(box_a, box_b_corners[k])){
+            poly_center = poly_center + box_b_corners[k];
+            cross_points[cnt] = box_b_corners[k];
+            cnt++;
+        }
+        if (check_in_box2d(box_b, box_a_corners[k])){
+            poly_center = poly_center + box_a_corners[k];
+            cross_points[cnt] = box_a_corners[k];
+            cnt++;
+        }
+    }
+
+    poly_center.x /= cnt;
+    poly_center.y /= cnt;
+
+    // sort the points of polygon
+    Point temp;
+    for (int j = 0; j < cnt - 1; j++){
+        for (int i = 0; i < cnt - j - 1; i++){
+            if (point_cmp(cross_points[i], cross_points[i + 1], poly_center)){
+                temp = cross_points[i];
+                cross_points[i] = cross_points[i + 1];
+                cross_points[i + 1] = temp;
+            }
+        }
+    }
+
+    // get the overlap areas
+    float area = 0;
+    for (int k = 0; k < cnt - 1; k++){
+        area += cross(cross_points[k] - cross_points[0], cross_points[k + 1] - cross_points[0]);
+    }
+
+    return fabs(area) / 2.0;
+}
+
+inline float iou_bev(const float *box_a, const float *box_b){
+    // params: box_a (7) [x, y, z, dx, dy, dz, heading]
+    // params: box_b (7) [x, y, z, dx, dy, dz, heading]
+    float sa = box_a[3] * box_a[4];
+    float sb = box_b[3] * box_b[4];
+    float s_overlap = box_overlap(box_a, box_b);
+    return s_overlap / fmaxf(sa + sb - s_overlap, EPS);
+}
+
+
+int boxes_iou_bev_cpu(at::Tensor boxes_a_tensor, at::Tensor boxes_b_tensor, at::Tensor ans_iou_tensor){
+    // params boxes_a_tensor: (N, 7) [x, y, z, dx, dy, dz, heading]
+    // params boxes_b_tensor: (M, 7) [x, y, z, dx, dy, dz, heading]
+    // params ans_iou_tensor: (N, M)
+
+    CHECK_CONTIGUOUS(boxes_a_tensor);
+    CHECK_CONTIGUOUS(boxes_b_tensor);
+
+    int num_boxes_a = boxes_a_tensor.size(0);
+    int num_boxes_b = boxes_b_tensor.size(0);
+    const float *boxes_a = boxes_a_tensor.data<float>();
+    const float *boxes_b = boxes_b_tensor.data<float>();
+    float *ans_iou = ans_iou_tensor.data<float>();
+
+    for (int i = 0; i < num_boxes_a; i++){
+        for (int j = 0; j < num_boxes_b; j++){
+            ans_iou[i * num_boxes_b + j] = iou_bev(boxes_a + i * 7, boxes_b + j * 7);
+        }
+    }
+    return 1;
+}
diff --git a/opencood/opencood/pcdet_utils/iou3d_nms/src/iou3d_cpu.h b/opencood/opencood/pcdet_utils/iou3d_nms/src/iou3d_cpu.h
new file mode 100755
index 00000000..8835ee70
--- /dev/null
+++ b/opencood/opencood/pcdet_utils/iou3d_nms/src/iou3d_cpu.h
@@ -0,0 +1,11 @@
+#ifndef IOU3D_CPU_H
+#define IOU3D_CPU_H
+
+#include <torch/serialize/tensor.h>
+#include <vector>
+#include <cuda.h>
+#include <cuda_runtime_api.h>
+
+int boxes_iou_bev_cpu(at::Tensor boxes_a_tensor, at::Tensor boxes_b_tensor, at::Tensor ans_iou_tensor);
+
+#endif
diff --git a/opencood/opencood/pcdet_utils/iou3d_nms/src/iou3d_nms.cpp b/opencood/opencood/pcdet_utils/iou3d_nms/src/iou3d_nms.cpp
new file mode 100755
index 00000000..d41da8ad
--- /dev/null
+++ b/opencood/opencood/pcdet_utils/iou3d_nms/src/iou3d_nms.cpp
@@ -0,0 +1,188 @@
+/*
+3D IoU Calculation and Rotated NMS(modified from 2D NMS written by others)
+Written by Shaoshuai Shi
+All Rights Reserved 2019-2020.
+*/
+
+#include <torch/serialize/tensor.h>
+#include <torch/extension.h>
+#include <vector>
+#include <cuda.h>
+#include <cuda_runtime_api.h>
+#include "iou3d_nms.h"
+
+#define CHECK_CUDA(x) do { \
+  if (!x.type().is_cuda()) { \
+    fprintf(stderr, "%s must be CUDA tensor at %s:%d\n", #x, __FILE__, __LINE__); \
+    exit(-1); \
+  } \
+} while (0)
+#define CHECK_CONTIGUOUS(x) do { \
+  if (!x.is_contiguous()) { \
+    fprintf(stderr, "%s must be contiguous tensor at %s:%d\n", #x, __FILE__, __LINE__); \
+    exit(-1); \
+  } \
+} while (0)
+#define CHECK_INPUT(x) CHECK_CUDA(x);CHECK_CONTIGUOUS(x)
+
+#define DIVUP(m,n) ((m) / (n) + ((m) % (n) > 0))
+
+#define CHECK_ERROR(ans) { gpuAssert((ans), __FILE__, __LINE__); }
+inline void gpuAssert(cudaError_t code, const char *file, int line, bool abort=true)
+{
+   if (code != cudaSuccess)
+   {
+      fprintf(stderr,"GPUassert: %s %s %d\n", cudaGetErrorString(code), file, line);
+      if (abort) exit(code);
+   }
+}
+
+const int THREADS_PER_BLOCK_NMS = sizeof(unsigned long long) * 8;
+
+
+void boxesoverlapLauncher(const int num_a, const float *boxes_a, const int num_b, const float *boxes_b, float *ans_overlap);
+void boxesioubevLauncher(const int num_a, const float *boxes_a, const int num_b, const float *boxes_b, float *ans_iou);
+void nmsLauncher(const float *boxes, unsigned long long * mask, int boxes_num, float nms_overlap_thresh);
+void nmsNormalLauncher(const float *boxes, unsigned long long * mask, int boxes_num, float nms_overlap_thresh);
+
+
+int boxes_overlap_bev_gpu(at::Tensor boxes_a, at::Tensor boxes_b, at::Tensor ans_overlap){
+    // params boxes_a: (N, 7) [x, y, z, dx, dy, dz, heading]
+    // params boxes_b: (M, 7) [x, y, z, dx, dy, dz, heading]
+    // params ans_overlap: (N, M)
+
+    CHECK_INPUT(boxes_a);
+    CHECK_INPUT(boxes_b);
+    CHECK_INPUT(ans_overlap);
+
+    int num_a = boxes_a.size(0);
+    int num_b = boxes_b.size(0);
+
+    const float * boxes_a_data = boxes_a.data<float>();
+    const float * boxes_b_data = boxes_b.data<float>();
+    float * ans_overlap_data = ans_overlap.data<float>();
+
+    boxesoverlapLauncher(num_a, boxes_a_data, num_b, boxes_b_data, ans_overlap_data);
+
+    return 1;
+}
+
+int boxes_iou_bev_gpu(at::Tensor boxes_a, at::Tensor boxes_b, at::Tensor ans_iou){
+    // params boxes_a: (N, 7) [x, y, z, dx, dy, dz, heading]
+    // params boxes_b: (M, 7) [x, y, z, dx, dy, dz, heading]
+    // params ans_overlap: (N, M)
+    CHECK_INPUT(boxes_a);
+    CHECK_INPUT(boxes_b);
+    CHECK_INPUT(ans_iou);
+
+    int num_a = boxes_a.size(0);
+    int num_b = boxes_b.size(0);
+
+    const float * boxes_a_data = boxes_a.data<float>();
+    const float * boxes_b_data = boxes_b.data<float>();
+    float * ans_iou_data = ans_iou.data<float>();
+
+    boxesioubevLauncher(num_a, boxes_a_data, num_b, boxes_b_data, ans_iou_data);
+
+    return 1;
+}
+
+int nms_gpu(at::Tensor boxes, at::Tensor keep, float nms_overlap_thresh){
+    // params boxes: (N, 7) [x, y, z, dx, dy, dz, heading]
+    // params keep: (N)
+    CHECK_INPUT(boxes);
+    CHECK_CONTIGUOUS(keep);
+
+    int boxes_num = boxes.size(0);
+    const float * boxes_data = boxes.data<float>();
+    long * keep_data = keep.data<long>();
+
+    const int col_blocks = DIVUP(boxes_num, THREADS_PER_BLOCK_NMS);
+
+    unsigned long long *mask_data = NULL;
+    CHECK_ERROR(cudaMalloc((void**)&mask_data, boxes_num * col_blocks * sizeof(unsigned long long)));
+    nmsLauncher(boxes_data, mask_data, boxes_num, nms_overlap_thresh);
+
+    // unsigned long long mask_cpu[boxes_num * col_blocks];
+    // unsigned long long *mask_cpu = new unsigned long long [boxes_num * col_blocks];
+    std::vector<unsigned long long> mask_cpu(boxes_num * col_blocks);
+
+//    printf("boxes_num=%d, col_blocks=%d\n", boxes_num, col_blocks);
+    CHECK_ERROR(cudaMemcpy(&mask_cpu[0], mask_data, boxes_num * col_blocks * sizeof(unsigned long long),
+                           cudaMemcpyDeviceToHost));
+
+    cudaFree(mask_data);
+
+    unsigned long long remv_cpu[col_blocks];
+    memset(remv_cpu, 0, col_blocks * sizeof(unsigned long long));
+
+    int num_to_keep = 0;
+
+    for (int i = 0; i < boxes_num; i++){
+        int nblock = i / THREADS_PER_BLOCK_NMS;
+        int inblock = i % THREADS_PER_BLOCK_NMS;
+
+        if (!(remv_cpu[nblock] & (1ULL << inblock))){
+            keep_data[num_to_keep++] = i;
+            unsigned long long *p = &mask_cpu[0] + i * col_blocks;
+            for (int j = nblock; j < col_blocks; j++){
+                remv_cpu[j] |= p[j];
+            }
+        }
+    }
+    if ( cudaSuccess != cudaGetLastError() ) printf( "Error!\n" );
+
+    return num_to_keep;
+}
+
+
+int nms_normal_gpu(at::Tensor boxes, at::Tensor keep, float nms_overlap_thresh){
+    // params boxes: (N, 7) [x, y, z, dx, dy, dz, heading]
+    // params keep: (N)
+
+    CHECK_INPUT(boxes);
+    CHECK_CONTIGUOUS(keep);
+
+    int boxes_num = boxes.size(0);
+    const float * boxes_data = boxes.data<float>();
+    long * keep_data = keep.data<long>();
+
+    const int col_blocks = DIVUP(boxes_num, THREADS_PER_BLOCK_NMS);
+
+    unsigned long long *mask_data = NULL;
+    CHECK_ERROR(cudaMalloc((void**)&mask_data, boxes_num * col_blocks * sizeof(unsigned long long)));
+    nmsNormalLauncher(boxes_data, mask_data, boxes_num, nms_overlap_thresh);
+
+    // unsigned long long mask_cpu[boxes_num * col_blocks];
+    // unsigned long long *mask_cpu = new unsigned long long [boxes_num * col_blocks];
+    std::vector<unsigned long long> mask_cpu(boxes_num * col_blocks);
+
+//    printf("boxes_num=%d, col_blocks=%d\n", boxes_num, col_blocks);
+    CHECK_ERROR(cudaMemcpy(&mask_cpu[0], mask_data, boxes_num * col_blocks * sizeof(unsigned long long),
+                           cudaMemcpyDeviceToHost));
+
+    cudaFree(mask_data);
+
+    unsigned long long remv_cpu[col_blocks];
+    memset(remv_cpu, 0, col_blocks * sizeof(unsigned long long));
+
+    int num_to_keep = 0;
+
+    for (int i = 0; i < boxes_num; i++){
+        int nblock = i / THREADS_PER_BLOCK_NMS;
+        int inblock = i % THREADS_PER_BLOCK_NMS;
+
+        if (!(remv_cpu[nblock] & (1ULL << inblock))){
+            keep_data[num_to_keep++] = i;
+            unsigned long long *p = &mask_cpu[0] + i * col_blocks;
+            for (int j = nblock; j < col_blocks; j++){
+                remv_cpu[j] |= p[j];
+            }
+        }
+    }
+    if ( cudaSuccess != cudaGetLastError() ) printf( "Error!\n" );
+
+    return num_to_keep;
+}
+
+
diff --git a/opencood/opencood/pcdet_utils/iou3d_nms/src/iou3d_nms.h b/opencood/opencood/pcdet_utils/iou3d_nms/src/iou3d_nms.h
new file mode 100755
index 00000000..aa7ae0ed
--- /dev/null
+++ b/opencood/opencood/pcdet_utils/iou3d_nms/src/iou3d_nms.h
@@ -0,0 +1,14 @@
+#ifndef IOU3D_NMS_H
+#define IOU3D_NMS_H
+
+#include <torch/serialize/tensor.h>
+#include <vector>
+#include <cuda.h>
+#include <cuda_runtime_api.h>
+
+int boxes_overlap_bev_gpu(at::Tensor boxes_a, at::Tensor boxes_b, at::Tensor ans_overlap);
+int boxes_iou_bev_gpu(at::Tensor boxes_a, at::Tensor boxes_b, at::Tensor ans_iou);
+int nms_gpu(at::Tensor boxes, at::Tensor keep, float nms_overlap_thresh);
+int nms_normal_gpu(at::Tensor boxes, at::Tensor keep, float nms_overlap_thresh);
+
+#endif
diff --git a/opencood/opencood/pcdet_utils/iou3d_nms/src/iou3d_nms_api.cpp b/opencood/opencood/pcdet_utils/iou3d_nms/src/iou3d_nms_api.cpp
new file mode 100755
index 00000000..5a2d3a37
--- /dev/null
+++ b/opencood/opencood/pcdet_utils/iou3d_nms/src/iou3d_nms_api.cpp
@@ -0,0 +1,17 @@
+#include <torch/serialize/tensor.h>
+#include <torch/extension.h>
+#include <vector>
+#include <cuda.h>
+#include <cuda_runtime_api.h>
+
+#include "iou3d_cpu.h"
+#include "iou3d_nms.h"
+
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+	m.def("boxes_overlap_bev_gpu", &boxes_overlap_bev_gpu, "oriented boxes overlap");
+	m.def("boxes_iou_bev_gpu", &boxes_iou_bev_gpu, "oriented boxes iou");
+	m.def("nms_gpu", &nms_gpu, "oriented nms gpu");
+	m.def("nms_normal_gpu", &nms_normal_gpu, "nms gpu");
+	m.def("boxes_iou_bev_cpu", &boxes_iou_bev_cpu, "oriented boxes iou");
+}
diff --git a/opencood/opencood/pcdet_utils/iou3d_nms/src/iou3d_nms_kernel.cu b/opencood/opencood/pcdet_utils/iou3d_nms/src/iou3d_nms_kernel.cu
new file mode 100755
index 00000000..e5e305cd
--- /dev/null
+++ b/opencood/opencood/pcdet_utils/iou3d_nms/src/iou3d_nms_kernel.cu
@@ -0,0 +1,414 @@
+/*
+3D IoU Calculation and Rotated NMS(modified from 2D NMS written by others)
+Written by Shaoshuai Shi
+All Rights Reserved 2019-2020.
+*/
+
+
+#include <stdio.h>
+#define THREADS_PER_BLOCK 16
+#define DIVUP(m, n) ((m) / (n) + ((m) % (n) > 0))
+
+// #define DEBUG
+const int THREADS_PER_BLOCK_NMS = sizeof(unsigned long long) * 8;
+const float EPS = 1e-8;
+struct Point {
+    float x, y;
+    __device__ Point() {}
+    __device__ Point(double _x, double _y){
+        x = _x, y = _y;
+    }
+
+    __device__ void set(float _x, float _y){
+        x = _x; y = _y;
+    }
+
+    __device__ Point operator +(const Point &b)const{
+        return Point(x + b.x, y + b.y);
+    }
+
+    __device__ Point operator -(const Point &b)const{
+        return Point(x - b.x, y - b.y);
+    }
+};
+
+__device__ inline float cross(const Point &a, const Point &b){
+    return a.x * b.y - a.y * b.x;
+}
+
+__device__ inline float cross(const Point &p1, const Point &p2, const Point &p0){
+    return (p1.x - p0.x) * (p2.y - p0.y) - (p2.x - p0.x) * (p1.y - p0.y);
+}
+
+__device__ int check_rect_cross(const Point &p1, const Point &p2, const Point &q1, const Point &q2){
+    int ret = min(p1.x,p2.x) <= max(q1.x,q2.x)  &&
+              min(q1.x,q2.x) <= max(p1.x,p2.x) &&
+              min(p1.y,p2.y) <= max(q1.y,q2.y) &&
+              min(q1.y,q2.y) <= max(p1.y,p2.y);
+    return ret;
+}
+
+__device__ inline int check_in_box2d(const float *box, const Point &p){
+    //params: (7) [x, y, z, dx, dy, dz, heading]
+    const float MARGIN = 1e-2;
+
+    float center_x = box[0], center_y = box[1];
+    float angle_cos = cos(-box[6]), angle_sin = sin(-box[6]);  // rotate the point in the opposite direction of box
+    float rot_x = (p.x - center_x) * angle_cos + (p.y - center_y) * (-angle_sin);
+    float rot_y = (p.x - center_x) * angle_sin + (p.y - center_y) * angle_cos;
+
+    return (fabs(rot_x) < box[3] / 2 + MARGIN && fabs(rot_y) < box[4] / 2 + MARGIN);
+}
+
+__device__ inline int intersection(const Point &p1, const Point &p0, const Point &q1, const Point &q0, Point &ans){
+    // fast exclusion
+    if (check_rect_cross(p0, p1, q0, q1) == 0) return 0;
+
+    // check cross standing
+    float s1 = cross(q0, p1, p0);
+    float s2 = cross(p1, q1, p0);
+    float s3 = cross(p0, q1, q0);
+    float s4 = cross(q1, p1, q0);
+
+    if (!(s1 * s2 > 0 && s3 * s4 > 0)) return 0;
+
+    // calculate intersection of two lines
+    float s5 = cross(q1, p1, p0);
+    if(fabs(s5 - s1) > EPS){
+        ans.x = (s5 * q0.x - s1 * q1.x) / (s5 - s1);
+        ans.y = (s5 * q0.y - s1 * q1.y) / (s5 - s1);
+
+    }
+    else{
+        float a0 = p0.y - p1.y, b0 = p1.x - p0.x, c0 = p0.x * p1.y - p1.x * p0.y;
+        float a1 = q0.y - q1.y, b1 = q1.x - q0.x, c1 = q0.x * q1.y - q1.x * q0.y;
+        float D = a0 * b1 - a1 * b0;
+
+        ans.x = (b0 * c1 - b1 * c0) / D;
+        ans.y = (a1 * c0 - a0 * c1) / D;
+    }
+
+    return 1;
+}
+
+__device__ inline void rotate_around_center(const Point &center, const float angle_cos, const float angle_sin, Point &p){
+    float new_x = (p.x - center.x) * angle_cos + (p.y - center.y) * (-angle_sin) + center.x;
+    float new_y = (p.x - center.x) * angle_sin + (p.y - center.y) * angle_cos + center.y;
+    p.set(new_x, new_y);
+}
+
+__device__ inline int point_cmp(const Point &a, const Point &b, const Point &center){
+    return atan2(a.y - center.y, a.x - center.x) > atan2(b.y - center.y, b.x - center.x);
+}
+
+__device__ inline float box_overlap(const float *box_a, const float *box_b){
+    // params box_a: [x, y, z, dx, dy, dz, heading]
+    // params box_b: [x, y, z, dx, dy, dz, heading]
+
+    float a_angle = box_a[6], b_angle = box_b[6];
+    float a_dx_half = box_a[3] / 2, b_dx_half = box_b[3] / 2, a_dy_half = box_a[4] / 2, b_dy_half = box_b[4] / 2;
+    float a_x1 = box_a[0] - a_dx_half, a_y1 = box_a[1] - a_dy_half;
+    float a_x2 = box_a[0] + a_dx_half, a_y2 = box_a[1] + a_dy_half;
+    float b_x1 = box_b[0] - b_dx_half, b_y1 = box_b[1] - b_dy_half;
+    float b_x2 = box_b[0] + b_dx_half, b_y2 = box_b[1] + b_dy_half;
+
+    Point center_a(box_a[0], box_a[1]);
+    Point center_b(box_b[0], box_b[1]);
+
+#ifdef DEBUG
+    printf("a: (%.3f, %.3f, %.3f, %.3f, %.3f), b: (%.3f, %.3f, %.3f, %.3f, %.3f)\n", a_x1, a_y1, a_x2, a_y2, a_angle,
+           b_x1, b_y1, b_x2, b_y2, b_angle);
+    printf("center a: (%.3f, %.3f), b: (%.3f, %.3f)\n", center_a.x, center_a.y, center_b.x, center_b.y);
+#endif
+
+    Point box_a_corners[5];
+    box_a_corners[0].set(a_x1, a_y1);
+    box_a_corners[1].set(a_x2, a_y1);
+    box_a_corners[2].set(a_x2, a_y2);
+    box_a_corners[3].set(a_x1, a_y2);
+
+    Point box_b_corners[5];
+    box_b_corners[0].set(b_x1, b_y1);
+    box_b_corners[1].set(b_x2, b_y1);
+    box_b_corners[2].set(b_x2, b_y2);
+    box_b_corners[3].set(b_x1, b_y2);
+
+    // get oriented corners
+    float a_angle_cos = cos(a_angle), a_angle_sin = sin(a_angle);
+    float b_angle_cos = cos(b_angle), b_angle_sin = sin(b_angle);
+
+    for (int k = 0; k < 4; k++){
+#ifdef DEBUG
+        printf("before corner %d: a(%.3f, %.3f), b(%.3f, %.3f) \n", k, box_a_corners[k].x, box_a_corners[k].y, box_b_corners[k].x, box_b_corners[k].y);
+#endif
+        rotate_around_center(center_a, a_angle_cos, a_angle_sin, box_a_corners[k]);
+        rotate_around_center(center_b, b_angle_cos, b_angle_sin, box_b_corners[k]);
+#ifdef DEBUG
+        printf("corner %d: a(%.3f, %.3f), b(%.3f, %.3f) \n", k, box_a_corners[k].x, box_a_corners[k].y, box_b_corners[k].x, box_b_corners[k].y);
+#endif
+    }
+
+    box_a_corners[4] = box_a_corners[0];
+    box_b_corners[4] = box_b_corners[0];
+
+    // get intersection of lines
+    Point cross_points[16];
+    Point poly_center;
+    int cnt = 0, flag = 0;
+
+    poly_center.set(0, 0);
+    for (int i = 0; i < 4; i++){
+        for (int j = 0; j < 4; j++){
+            flag = intersection(box_a_corners[i + 1], box_a_corners[i], box_b_corners[j + 1], box_b_corners[j], cross_points[cnt]);
+            if (flag){
+                poly_center = poly_center + cross_points[cnt];
+                cnt++;
+#ifdef DEBUG
+                printf("Cross points (%.3f, %.3f): a(%.3f, %.3f)->(%.3f, %.3f), b(%.3f, %.3f)->(%.3f, %.3f) \n",
+                    cross_points[cnt - 1].x, cross_points[cnt - 1].y,
+                    box_a_corners[i].x, box_a_corners[i].y, box_a_corners[i + 1].x, box_a_corners[i + 1].y,
+                    box_b_corners[i].x, box_b_corners[i].y, box_b_corners[i + 1].x, box_b_corners[i + 1].y);
+#endif
+            }
+        }
+    }
+
+    // check corners
+    for (int k = 0; k < 4; k++){
+        if (check_in_box2d(box_a, box_b_corners[k])){
+            poly_center = poly_center + box_b_corners[k];
+            cross_points[cnt] = box_b_corners[k];
+            cnt++;
+#ifdef DEBUG
+                printf("b corners in a: corner_b(%.3f, %.3f)", cross_points[cnt - 1].x, cross_points[cnt - 1].y);
+#endif
+        }
+        if (check_in_box2d(box_b, box_a_corners[k])){
+            poly_center = poly_center + box_a_corners[k];
+            cross_points[cnt] = box_a_corners[k];
+            cnt++;
+#ifdef DEBUG
+                printf("a corners in b: corner_a(%.3f, %.3f)", cross_points[cnt - 1].x, cross_points[cnt - 1].y);
+#endif
+        }
+    }
+
+    poly_center.x /= cnt;
+    poly_center.y /= cnt;
+
+    // sort the points of polygon
+    Point temp;
+    for (int j = 0; j < cnt - 1; j++){
+        for (int i = 0; i < cnt - j - 1; i++){
+            if (point_cmp(cross_points[i], cross_points[i + 1], poly_center)){
+                temp = cross_points[i];
+                cross_points[i] = cross_points[i + 1];
+                cross_points[i + 1] = temp;
+            }
+        }
+    }
+
+#ifdef DEBUG
+    printf("cnt=%d\n", cnt);
+    for (int i = 0; i < cnt; i++){
+        printf("All cross point %d: (%.3f, %.3f)\n", i, cross_points[i].x, cross_points[i].y);
+    }
+#endif
+
+    // get the overlap areas
+    float area = 0;
+    for (int k = 0; k < cnt - 1; k++){
+        area += cross(cross_points[k] - cross_points[0], cross_points[k + 1] - cross_points[0]);
+    }
+
+    return fabs(area) / 2.0;
+}
+
+__device__ inline float iou_bev(const float *box_a, const float *box_b){
+    // params box_a: [x, y, z, dx, dy, dz, heading]
+    // params box_b: [x, y, z, dx, dy, dz, heading]
+    float sa = box_a[3] * box_a[4];
+    float sb = box_b[3] * box_b[4];
+    float s_overlap = box_overlap(box_a, box_b);
+    return s_overlap / fmaxf(sa + sb - s_overlap, EPS);
+}
+
+__global__ void boxes_overlap_kernel(const int num_a, const float *boxes_a, const int num_b, const float *boxes_b, float *ans_overlap){
+    // params boxes_a: (N, 7) [x, y, z, dx, dy, dz, heading]
+    // params boxes_b: (M, 7) [x, y, z, dx, dy, dz, heading]
+    const int a_idx = blockIdx.y * THREADS_PER_BLOCK + threadIdx.y;
+    const int b_idx = blockIdx.x * THREADS_PER_BLOCK + threadIdx.x;
+
+    if (a_idx >= num_a || b_idx >= num_b){
+        return;
+    }
+    const float * cur_box_a = boxes_a + a_idx * 7;
+    const float * cur_box_b = boxes_b + b_idx * 7;
+    float s_overlap = box_overlap(cur_box_a, cur_box_b);
+    ans_overlap[a_idx * num_b + b_idx] = s_overlap;
+}
+
+__global__ void boxes_iou_bev_kernel(const int num_a, const float *boxes_a, const int num_b, const float *boxes_b, float *ans_iou){
+    // params boxes_a: (N, 7) [x, y, z, dx, dy, dz, heading]
+    // params boxes_b: (M, 7) [x, y, z, dx, dy, dz, heading]
+    const int a_idx = blockIdx.y * THREADS_PER_BLOCK + threadIdx.y;
+    const int b_idx = blockIdx.x * THREADS_PER_BLOCK + threadIdx.x;
+
+    if (a_idx >= num_a || b_idx >= num_b){
+        return;
+    }
+
+    const float * cur_box_a = boxes_a + a_idx * 7;
+    const float * cur_box_b = boxes_b + b_idx * 7;
+    float cur_iou_bev = iou_bev(cur_box_a, cur_box_b);
+    ans_iou[a_idx * num_b + b_idx] = cur_iou_bev;
+}
+
+__global__ void nms_kernel(const int boxes_num, const float nms_overlap_thresh,
+                           const float *boxes, unsigned long long *mask){
+    //params: boxes (N, 7) [x, y, z, dx, dy, dz, heading]
+    //params: mask (N, N/THREADS_PER_BLOCK_NMS)
+
+    const int row_start = blockIdx.y;
+    const int col_start = blockIdx.x;
+
+    // if (row_start > col_start) return;
+
+    const int row_size = fminf(boxes_num - row_start * THREADS_PER_BLOCK_NMS, THREADS_PER_BLOCK_NMS);
+    const int col_size = fminf(boxes_num - col_start * THREADS_PER_BLOCK_NMS, THREADS_PER_BLOCK_NMS);
+
+    __shared__ float block_boxes[THREADS_PER_BLOCK_NMS * 7];
+
+    if (threadIdx.x < col_size) {
+        block_boxes[threadIdx.x * 7 + 0] = boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 7 + 0];
+        block_boxes[threadIdx.x * 7 + 1] = boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 7 + 1];
+        block_boxes[threadIdx.x * 7 + 2] = boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 7 + 2];
+        block_boxes[threadIdx.x * 7 + 3] = boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 7 + 3];
+        block_boxes[threadIdx.x * 7 + 4] = boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 7 + 4];
+        block_boxes[threadIdx.x * 7 + 5] = boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 7 + 5];
+        block_boxes[threadIdx.x * 7 + 6] = boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 7 + 6];
+    }
+    __syncthreads();
+
+    if (threadIdx.x < row_size) {
+        const int cur_box_idx = THREADS_PER_BLOCK_NMS * row_start + threadIdx.x;
+        const float *cur_box = boxes + cur_box_idx * 7;
+
+        int i = 0;
+        unsigned long long t = 0;
+        int start = 0;
+        if (row_start == col_start) {
+          start = threadIdx.x + 1;
+        }
+        for (i = start; i < col_size; i++) {
+            if (iou_bev(cur_box, block_boxes + i * 7) > nms_overlap_thresh){
+                t |= 1ULL << i;
+            }
+        }
+        const int col_blocks = DIVUP(boxes_num, THREADS_PER_BLOCK_NMS);
+        mask[cur_box_idx * col_blocks + col_start] = t;
+    }
+}
+
+
+__device__ inline float iou_normal(float const * const a, float const * const b) {
+    //params: a: [x, y, z, dx, dy, dz, heading]
+    //params: b: [x, y, z, dx, dy, dz, heading]
+
+    float left = fmaxf(a[0] - a[3] / 2, b[0] - b[3] / 2), right = fminf(a[0] + a[3] / 2, b[0] + b[3] / 2);
+    float top = fmaxf(a[1] - a[4] / 2, b[1] - b[4] / 2), bottom = fminf(a[1] + a[4] / 2, b[1] + b[4] / 2);
+    float width = fmaxf(right - left, 0.f), height = fmaxf(bottom - top, 0.f);
+    float interS = width * height;
+    float Sa = a[3] * a[4];
+    float Sb = b[3] * b[4];
+    return interS / fmaxf(Sa + Sb - interS, EPS);
+}
+
+
+__global__ void nms_normal_kernel(const int boxes_num, const float nms_overlap_thresh,
+                           const float *boxes, unsigned long long *mask){
+    //params: boxes (N, 7) [x, y, z, dx, dy, dz, heading]
+    //params: mask (N, N/THREADS_PER_BLOCK_NMS)
+
+    const int row_start = blockIdx.y;
+    const int col_start = blockIdx.x;
+
+    // if (row_start > col_start) return;
+
+    const int row_size = fminf(boxes_num - row_start * THREADS_PER_BLOCK_NMS, THREADS_PER_BLOCK_NMS);
+    const int col_size = fminf(boxes_num - col_start * THREADS_PER_BLOCK_NMS, THREADS_PER_BLOCK_NMS);
+
+    __shared__ float block_boxes[THREADS_PER_BLOCK_NMS * 7];
+
+    if (threadIdx.x < col_size) {
+        block_boxes[threadIdx.x * 7 + 0] = boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 7 + 0];
+        block_boxes[threadIdx.x * 7 + 1] = boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 7 + 1];
+        block_boxes[threadIdx.x * 7 + 2] = boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 7 + 2];
+        block_boxes[threadIdx.x * 7 + 3] = boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 7 + 3];
+        block_boxes[threadIdx.x * 7 + 4] = boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 7 + 4];
+        block_boxes[threadIdx.x * 7 + 5] = boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 7 + 5];
+        block_boxes[threadIdx.x * 7 + 6] = boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 7 + 6];
+    }
+    __syncthreads();
+
+    if (threadIdx.x < row_size) {
+        const int cur_box_idx = THREADS_PER_BLOCK_NMS * row_start + threadIdx.x;
+        const float *cur_box = boxes + cur_box_idx * 7;
+
+        int i = 0;
+        unsigned long long t = 0;
+        int start = 0;
+        if (row_start == col_start) {
+          start = threadIdx.x + 1;
+        }
+        for (i = start; i < col_size; i++) {
+            if (iou_normal(cur_box, block_boxes + i * 7) > nms_overlap_thresh){
+                t |= 1ULL << i;
+            }
+        }
+        const int col_blocks = DIVUP(boxes_num, THREADS_PER_BLOCK_NMS);
+        mask[cur_box_idx * col_blocks + col_start] = t;
+    }
+}
+
+
+
+
+
+void boxesoverlapLauncher(const int num_a, const float *boxes_a, const int num_b, const float *boxes_b, float *ans_overlap){
+
+    dim3 blocks(DIVUP(num_b, THREADS_PER_BLOCK), DIVUP(num_a, THREADS_PER_BLOCK));  // blockIdx.x(col), blockIdx.y(row)
+    dim3 threads(THREADS_PER_BLOCK, THREADS_PER_BLOCK);
+
+    boxes_overlap_kernel<<<blocks, threads>>>(num_a, boxes_a, num_b, boxes_b, ans_overlap);
+#ifdef DEBUG
+    cudaDeviceSynchronize();  // for using printf in kernel function
+#endif
+}
+
+void boxesioubevLauncher(const int num_a, const float *boxes_a, const int num_b, const float *boxes_b, float *ans_iou){
+
+    dim3 blocks(DIVUP(num_b, THREADS_PER_BLOCK), DIVUP(num_a, THREADS_PER_BLOCK));  // blockIdx.x(col), blockIdx.y(row)
+    dim3 threads(THREADS_PER_BLOCK, THREADS_PER_BLOCK);
+
+    boxes_iou_bev_kernel<<<blocks, threads>>>(num_a, boxes_a, num_b, boxes_b, ans_iou);
+#ifdef DEBUG
+    cudaDeviceSynchronize();  // for using printf in kernel function
+#endif
+}
+
+
+void nmsLauncher(const float *boxes, unsigned long long * mask, int boxes_num, float nms_overlap_thresh){
+    dim3 blocks(DIVUP(boxes_num, THREADS_PER_BLOCK_NMS),
+                DIVUP(boxes_num, THREADS_PER_BLOCK_NMS));
+    dim3 threads(THREADS_PER_BLOCK_NMS);
+    nms_kernel<<<blocks, threads>>>(boxes_num, nms_overlap_thresh, boxes, mask);
+}
+
+
+void nmsNormalLauncher(const float *boxes, unsigned long long * mask, int boxes_num, float nms_overlap_thresh){
+    dim3 blocks(DIVUP(boxes_num, THREADS_PER_BLOCK_NMS),
+                DIVUP(boxes_num, THREADS_PER_BLOCK_NMS));
+    dim3 threads(THREADS_PER_BLOCK_NMS);
+    nms_normal_kernel<<<blocks, threads>>>(boxes_num, nms_overlap_thresh, boxes, mask);
+}
diff --git a/opencood/opencood/pcdet_utils/pointnet2/pointnet2_batch/pointnet2_modules.py b/opencood/opencood/pcdet_utils/pointnet2/pointnet2_batch/pointnet2_modules.py
new file mode 100755
index 00000000..463dbb0d
--- /dev/null
+++ b/opencood/opencood/pcdet_utils/pointnet2/pointnet2_batch/pointnet2_modules.py
@@ -0,0 +1,174 @@
+from typing import List
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from opencood.pcdet_utils.pointnet2.pointnet2_batch import pointnet2_utils
+
+
+class _PointnetSAModuleBase(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+        self.npoint = None
+        self.groupers = None
+        self.mlps = None
+        self.pool_method = 'max_pool'
+
+    def forward(self, xyz: torch.Tensor, features: torch.Tensor = None, new_xyz=None) -> (torch.Tensor, torch.Tensor):
+        """
+        :param xyz: (B, N, 3) tensor of the xyz coordinates of the features
+        :param features: (B, N, C) tensor of the descriptors of the the features
+        :param new_xyz:
+        :return:
+            new_xyz: (B, npoint, 3) tensor of the new features' xyz
+            new_features: (B, npoint, \sum_k(mlps[k][-1])) tensor of the new_features descriptors
+        """
+        new_features_list = []
+
+        xyz_flipped = xyz.transpose(1, 2).contiguous()
+        if new_xyz is None:
+            new_xyz = pointnet2_utils.gather_operation(
+                xyz_flipped,
+                pointnet2_utils.furthest_point_sample(xyz, self.npoint)
+            ).transpose(1, 2).contiguous() if self.npoint is not None else None
+
+        for i in range(len(self.groupers)):
+            new_features = self.groupers[i](xyz, new_xyz, features)  # (B, C, npoint, nsample)
+
+            new_features = self.mlps[i](new_features)  # (B, mlp[-1], npoint, nsample)
+            if self.pool_method == 'max_pool':
+                new_features = F.max_pool2d(
+                    new_features, kernel_size=[1, new_features.size(3)]
+                )  # (B, mlp[-1], npoint, 1)
+            elif self.pool_method == 'avg_pool':
+                new_features = F.avg_pool2d(
+                    new_features, kernel_size=[1, new_features.size(3)]
+                )  # (B, mlp[-1], npoint, 1)
+            else:
+                raise NotImplementedError
+
+            new_features = new_features.squeeze(-1)  # (B, mlp[-1], npoint)
+            new_features_list.append(new_features)
+
+        return new_xyz, torch.cat(new_features_list, dim=1)
+
+
+class PointnetSAModuleMSG(_PointnetSAModuleBase):
+    """Pointnet set abstraction layer with multiscale grouping"""
+
+    def __init__(self, *, npoint: int, radii: List[float], nsamples: List[int], mlps: List[List[int]], bn: bool = True,
+                 use_xyz: bool = True, pool_method='max_pool'):
+        """
+        :param npoint: int
+        :param radii: list of float, list of radii to group with
+        :param nsamples: list of int, number of samples in each ball query
+        :param mlps: list of list of int, spec of the pointnet before the global pooling for each scale
+        :param bn: whether to use batchnorm
+        :param use_xyz:
+        :param pool_method: max_pool / avg_pool
+        """
+        super().__init__()
+
+        assert len(radii) == len(nsamples) == len(mlps)
+
+        self.npoint = npoint
+        self.groupers = nn.ModuleList()
+        self.mlps = nn.ModuleList()
+        for i in range(len(radii)):
+            radius = radii[i]
+            nsample = nsamples[i]
+            self.groupers.append(
+                pointnet2_utils.QueryAndGroup(radius, nsample, use_xyz=use_xyz)
+                if npoint is not None else pointnet2_utils.GroupAll(use_xyz)
+            )
+            mlp_spec = mlps[i]
+            if use_xyz:
+                mlp_spec[0] += 3
+
+            shared_mlps = []
+            for k in range(len(mlp_spec) - 1):
+                shared_mlps.extend([
+                    nn.Conv2d(mlp_spec[k], mlp_spec[k + 1], kernel_size=1, bias=False),
+                    nn.BatchNorm2d(mlp_spec[k + 1]),
+                    nn.ReLU()
+                ])
+            self.mlps.append(nn.Sequential(*shared_mlps))
+
+        self.pool_method = pool_method
+
+
+class PointnetSAModule(PointnetSAModuleMSG):
+    """Pointnet set abstraction layer"""
+
+    def __init__(self, *, mlp: List[int], npoint: int = None, radius: float = None, nsample: int = None,
+                 bn: bool = True, use_xyz: bool = True, pool_method='max_pool'):
+        """
+        :param mlp: list of int, spec of the pointnet before the global max_pool
+        :param npoint: int, number of features
+        :param radius: float, radius of ball
+        :param nsample: int, number of samples in the ball query
+        :param bn: whether to use batchnorm
+        :param use_xyz:
+        :param pool_method: max_pool / avg_pool
+        """
+        super().__init__(
+            mlps=[mlp], npoint=npoint, radii=[radius], nsamples=[nsample], bn=bn, use_xyz=use_xyz,
+            pool_method=pool_method
+        )
+
+
+class PointnetFPModule(nn.Module):
+    r"""Propigates the features of one set to another"""
+
+    def __init__(self, *, mlp: List[int], bn: bool = True):
+        """
+        :param mlp: list of int
+        :param bn: whether to use batchnorm
+        """
+        super().__init__()
+
+        shared_mlps = []
+        for k in range(len(mlp) - 1):
+            shared_mlps.extend([
+                nn.Conv2d(mlp[k], mlp[k + 1], kernel_size=1, bias=False),
+                nn.BatchNorm2d(mlp[k + 1]),
+                nn.ReLU()
+            ])
+        self.mlp = nn.Sequential(*shared_mlps)
+
+    def forward(
+            self, unknown: torch.Tensor, known: torch.Tensor, unknow_feats: torch.Tensor, known_feats: torch.Tensor
+    ) -> torch.Tensor:
+        """
+        :param unknown: (B, n, 3) tensor of the xyz positions of the unknown features
+        :param known: (B, m, 3) tensor of the xyz positions of the known features
+        :param unknow_feats: (B, C1, n) tensor of the features to be propigated to
+        :param known_feats: (B, C2, m) tensor of features to be propigated
+        :return:
+            new_features: (B, mlp[-1], n) tensor of the features of the unknown features
+        """
+        if known is not None:
+            dist, idx = pointnet2_utils.three_nn(unknown, known)
+            dist_recip = 1.0 / (dist + 1e-8)
+            norm = torch.sum(dist_recip, dim=2, keepdim=True)
+            weight = dist_recip / norm
+
+            interpolated_feats = pointnet2_utils.three_interpolate(known_feats, idx, weight)
+        else:
+            interpolated_feats = known_feats.expand(*known_feats.size()[0:2], unknown.size(1))
+
+        if unknow_feats is not None:
+            new_features = torch.cat([interpolated_feats, unknow_feats], dim=1)  # (B, C2 + C1, n)
+        else:
+            new_features = interpolated_feats
+
+        new_features = new_features.unsqueeze(-1)
+        new_features = self.mlp(new_features)
+
+        return new_features.squeeze(-1)
+
+
+if __name__ == "__main__":
+    pass
diff --git a/opencood/opencood/pcdet_utils/pointnet2/pointnet2_batch/pointnet2_utils.py b/opencood/opencood/pcdet_utils/pointnet2/pointnet2_batch/pointnet2_utils.py
new file mode 100755
index 00000000..f52d3785
--- /dev/null
+++ b/opencood/opencood/pcdet_utils/pointnet2/pointnet2_batch/pointnet2_utils.py
@@ -0,0 +1,291 @@
+from typing import Tuple
+
+import torch
+import torch.nn as nn
+from torch.autograd import Function, Variable
+
+from opencood.pcdet_utils.pointnet2.pointnet2_batch import \
+    pointnet2_batch_cuda as pointnet2
+
+
+class FurthestPointSampling(Function):
+    @staticmethod
+    def forward(ctx, xyz: torch.Tensor, npoint: int) -> torch.Tensor:
+        """
+        Uses iterative furthest point sampling to select a set of npoint features that have the largest
+        minimum distance
+        :param ctx:
+        :param xyz: (B, N, 3) where N > npoint
+        :param npoint: int, number of features in the sampled set
+        :return:
+             output: (B, npoint) tensor containing the set
+        """
+        assert xyz.is_contiguous()
+
+        B, N, _ = xyz.size()
+        output = torch.cuda.IntTensor(B, npoint)
+        temp = torch.cuda.FloatTensor(B, N).fill_(1e10)
+
+        pointnet2.furthest_point_sampling_wrapper(B, N, npoint, xyz, temp, output)
+        return output
+
+    @staticmethod
+    def backward(xyz, a=None):
+        return None, None
+
+
+furthest_point_sample = FurthestPointSampling.apply
+
+
+class GatherOperation(Function):
+
+    @staticmethod
+    def forward(ctx, features: torch.Tensor, idx: torch.Tensor) -> torch.Tensor:
+        """
+        :param ctx:
+        :param features: (B, C, N)
+        :param idx: (B, npoint) index tensor of the features to gather
+        :return:
+            output: (B, C, npoint)
+        """
+        assert features.is_contiguous()
+        assert idx.is_contiguous()
+
+        B, npoint = idx.size()
+        _, C, N = features.size()
+        output = torch.cuda.FloatTensor(B, C, npoint)
+
+        pointnet2.gather_points_wrapper(B, C, N, npoint, features, idx, output)
+
+        ctx.for_backwards = (idx, C, N)
+        return output
+
+    @staticmethod
+    def backward(ctx, grad_out):
+        idx, C, N = ctx.for_backwards
+        B, npoint = idx.size()
+
+        grad_features = Variable(torch.cuda.FloatTensor(B, C, N).zero_())
+        grad_out_data = grad_out.data.contiguous()
+        pointnet2.gather_points_grad_wrapper(B, C, N, npoint, grad_out_data, idx, grad_features.data)
+        return grad_features, None
+
+
+gather_operation = GatherOperation.apply
+
+
+class ThreeNN(Function):
+
+    @staticmethod
+    def forward(ctx, unknown: torch.Tensor, known: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Find the three nearest neighbors of unknown in known
+        :param ctx:
+        :param unknown: (B, N, 3)
+        :param known: (B, M, 3)
+        :return:
+            dist: (B, N, 3) l2 distance to the three nearest neighbors
+            idx: (B, N, 3) index of 3 nearest neighbors
+        """
+        assert unknown.is_contiguous()
+        assert known.is_contiguous()
+
+        B, N, _ = unknown.size()
+        m = known.size(1)
+        dist2 = torch.cuda.FloatTensor(B, N, 3)
+        idx = torch.cuda.IntTensor(B, N, 3)
+
+        pointnet2.three_nn_wrapper(B, N, m, unknown, known, dist2, idx)
+        return torch.sqrt(dist2), idx
+
+    @staticmethod
+    def backward(ctx, a=None, b=None):
+        return None, None
+
+
+three_nn = ThreeNN.apply
+
+
+class ThreeInterpolate(Function):
+
+    @staticmethod
+    def forward(ctx, features: torch.Tensor, idx: torch.Tensor, weight: torch.Tensor) -> torch.Tensor:
+        """
+        Performs weight linear interpolation on 3 features
+        :param ctx:
+        :param features: (B, C, M) Features descriptors to be interpolated from
+        :param idx: (B, n, 3) three nearest neighbors of the target features in features
+        :param weight: (B, n, 3) weights
+        :return:
+            output: (B, C, N) tensor of the interpolated features
+        """
+        assert features.is_contiguous()
+        assert idx.is_contiguous()
+        assert weight.is_contiguous()
+
+        B, c, m = features.size()
+        n = idx.size(1)
+        ctx.three_interpolate_for_backward = (idx, weight, m)
+        output = torch.cuda.FloatTensor(B, c, n)
+
+        pointnet2.three_interpolate_wrapper(B, c, m, n, features, idx, weight, output)
+        return output
+
+    @staticmethod
+    def backward(ctx, grad_out: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        """
+        :param ctx:
+        :param grad_out: (B, C, N) tensor with gradients of outputs
+        :return:
+            grad_features: (B, C, M) tensor with gradients of features
+            None:
+            None:
+        """
+        idx, weight, m = ctx.three_interpolate_for_backward
+        B, c, n = grad_out.size()
+
+        grad_features = Variable(torch.cuda.FloatTensor(B, c, m).zero_())
+        grad_out_data = grad_out.data.contiguous()
+
+        pointnet2.three_interpolate_grad_wrapper(B, c, n, m, grad_out_data, idx, weight, grad_features.data)
+        return grad_features, None, None
+
+
+three_interpolate = ThreeInterpolate.apply
+
+
+class GroupingOperation(Function):
+
+    @staticmethod
+    def forward(ctx, features: torch.Tensor, idx: torch.Tensor) -> torch.Tensor:
+        """
+        :param ctx:
+        :param features: (B, C, N) tensor of features to group
+        :param idx: (B, npoint, nsample) tensor containing the indicies of features to group with
+        :return:
+            output: (B, C, npoint, nsample) tensor
+        """
+        assert features.is_contiguous()
+        assert idx.is_contiguous()
+
+        B, nfeatures, nsample = idx.size()
+        _, C, N = features.size()
+        output = torch.cuda.FloatTensor(B, C, nfeatures, nsample)
+
+        pointnet2.group_points_wrapper(B, C, N, nfeatures, nsample, features, idx, output)
+
+        ctx.for_backwards = (idx, N)
+        return output
+
+    @staticmethod
+    def backward(ctx, grad_out: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        :param ctx:
+        :param grad_out: (B, C, npoint, nsample) tensor of the gradients of the output from forward
+        :return:
+            grad_features: (B, C, N) gradient of the features
+        """
+        idx, N = ctx.for_backwards
+
+        B, C, npoint, nsample = grad_out.size()
+        grad_features = Variable(torch.cuda.FloatTensor(B, C, N).zero_())
+
+        grad_out_data = grad_out.data.contiguous()
+        pointnet2.group_points_grad_wrapper(B, C, N, npoint, nsample, grad_out_data, idx, grad_features.data)
+        return grad_features, None
+
+
+grouping_operation = GroupingOperation.apply
+
+
+class BallQuery(Function):
+
+    @staticmethod
+    def forward(ctx, radius: float, nsample: int, xyz: torch.Tensor, new_xyz: torch.Tensor) -> torch.Tensor:
+        """
+        :param ctx:
+        :param radius: float, radius of the balls
+        :param nsample: int, maximum number of features in the balls
+        :param xyz: (B, N, 3) xyz coordinates of the features
+        :param new_xyz: (B, npoint, 3) centers of the ball query
+        :return:
+            idx: (B, npoint, nsample) tensor with the indicies of the features that form the query balls
+        """
+        assert new_xyz.is_contiguous()
+        assert xyz.is_contiguous()
+
+        B, N, _ = xyz.size()
+        npoint = new_xyz.size(1)
+        idx = torch.cuda.IntTensor(B, npoint, nsample).zero_()
+
+        pointnet2.ball_query_wrapper(B, N, npoint, radius, nsample, new_xyz, xyz, idx)
+        return idx
+
+    @staticmethod
+    def backward(ctx, a=None):
+        return None, None, None, None
+
+
+ball_query = BallQuery.apply
+
+
+class QueryAndGroup(nn.Module):
+    def __init__(self, radius: float, nsample: int, use_xyz: bool = True):
+        """
+        :param radius: float, radius of ball
+        :param nsample: int, maximum number of features to gather in the ball
+        :param use_xyz:
+        """
+        super().__init__()
+        self.radius, self.nsample, self.use_xyz = radius, nsample, use_xyz
+
+    def forward(self, xyz: torch.Tensor, new_xyz: torch.Tensor, features: torch.Tensor = None) -> Tuple[torch.Tensor]:
+        """
+        :param xyz: (B, N, 3) xyz coordinates of the features
+        :param new_xyz: (B, npoint, 3) centroids
+        :param features: (B, C, N) descriptors of the features
+        :return:
+            new_features: (B, 3 + C, npoint, nsample)
+        """
+        idx = ball_query(self.radius, self.nsample, xyz, new_xyz)
+        xyz_trans = xyz.transpose(1, 2).contiguous()
+        grouped_xyz = grouping_operation(xyz_trans, idx)  # (B, 3, npoint, nsample)
+        grouped_xyz -= new_xyz.transpose(1, 2).unsqueeze(-1)
+
+        if features is not None:
+            grouped_features = grouping_operation(features, idx)
+            if self.use_xyz:
+                new_features = torch.cat([grouped_xyz, grouped_features], dim=1)  # (B, C + 3, npoint, nsample)
+            else:
+                new_features = grouped_features
+        else:
+            assert self.use_xyz, "Cannot have not features and not use xyz as a feature!"
+            new_features = grouped_xyz
+
+        return new_features
+
+
+class GroupAll(nn.Module):
+    def __init__(self, use_xyz: bool = True):
+        super().__init__()
+        self.use_xyz = use_xyz
+
+    def forward(self, xyz: torch.Tensor, new_xyz: torch.Tensor, features: torch.Tensor = None):
+        """
+        :param xyz: (B, N, 3) xyz coordinates of the features
+        :param new_xyz: ignored
+        :param features: (B, C, N) descriptors of the features
+        :return:
+            new_features: (B, C + 3, 1, N)
+        """
+        grouped_xyz = xyz.transpose(1, 2).unsqueeze(2)
+        if features is not None:
+            grouped_features = features.unsqueeze(2)
+            if self.use_xyz:
+                new_features = torch.cat([grouped_xyz, grouped_features], dim=1)  # (B, 3 + C, 1, N)
+            else:
+                new_features = grouped_features
+        else:
+            new_features = grouped_xyz
+
+        return new_features
diff --git a/opencood/opencood/pcdet_utils/pointnet2/pointnet2_batch/src/ball_query.cpp b/opencood/opencood/pcdet_utils/pointnet2/pointnet2_batch/src/ball_query.cpp
new file mode 100755
index 00000000..67784c63
--- /dev/null
+++ b/opencood/opencood/pcdet_utils/pointnet2/pointnet2_batch/src/ball_query.cpp
@@ -0,0 +1,42 @@
+/*
+batch version of ball query, modified from the original implementation of official PointNet++ codes.
+Written by Shaoshuai Shi
+All Rights Reserved 2018.
+*/
+
+
+#include <torch/serialize/tensor.h>
+#include <vector>
+#include <THC/THC.h>
+#include <cuda.h>
+#include <cuda_runtime_api.h>
+#include "ball_query_gpu.h"
+
+extern THCState *state;
+
+#define CHECK_CUDA(x) do { \
+	  if (!x.type().is_cuda()) { \
+		      fprintf(stderr, "%s must be CUDA tensor at %s:%d\n", #x, __FILE__, __LINE__); \
+		      exit(-1); \
+		    } \
+} while (0)
+#define CHECK_CONTIGUOUS(x) do { \
+	  if (!x.is_contiguous()) { \
+		      fprintf(stderr, "%s must be contiguous tensor at %s:%d\n", #x, __FILE__, __LINE__); \
+		      exit(-1); \
+		    } \
+} while (0)
+#define CHECK_INPUT(x) CHECK_CUDA(x);CHECK_CONTIGUOUS(x)
+
+
+int ball_query_wrapper_fast(int b, int n, int m, float radius, int nsample, 
+    at::Tensor new_xyz_tensor, at::Tensor xyz_tensor, at::Tensor idx_tensor) {
+    CHECK_INPUT(new_xyz_tensor);
+    CHECK_INPUT(xyz_tensor);
+    const float *new_xyz = new_xyz_tensor.data<float>();
+    const float *xyz = xyz_tensor.data<float>();
+    int *idx = idx_tensor.data<int>();
+    
+    ball_query_kernel_launcher_fast(b, n, m, radius, nsample, new_xyz, xyz, idx);
+    return 1;
+}
diff --git a/opencood/opencood/pcdet_utils/pointnet2/pointnet2_batch/src/ball_query_gpu.cu b/opencood/opencood/pcdet_utils/pointnet2/pointnet2_batch/src/ball_query_gpu.cu
new file mode 100755
index 00000000..38c00636
--- /dev/null
+++ b/opencood/opencood/pcdet_utils/pointnet2/pointnet2_batch/src/ball_query_gpu.cu
@@ -0,0 +1,73 @@
+/*
+batch version of ball query, modified from the original implementation of official PointNet++ codes.
+Written by Shaoshuai Shi
+All Rights Reserved 2018.
+*/
+
+#include <math.h>
+#include <stdio.h>
+#include <stdlib.h>
+
+#include "ball_query_gpu.h"
+#include "cuda_utils.h"
+
+
+__global__ void ball_query_kernel_fast(int b, int n, int m, float radius, int nsample, 
+    const float *__restrict__ new_xyz, const float *__restrict__ xyz, int *__restrict__ idx) {
+    // new_xyz: (B, M, 3)
+    // xyz: (B, N, 3)
+    // output:
+    //      idx: (B, M, nsample)
+    int bs_idx = blockIdx.y;
+    int pt_idx = blockIdx.x * blockDim.x + threadIdx.x;
+    if (bs_idx >= b || pt_idx >= m) return;
+
+    new_xyz += bs_idx * m * 3 + pt_idx * 3;
+    xyz += bs_idx * n * 3;
+    idx += bs_idx * m * nsample + pt_idx * nsample;
+
+    float radius2 = radius * radius;
+    float new_x = new_xyz[0];
+    float new_y = new_xyz[1];
+    float new_z = new_xyz[2];
+
+    int cnt = 0;
+    for (int k = 0; k < n; ++k) {
+        float x = xyz[k * 3 + 0];
+        float y = xyz[k * 3 + 1];
+        float z = xyz[k * 3 + 2];
+        float d2 = (new_x - x) * (new_x - x) + (new_y - y) * (new_y - y) + (new_z - z) * (new_z - z);
+        if (d2 < radius2){
+            if (cnt == 0){
+                for (int l = 0; l < nsample; ++l) {
+                    idx[l] = k;
+                }
+            }
+            idx[cnt] = k;
+            ++cnt;
+            if (cnt >= nsample) break;
+        }
+    }
+}
+
+
+void ball_query_kernel_launcher_fast(int b, int n, int m, float radius, int nsample, \
+    const float *new_xyz, const float *xyz, int *idx) {
+    // new_xyz: (B, M, 3)
+    // xyz: (B, N, 3)
+    // output:
+    //      idx: (B, M, nsample)
+
+    cudaError_t err;
+
+    dim3 blocks(DIVUP(m, THREADS_PER_BLOCK), b);  // blockIdx.x(col), blockIdx.y(row)
+    dim3 threads(THREADS_PER_BLOCK);
+
+    ball_query_kernel_fast<<<blocks, threads>>>(b, n, m, radius, nsample, new_xyz, xyz, idx);
+    // cudaDeviceSynchronize();  // for using printf in kernel function
+    err = cudaGetLastError();
+    if (cudaSuccess != err) {
+        fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
+        exit(-1);
+    }
+}
diff --git a/opencood/opencood/pcdet_utils/pointnet2/pointnet2_batch/src/ball_query_gpu.h b/opencood/opencood/pcdet_utils/pointnet2/pointnet2_batch/src/ball_query_gpu.h
new file mode 100755
index 00000000..1213dda7
--- /dev/null
+++ b/opencood/opencood/pcdet_utils/pointnet2/pointnet2_batch/src/ball_query_gpu.h
@@ -0,0 +1,15 @@
+#ifndef _BALL_QUERY_GPU_H
+#define _BALL_QUERY_GPU_H
+
+#include <torch/serialize/tensor.h>
+#include <vector>
+#include <cuda.h>
+#include <cuda_runtime_api.h>
+
+int ball_query_wrapper_fast(int b, int n, int m, float radius, int nsample, 
+	at::Tensor new_xyz_tensor, at::Tensor xyz_tensor, at::Tensor idx_tensor);
+
+void ball_query_kernel_launcher_fast(int b, int n, int m, float radius, int nsample, 
+	const float *xyz, const float *new_xyz, int *idx);
+
+#endif
diff --git a/opencood/opencood/pcdet_utils/pointnet2/pointnet2_batch/src/cuda_utils.h b/opencood/opencood/pcdet_utils/pointnet2/pointnet2_batch/src/cuda_utils.h
new file mode 100755
index 00000000..7fe27969
--- /dev/null
+++ b/opencood/opencood/pcdet_utils/pointnet2/pointnet2_batch/src/cuda_utils.h
@@ -0,0 +1,15 @@
+#ifndef _CUDA_UTILS_H
+#define _CUDA_UTILS_H
+
+#include <cmath>
+
+#define TOTAL_THREADS 1024
+#define THREADS_PER_BLOCK 256
+#define DIVUP(m,n) ((m) / (n) + ((m) % (n) > 0))
+
+inline int opt_n_threads(int work_size) {
+    const int pow_2 = std::log(static_cast<double>(work_size)) / std::log(2.0);
+
+    return max(min(1 << pow_2, TOTAL_THREADS), 1);
+}
+#endif
diff --git a/opencood/opencood/pcdet_utils/pointnet2/pointnet2_batch/src/group_points.cpp b/opencood/opencood/pcdet_utils/pointnet2/pointnet2_batch/src/group_points.cpp
new file mode 100755
index 00000000..5565700f
--- /dev/null
+++ b/opencood/opencood/pcdet_utils/pointnet2/pointnet2_batch/src/group_points.cpp
@@ -0,0 +1,39 @@
+/*
+batch version of point grouping, modified from the original implementation of official PointNet++ codes.
+Written by Shaoshuai Shi
+All Rights Reserved 2018.
+*/
+
+
+#include <torch/serialize/tensor.h>
+#include <cuda.h>
+#include <cuda_runtime_api.h>
+#include <vector>
+#include <THC/THC.h>
+#include "group_points_gpu.h"
+
+extern THCState *state;
+
+
+int group_points_grad_wrapper_fast(int b, int c, int n, int npoints, int nsample, 
+    at::Tensor grad_out_tensor, at::Tensor idx_tensor, at::Tensor grad_points_tensor) {
+
+    float *grad_points = grad_points_tensor.data<float>();
+    const int *idx = idx_tensor.data<int>();
+    const float *grad_out = grad_out_tensor.data<float>();
+
+    group_points_grad_kernel_launcher_fast(b, c, n, npoints, nsample, grad_out, idx, grad_points);
+    return 1;
+}
+
+
+int group_points_wrapper_fast(int b, int c, int n, int npoints, int nsample, 
+    at::Tensor points_tensor, at::Tensor idx_tensor, at::Tensor out_tensor) {
+
+    const float *points = points_tensor.data<float>();
+    const int *idx = idx_tensor.data<int>();
+    float *out = out_tensor.data<float>();
+
+    group_points_kernel_launcher_fast(b, c, n, npoints, nsample, points, idx, out);
+    return 1;
+}
diff --git a/opencood/opencood/pcdet_utils/pointnet2/pointnet2_batch/src/group_points_gpu.cu b/opencood/opencood/pcdet_utils/pointnet2/pointnet2_batch/src/group_points_gpu.cu
new file mode 100755
index 00000000..d9038f69
--- /dev/null
+++ b/opencood/opencood/pcdet_utils/pointnet2/pointnet2_batch/src/group_points_gpu.cu
@@ -0,0 +1,92 @@
+/*
+batch version of point grouping, modified from the original implementation of official PointNet++ codes.
+Written by Shaoshuai Shi
+All Rights Reserved 2018.
+*/
+
+#include <stdio.h>
+#include <stdlib.h>
+
+#include "cuda_utils.h"
+#include "group_points_gpu.h"
+
+
+__global__ void group_points_grad_kernel_fast(int b, int c, int n, int npoints, int nsample, 
+    const float *__restrict__ grad_out, const int *__restrict__ idx, float *__restrict__ grad_points) {
+    // grad_out: (B, C, npoints, nsample)
+    // idx: (B, npoints, nsample)
+    // output:
+    //      grad_points: (B, C, N)
+    int bs_idx = blockIdx.z;
+    int c_idx = blockIdx.y;
+    int index = blockIdx.x * blockDim.x + threadIdx.x;
+    int pt_idx = index / nsample;
+    if (bs_idx >= b || c_idx >= c || pt_idx >= npoints) return;
+
+    int sample_idx = index % nsample;
+    grad_out += bs_idx * c * npoints * nsample + c_idx * npoints * nsample + pt_idx * nsample + sample_idx;
+    idx += bs_idx * npoints * nsample + pt_idx * nsample + sample_idx; 
+    
+    atomicAdd(grad_points + bs_idx * c * n + c_idx * n + idx[0] , grad_out[0]);
+}
+
+void group_points_grad_kernel_launcher_fast(int b, int c, int n, int npoints, int nsample, 
+    const float *grad_out, const int *idx, float *grad_points) {
+    // grad_out: (B, C, npoints, nsample)
+    // idx: (B, npoints, nsample)
+    // output:
+    //      grad_points: (B, C, N)
+    cudaError_t err;
+    dim3 blocks(DIVUP(npoints * nsample, THREADS_PER_BLOCK), c, b);  // blockIdx.x(col), blockIdx.y(row)
+    dim3 threads(THREADS_PER_BLOCK);
+
+    group_points_grad_kernel_fast<<<blocks, threads>>>(b, c, n, npoints, nsample, grad_out, idx, grad_points);
+
+    err = cudaGetLastError();
+    if (cudaSuccess != err) {
+        fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
+        exit(-1);
+    }
+}
+
+
+__global__ void group_points_kernel_fast(int b, int c, int n, int npoints, int nsample, 
+    const float *__restrict__ points, const int *__restrict__ idx, float *__restrict__ out) {
+    // points: (B, C, N)
+    // idx: (B, npoints, nsample)
+    // output:
+    //      out: (B, C, npoints, nsample)
+    int bs_idx = blockIdx.z;
+    int c_idx = blockIdx.y;
+    int index = blockIdx.x * blockDim.x + threadIdx.x;
+    int pt_idx = index / nsample;
+    if (bs_idx >= b || c_idx >= c || pt_idx >= npoints) return;
+
+    int sample_idx = index % nsample;
+
+    idx += bs_idx * npoints * nsample + pt_idx * nsample + sample_idx; 
+    int in_idx = bs_idx * c * n + c_idx * n + idx[0];
+    int out_idx = bs_idx * c * npoints * nsample + c_idx * npoints * nsample + pt_idx * nsample + sample_idx;
+
+    out[out_idx] = points[in_idx];
+}
+
+
+void group_points_kernel_launcher_fast(int b, int c, int n, int npoints, int nsample, 
+    const float *points, const int *idx, float *out) {
+    // points: (B, C, N)
+    // idx: (B, npoints, nsample)
+    // output:
+    //      out: (B, C, npoints, nsample)
+    cudaError_t err;
+    dim3 blocks(DIVUP(npoints * nsample, THREADS_PER_BLOCK), c, b);  // blockIdx.x(col), blockIdx.y(row)
+    dim3 threads(THREADS_PER_BLOCK);
+
+    group_points_kernel_fast<<<blocks, threads>>>(b, c, n, npoints, nsample, points, idx, out);
+    // cudaDeviceSynchronize();  // for using printf in kernel function
+    err = cudaGetLastError();
+    if (cudaSuccess != err) {
+        fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
+        exit(-1);
+    }
+}
diff --git a/opencood/opencood/pcdet_utils/pointnet2/pointnet2_batch/src/group_points_gpu.h b/opencood/opencood/pcdet_utils/pointnet2/pointnet2_batch/src/group_points_gpu.h
new file mode 100755
index 00000000..8a17c68b
--- /dev/null
+++ b/opencood/opencood/pcdet_utils/pointnet2/pointnet2_batch/src/group_points_gpu.h
@@ -0,0 +1,22 @@
+#ifndef _GROUP_POINTS_GPU_H
+#define _GROUP_POINTS_GPU_H
+
+#include <torch/serialize/tensor.h>
+#include <cuda.h>
+#include <cuda_runtime_api.h>
+#include <vector>
+
+
+int group_points_wrapper_fast(int b, int c, int n, int npoints, int nsample, 
+    at::Tensor points_tensor, at::Tensor idx_tensor, at::Tensor out_tensor);
+
+void group_points_kernel_launcher_fast(int b, int c, int n, int npoints, int nsample, 
+    const float *points, const int *idx, float *out);
+
+int group_points_grad_wrapper_fast(int b, int c, int n, int npoints, int nsample, 
+    at::Tensor grad_out_tensor, at::Tensor idx_tensor, at::Tensor grad_points_tensor);
+
+void group_points_grad_kernel_launcher_fast(int b, int c, int n, int npoints, int nsample, 
+    const float *grad_out, const int *idx, float *grad_points);
+
+#endif
diff --git a/opencood/opencood/pcdet_utils/pointnet2/pointnet2_batch/src/interpolate.cpp b/opencood/opencood/pcdet_utils/pointnet2/pointnet2_batch/src/interpolate.cpp
new file mode 100755
index 00000000..c2496e8e
--- /dev/null
+++ b/opencood/opencood/pcdet_utils/pointnet2/pointnet2_batch/src/interpolate.cpp
@@ -0,0 +1,58 @@
+/*
+batch version of point interpolation, modified from the original implementation of official PointNet++ codes.
+Written by Shaoshuai Shi
+All Rights Reserved 2018.
+*/
+
+
+#include <torch/serialize/tensor.h>
+#include <vector>
+#include <THC/THC.h>
+#include <math.h>
+#include <stdio.h>
+#include <stdlib.h>
+#include <cuda.h>
+#include <cuda_runtime_api.h>
+#include "interpolate_gpu.h"
+
+extern THCState *state;
+
+
+void three_nn_wrapper_fast(int b, int n, int m, at::Tensor unknown_tensor, 
+    at::Tensor known_tensor, at::Tensor dist2_tensor, at::Tensor idx_tensor) {
+    const float *unknown = unknown_tensor.data<float>();
+    const float *known = known_tensor.data<float>();
+    float *dist2 = dist2_tensor.data<float>();
+    int *idx = idx_tensor.data<int>();
+
+    three_nn_kernel_launcher_fast(b, n, m, unknown, known, dist2, idx);
+}
+
+
+void three_interpolate_wrapper_fast(int b, int c, int m, int n,
+                         at::Tensor points_tensor,
+                         at::Tensor idx_tensor,
+                         at::Tensor weight_tensor,
+                         at::Tensor out_tensor) {
+
+    const float *points = points_tensor.data<float>();
+    const float *weight = weight_tensor.data<float>();
+    float *out = out_tensor.data<float>();
+    const int *idx = idx_tensor.data<int>();
+
+    three_interpolate_kernel_launcher_fast(b, c, m, n, points, idx, weight, out);
+}
+
+void three_interpolate_grad_wrapper_fast(int b, int c, int n, int m,
+                            at::Tensor grad_out_tensor,
+                            at::Tensor idx_tensor,
+                            at::Tensor weight_tensor,
+                            at::Tensor grad_points_tensor) {
+
+    const float *grad_out = grad_out_tensor.data<float>();
+    const float *weight = weight_tensor.data<float>();
+    float *grad_points = grad_points_tensor.data<float>();
+    const int *idx = idx_tensor.data<int>();
+
+    three_interpolate_grad_kernel_launcher_fast(b, c, n, m, grad_out, idx, weight, grad_points);
+}
diff --git a/opencood/opencood/pcdet_utils/pointnet2/pointnet2_batch/src/interpolate_gpu.cu b/opencood/opencood/pcdet_utils/pointnet2/pointnet2_batch/src/interpolate_gpu.cu
new file mode 100755
index 00000000..eb60c0dc
--- /dev/null
+++ b/opencood/opencood/pcdet_utils/pointnet2/pointnet2_batch/src/interpolate_gpu.cu
@@ -0,0 +1,168 @@
+/*
+batch version of point interpolation, modified from the original implementation of official PointNet++ codes.
+Written by Shaoshuai Shi
+All Rights Reserved 2018.
+*/
+
+
+#include <math.h>
+#include <stdio.h>
+#include <stdlib.h>
+
+#include "cuda_utils.h"
+#include "interpolate_gpu.h"
+
+
+__global__ void three_nn_kernel_fast(int b, int n, int m, const float *__restrict__ unknown, 
+    const float *__restrict__ known, float *__restrict__ dist2, int *__restrict__ idx) {
+    // unknown: (B, N, 3)
+    // known: (B, M, 3)
+    // output: 
+    //      dist2: (B, N, 3)
+    //      idx: (B, N, 3)
+    
+    int bs_idx = blockIdx.y;
+    int pt_idx = blockIdx.x * blockDim.x + threadIdx.x;
+    if (bs_idx >= b || pt_idx >= n) return;
+
+    unknown += bs_idx * n * 3 + pt_idx * 3;
+    known += bs_idx * m * 3;
+    dist2 += bs_idx * n * 3 + pt_idx * 3;
+    idx += bs_idx * n * 3 + pt_idx * 3;
+
+    float ux = unknown[0];
+    float uy = unknown[1];
+    float uz = unknown[2];
+
+    double best1 = 1e40, best2 = 1e40, best3 = 1e40;
+    int besti1 = 0, besti2 = 0, besti3 = 0;
+    for (int k = 0; k < m; ++k) {
+        float x = known[k * 3 + 0];
+        float y = known[k * 3 + 1];
+        float z = known[k * 3 + 2];
+        float d = (ux - x) * (ux - x) + (uy - y) * (uy - y) + (uz - z) * (uz - z);
+        if (d < best1) {
+            best3 = best2; besti3 = besti2;
+            best2 = best1; besti2 = besti1;
+            best1 = d; besti1 = k;
+        } 
+        else if (d < best2) {
+            best3 = best2; besti3 = besti2;
+            best2 = d; besti2 = k;
+        } 
+        else if (d < best3) {
+            best3 = d; besti3 = k;
+        }
+    }
+    dist2[0] = best1; dist2[1] = best2; dist2[2] = best3;
+    idx[0] = besti1; idx[1] = besti2; idx[2] = besti3;
+}
+
+
+void three_nn_kernel_launcher_fast(int b, int n, int m, const float *unknown, 
+    const float *known, float *dist2, int *idx) {
+    // unknown: (B, N, 3)
+    // known: (B, M, 3)
+    // output: 
+    //      dist2: (B, N, 3)
+    //      idx: (B, N, 3)
+
+    cudaError_t err;
+    dim3 blocks(DIVUP(n, THREADS_PER_BLOCK), b);  // blockIdx.x(col), blockIdx.y(row)
+    dim3 threads(THREADS_PER_BLOCK);
+
+    three_nn_kernel_fast<<<blocks, threads>>>(b, n, m, unknown, known, dist2, idx);
+
+    err = cudaGetLastError();
+    if (cudaSuccess != err) {
+        fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
+        exit(-1);
+    }
+}
+
+
+__global__ void three_interpolate_kernel_fast(int b, int c, int m, int n, const float *__restrict__ points, 
+    const int *__restrict__ idx, const float *__restrict__ weight, float *__restrict__ out) {
+    // points: (B, C, M)
+    // idx: (B, N, 3)
+    // weight: (B, N, 3)
+    // output:
+    //      out: (B, C, N)
+
+    int bs_idx = blockIdx.z;
+    int c_idx = blockIdx.y;
+    int pt_idx = blockIdx.x * blockDim.x + threadIdx.x;
+
+    if (bs_idx >= b || c_idx >= c || pt_idx >= n) return;
+
+    weight += bs_idx * n * 3 + pt_idx * 3;
+    points += bs_idx * c * m + c_idx * m;
+    idx += bs_idx * n * 3 + pt_idx * 3;
+    out += bs_idx * c * n + c_idx * n;
+
+    out[pt_idx] = weight[0] * points[idx[0]] + weight[1] * points[idx[1]] + weight[2] * points[idx[2]];
+}
+
+void three_interpolate_kernel_launcher_fast(int b, int c, int m, int n, 
+    const float *points, const int *idx, const float *weight, float *out) {
+    // points: (B, C, M)
+    // idx: (B, N, 3)
+    // weight: (B, N, 3)
+    // output:
+    //      out: (B, C, N)
+
+    cudaError_t err;
+    dim3 blocks(DIVUP(n, THREADS_PER_BLOCK), c, b);  // blockIdx.x(col), blockIdx.y(row)
+    dim3 threads(THREADS_PER_BLOCK);
+    three_interpolate_kernel_fast<<<blocks, threads>>>(b, c, m, n, points, idx, weight, out);
+
+    err = cudaGetLastError();
+    if (cudaSuccess != err) {
+        fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
+        exit(-1);
+    }
+}
+
+
+__global__ void three_interpolate_grad_kernel_fast(int b, int c, int n, int m, const float *__restrict__ grad_out, 
+    const int *__restrict__ idx, const float *__restrict__ weight, float *__restrict__ grad_points) {
+    // grad_out: (B, C, N)
+    // weight: (B, N, 3)
+    // output:
+    //      grad_points: (B, C, M)
+
+    int bs_idx = blockIdx.z;
+    int c_idx = blockIdx.y;
+    int pt_idx = blockIdx.x * blockDim.x + threadIdx.x;
+
+    if (bs_idx >= b || c_idx >= c || pt_idx >= n) return;
+    
+    grad_out += bs_idx * c * n + c_idx * n + pt_idx;
+    weight += bs_idx * n * 3 + pt_idx * 3;
+    grad_points += bs_idx * c * m + c_idx * m;
+    idx += bs_idx * n * 3 + pt_idx * 3;
+
+
+    atomicAdd(grad_points + idx[0], grad_out[0] * weight[0]);
+    atomicAdd(grad_points + idx[1], grad_out[0] * weight[1]);
+    atomicAdd(grad_points + idx[2], grad_out[0] * weight[2]);
+}
+
+void three_interpolate_grad_kernel_launcher_fast(int b, int c, int n, int m, const float *grad_out, 
+    const int *idx, const float *weight, float *grad_points) {
+    // grad_out: (B, C, N)
+    // weight: (B, N, 3)
+    // output:
+    //      grad_points: (B, C, M)
+
+    cudaError_t err;
+    dim3 blocks(DIVUP(n, THREADS_PER_BLOCK), c, b);  // blockIdx.x(col), blockIdx.y(row)
+    dim3 threads(THREADS_PER_BLOCK);
+    three_interpolate_grad_kernel_fast<<<blocks, threads>>>(b, c, n, m, grad_out, idx, weight, grad_points);
+
+    err = cudaGetLastError();
+    if (cudaSuccess != err) {
+        fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
+        exit(-1);
+    }
+}
diff --git a/opencood/opencood/pcdet_utils/pointnet2/pointnet2_batch/src/interpolate_gpu.h b/opencood/opencood/pcdet_utils/pointnet2/pointnet2_batch/src/interpolate_gpu.h
new file mode 100755
index 00000000..95ea1d79
--- /dev/null
+++ b/opencood/opencood/pcdet_utils/pointnet2/pointnet2_batch/src/interpolate_gpu.h
@@ -0,0 +1,30 @@
+#ifndef _INTERPOLATE_GPU_H
+#define _INTERPOLATE_GPU_H
+
+#include <torch/serialize/tensor.h>
+#include<vector>
+#include <cuda.h>
+#include <cuda_runtime_api.h>
+
+
+void three_nn_wrapper_fast(int b, int n, int m, at::Tensor unknown_tensor, 
+  at::Tensor known_tensor, at::Tensor dist2_tensor, at::Tensor idx_tensor);
+
+void three_nn_kernel_launcher_fast(int b, int n, int m, const float *unknown,
+	const float *known, float *dist2, int *idx);
+
+
+void three_interpolate_wrapper_fast(int b, int c, int m, int n, at::Tensor points_tensor, 
+    at::Tensor idx_tensor, at::Tensor weight_tensor, at::Tensor out_tensor);
+
+void three_interpolate_kernel_launcher_fast(int b, int c, int m, int n, 
+    const float *points, const int *idx, const float *weight, float *out);
+
+
+void three_interpolate_grad_wrapper_fast(int b, int c, int n, int m, at::Tensor grad_out_tensor, 
+    at::Tensor idx_tensor, at::Tensor weight_tensor, at::Tensor grad_points_tensor);
+
+void three_interpolate_grad_kernel_launcher_fast(int b, int c, int n, int m, const float *grad_out, 
+    const int *idx, const float *weight, float *grad_points);
+
+#endif
diff --git a/opencood/opencood/pcdet_utils/pointnet2/pointnet2_batch/src/pointnet2_api.cpp b/opencood/opencood/pcdet_utils/pointnet2/pointnet2_batch/src/pointnet2_api.cpp
new file mode 100755
index 00000000..d91f0f21
--- /dev/null
+++ b/opencood/opencood/pcdet_utils/pointnet2/pointnet2_batch/src/pointnet2_api.cpp
@@ -0,0 +1,24 @@
+#include <torch/serialize/tensor.h>
+#include <torch/extension.h>
+
+#include "ball_query_gpu.h"
+#include "group_points_gpu.h"
+#include "sampling_gpu.h"
+#include "interpolate_gpu.h"
+
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+    m.def("ball_query_wrapper", &ball_query_wrapper_fast, "ball_query_wrapper_fast");
+
+    m.def("group_points_wrapper", &group_points_wrapper_fast, "group_points_wrapper_fast");
+    m.def("group_points_grad_wrapper", &group_points_grad_wrapper_fast, "group_points_grad_wrapper_fast");
+
+    m.def("gather_points_wrapper", &gather_points_wrapper_fast, "gather_points_wrapper_fast");
+    m.def("gather_points_grad_wrapper", &gather_points_grad_wrapper_fast, "gather_points_grad_wrapper_fast");
+
+    m.def("furthest_point_sampling_wrapper", &furthest_point_sampling_wrapper, "furthest_point_sampling_wrapper");
+    
+    m.def("three_nn_wrapper", &three_nn_wrapper_fast, "three_nn_wrapper_fast");
+    m.def("three_interpolate_wrapper", &three_interpolate_wrapper_fast, "three_interpolate_wrapper_fast");
+    m.def("three_interpolate_grad_wrapper", &three_interpolate_grad_wrapper_fast, "three_interpolate_grad_wrapper_fast");
+}
diff --git a/opencood/opencood/pcdet_utils/pointnet2/pointnet2_batch/src/sampling.cpp b/opencood/opencood/pcdet_utils/pointnet2/pointnet2_batch/src/sampling.cpp
new file mode 100755
index 00000000..d58ca277
--- /dev/null
+++ b/opencood/opencood/pcdet_utils/pointnet2/pointnet2_batch/src/sampling.cpp
@@ -0,0 +1,50 @@
+/*
+batch version of point sampling and gathering, modified from the original implementation of official PointNet++ codes.
+Written by Shaoshuai Shi
+All Rights Reserved 2018.
+*/
+
+
+#include <torch/serialize/tensor.h>
+#include <ATen/cuda/CUDAContext.h>
+#include <vector>
+#include <THC/THC.h>
+
+#include "sampling_gpu.h"
+
+extern THCState *state;
+
+
+int gather_points_wrapper_fast(int b, int c, int n, int npoints, 
+    at::Tensor points_tensor, at::Tensor idx_tensor, at::Tensor out_tensor){
+    const float *points = points_tensor.data<float>();
+    const int *idx = idx_tensor.data<int>();
+    float *out = out_tensor.data<float>();
+
+    gather_points_kernel_launcher_fast(b, c, n, npoints, points, idx, out);
+    return 1;
+}
+
+
+int gather_points_grad_wrapper_fast(int b, int c, int n, int npoints, 
+    at::Tensor grad_out_tensor, at::Tensor idx_tensor, at::Tensor grad_points_tensor) {
+
+    const float *grad_out = grad_out_tensor.data<float>();
+    const int *idx = idx_tensor.data<int>();
+    float *grad_points = grad_points_tensor.data<float>();
+
+    gather_points_grad_kernel_launcher_fast(b, c, n, npoints, grad_out, idx, grad_points);
+    return 1;
+}
+
+
+int furthest_point_sampling_wrapper(int b, int n, int m, 
+    at::Tensor points_tensor, at::Tensor temp_tensor, at::Tensor idx_tensor) {
+
+    const float *points = points_tensor.data<float>();
+    float *temp = temp_tensor.data<float>();
+    int *idx = idx_tensor.data<int>();
+
+    furthest_point_sampling_kernel_launcher(b, n, m, points, temp, idx);
+    return 1;
+}
diff --git a/opencood/opencood/pcdet_utils/pointnet2/pointnet2_batch/src/sampling_gpu.cu b/opencood/opencood/pcdet_utils/pointnet2/pointnet2_batch/src/sampling_gpu.cu
new file mode 100755
index 00000000..09a7357b
--- /dev/null
+++ b/opencood/opencood/pcdet_utils/pointnet2/pointnet2_batch/src/sampling_gpu.cu
@@ -0,0 +1,260 @@
+/*
+batch version of point sampling and gathering, modified from the original implementation of official PointNet++ codes.
+Written by Shaoshuai Shi
+All Rights Reserved 2018.
+*/
+
+
+#include <stdio.h>
+#include <stdlib.h>
+
+#include "cuda_utils.h"
+#include "sampling_gpu.h"
+
+
+__global__ void gather_points_kernel_fast(int b, int c, int n, int m, 
+    const float *__restrict__ points, const int *__restrict__ idx, float *__restrict__ out) {
+    // points: (B, C, N)
+    // idx: (B, M)
+    // output:
+    //      out: (B, C, M)
+
+    int bs_idx = blockIdx.z;
+    int c_idx = blockIdx.y;
+    int pt_idx = blockIdx.x * blockDim.x + threadIdx.x;
+    if (bs_idx >= b || c_idx >= c || pt_idx >= m) return;
+
+    out += bs_idx * c * m + c_idx * m + pt_idx;
+    idx += bs_idx * m + pt_idx;
+    points += bs_idx * c * n + c_idx * n;
+    out[0] = points[idx[0]];
+}
+
+void gather_points_kernel_launcher_fast(int b, int c, int n, int npoints, 
+    const float *points, const int *idx, float *out) {
+    // points: (B, C, N)
+    // idx: (B, npoints)
+    // output:
+    //      out: (B, C, npoints)
+
+    cudaError_t err;
+    dim3 blocks(DIVUP(npoints, THREADS_PER_BLOCK), c, b);  // blockIdx.x(col), blockIdx.y(row)
+    dim3 threads(THREADS_PER_BLOCK);
+
+    gather_points_kernel_fast<<<blocks, threads>>>(b, c, n, npoints, points, idx, out);
+
+    err = cudaGetLastError();
+    if (cudaSuccess != err) {
+        fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
+        exit(-1);
+    }
+}
+
+__global__ void gather_points_grad_kernel_fast(int b, int c, int n, int m, const float *__restrict__ grad_out, 
+    const int *__restrict__ idx, float *__restrict__ grad_points) {
+    // grad_out: (B, C, M)
+    // idx: (B, M)
+    // output:
+    //      grad_points: (B, C, N)
+
+    int bs_idx = blockIdx.z;
+    int c_idx = blockIdx.y;
+    int pt_idx = blockIdx.x * blockDim.x + threadIdx.x;
+    if (bs_idx >= b || c_idx >= c || pt_idx >= m) return;
+
+    grad_out += bs_idx * c * m + c_idx * m + pt_idx;
+    idx += bs_idx * m + pt_idx;
+    grad_points += bs_idx * c * n + c_idx * n;
+
+    atomicAdd(grad_points + idx[0], grad_out[0]);
+}
+
+void gather_points_grad_kernel_launcher_fast(int b, int c, int n, int npoints, 
+    const float *grad_out, const int *idx, float *grad_points) {
+    // grad_out: (B, C, npoints)
+    // idx: (B, npoints)
+    // output:
+    //      grad_points: (B, C, N)
+
+    cudaError_t err;
+    dim3 blocks(DIVUP(npoints, THREADS_PER_BLOCK), c, b);  // blockIdx.x(col), blockIdx.y(row)
+    dim3 threads(THREADS_PER_BLOCK);
+
+    gather_points_grad_kernel_fast<<<blocks, threads>>>(b, c, n, npoints, grad_out, idx, grad_points);
+
+    err = cudaGetLastError();
+    if (cudaSuccess != err) {
+        fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
+        exit(-1);
+    }
+}
+
+
+__device__ void __update(float *__restrict__ dists, int *__restrict__ dists_i, int idx1, int idx2){
+    const float v1 = dists[idx1], v2 = dists[idx2];
+    const int i1 = dists_i[idx1], i2 = dists_i[idx2];
+    dists[idx1] = max(v1, v2);
+    dists_i[idx1] = v2 > v1 ? i2 : i1;
+}
+
+template <unsigned int block_size>
+__global__ void furthest_point_sampling_kernel(int b, int n, int m, 
+    const float *__restrict__ dataset, float *__restrict__ temp, int *__restrict__ idxs) {
+    // dataset: (B, N, 3)
+    // tmp: (B, N)
+    // output:
+    //      idx: (B, M)
+
+    if (m <= 0) return;
+    __shared__ float dists[block_size];
+    __shared__ int dists_i[block_size];
+
+    int batch_index = blockIdx.x;
+    dataset += batch_index * n * 3;
+    temp += batch_index * n;
+    idxs += batch_index * m;
+
+    int tid = threadIdx.x;
+    const int stride = block_size;
+
+    int old = 0;
+    if (threadIdx.x == 0)
+    idxs[0] = old;
+
+    __syncthreads();
+    for (int j = 1; j < m; j++) {
+    int besti = 0;
+    float best = -1;
+    float x1 = dataset[old * 3 + 0];
+    float y1 = dataset[old * 3 + 1];
+    float z1 = dataset[old * 3 + 2];
+    for (int k = tid; k < n; k += stride) {
+        float x2, y2, z2;
+        x2 = dataset[k * 3 + 0];
+        y2 = dataset[k * 3 + 1];
+        z2 = dataset[k * 3 + 2];
+        // float mag = (x2 * x2) + (y2 * y2) + (z2 * z2);
+        // if (mag <= 1e-3)
+        // continue;
+
+        float d = (x2 - x1) * (x2 - x1) + (y2 - y1) * (y2 - y1) + (z2 - z1) * (z2 - z1);
+        float d2 = min(d, temp[k]);
+        temp[k] = d2;
+        besti = d2 > best ? k : besti;
+        best = d2 > best ? d2 : best;
+    }
+    dists[tid] = best;
+    dists_i[tid] = besti;
+    __syncthreads();
+
+    if (block_size >= 1024) {
+        if (tid < 512) {
+            __update(dists, dists_i, tid, tid + 512);
+        }
+        __syncthreads();
+    }
+
+    if (block_size >= 512) {
+        if (tid < 256) {
+            __update(dists, dists_i, tid, tid + 256);
+        }
+        __syncthreads();
+    }
+    if (block_size >= 256) {
+        if (tid < 128) {
+            __update(dists, dists_i, tid, tid + 128);
+        }
+        __syncthreads();
+    }
+    if (block_size >= 128) {
+        if (tid < 64) {
+            __update(dists, dists_i, tid, tid + 64);
+        }
+        __syncthreads();
+    }
+    if (block_size >= 64) {
+        if (tid < 32) {
+            __update(dists, dists_i, tid, tid + 32);
+        }
+        __syncthreads();
+    }
+    if (block_size >= 32) {
+        if (tid < 16) {
+            __update(dists, dists_i, tid, tid + 16);
+        }
+        __syncthreads();
+    }
+    if (block_size >= 16) {
+        if (tid < 8) {
+            __update(dists, dists_i, tid, tid + 8);
+        }
+        __syncthreads();
+    }
+    if (block_size >= 8) {
+        if (tid < 4) {
+            __update(dists, dists_i, tid, tid + 4);
+        }
+        __syncthreads();
+    }
+    if (block_size >= 4) {
+        if (tid < 2) {
+            __update(dists, dists_i, tid, tid + 2);
+        }
+        __syncthreads();
+    }
+    if (block_size >= 2) {
+        if (tid < 1) {
+            __update(dists, dists_i, tid, tid + 1);
+        }
+        __syncthreads();
+    }
+
+    old = dists_i[0];
+    if (tid == 0)
+        idxs[j] = old;
+    }
+}
+
+void furthest_point_sampling_kernel_launcher(int b, int n, int m, 
+    const float *dataset, float *temp, int *idxs) {
+    // dataset: (B, N, 3)
+    // tmp: (B, N)
+    // output:
+    //      idx: (B, M)
+
+    cudaError_t err;
+    unsigned int n_threads = opt_n_threads(n);
+
+    switch (n_threads) {
+        case 1024:
+        furthest_point_sampling_kernel<1024><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        case 512:
+        furthest_point_sampling_kernel<512><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        case 256:
+        furthest_point_sampling_kernel<256><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        case 128:
+        furthest_point_sampling_kernel<128><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        case 64:
+        furthest_point_sampling_kernel<64><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        case 32:
+        furthest_point_sampling_kernel<32><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        case 16:
+        furthest_point_sampling_kernel<16><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        case 8:
+        furthest_point_sampling_kernel<8><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        case 4:
+        furthest_point_sampling_kernel<4><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        case 2:
+        furthest_point_sampling_kernel<2><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        case 1:
+        furthest_point_sampling_kernel<1><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        default:
+        furthest_point_sampling_kernel<512><<<b, n_threads>>>(b, n, m, dataset, temp, idxs);
+    }
+
+    err = cudaGetLastError();
+    if (cudaSuccess != err) {
+        fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
+        exit(-1);
+    }
+}
diff --git a/opencood/opencood/pcdet_utils/pointnet2/pointnet2_batch/src/sampling_gpu.h b/opencood/opencood/pcdet_utils/pointnet2/pointnet2_batch/src/sampling_gpu.h
new file mode 100755
index 00000000..9017aa68
--- /dev/null
+++ b/opencood/opencood/pcdet_utils/pointnet2/pointnet2_batch/src/sampling_gpu.h
@@ -0,0 +1,29 @@
+#ifndef _SAMPLING_GPU_H
+#define _SAMPLING_GPU_H
+
+#include <torch/serialize/tensor.h>
+#include <ATen/cuda/CUDAContext.h>
+#include<vector>
+
+
+int gather_points_wrapper_fast(int b, int c, int n, int npoints, 
+    at::Tensor points_tensor, at::Tensor idx_tensor, at::Tensor out_tensor);
+
+void gather_points_kernel_launcher_fast(int b, int c, int n, int npoints, 
+    const float *points, const int *idx, float *out);
+
+
+int gather_points_grad_wrapper_fast(int b, int c, int n, int npoints, 
+    at::Tensor grad_out_tensor, at::Tensor idx_tensor, at::Tensor grad_points_tensor);
+
+void gather_points_grad_kernel_launcher_fast(int b, int c, int n, int npoints, 
+    const float *grad_out, const int *idx, float *grad_points);
+
+
+int furthest_point_sampling_wrapper(int b, int n, int m, 
+    at::Tensor points_tensor, at::Tensor temp_tensor, at::Tensor idx_tensor);
+
+void furthest_point_sampling_kernel_launcher(int b, int n, int m, 
+    const float *dataset, float *temp, int *idxs);
+
+#endif
diff --git a/opencood/opencood/pcdet_utils/pointnet2/pointnet2_stack/pointnet2_modules.py b/opencood/opencood/pcdet_utils/pointnet2/pointnet2_stack/pointnet2_modules.py
new file mode 100755
index 00000000..71f30382
--- /dev/null
+++ b/opencood/opencood/pcdet_utils/pointnet2/pointnet2_stack/pointnet2_modules.py
@@ -0,0 +1,137 @@
+from typing import List
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from opencood.pcdet_utils.pointnet2.pointnet2_stack import pointnet2_utils
+
+
+class StackSAModuleMSG(nn.Module):
+
+    def __init__(self, *, radii: List[float], nsamples: List[int], mlps: List[List[int]],
+                 use_xyz: bool = True, pool_method='max_pool'):
+        """
+        Args:
+            radii: list of float, list of radii to group with
+            nsamples: list of int, number of samples in each ball query
+            mlps: list of list of int, spec of the pointnet before the global pooling for each scale
+            use_xyz:
+            pool_method: max_pool / avg_pool
+        """
+        super().__init__()
+
+        assert len(radii) == len(nsamples) == len(mlps)
+
+        self.groupers = nn.ModuleList()
+        self.mlps = nn.ModuleList()
+        for i in range(len(radii)):
+            radius = radii[i]
+            nsample = nsamples[i]
+            self.groupers.append(pointnet2_utils.QueryAndGroup(radius, nsample, use_xyz=use_xyz))
+            mlp_spec = mlps[i]
+            if use_xyz:
+                mlp_spec[0] += 3
+
+            shared_mlps = []
+            for k in range(len(mlp_spec) - 1):
+                shared_mlps.extend([
+                    nn.Conv2d(mlp_spec[k], mlp_spec[k + 1], kernel_size=1, bias=False),
+                    nn.BatchNorm2d(mlp_spec[k + 1]),
+                    nn.ReLU()
+                ])
+            self.mlps.append(nn.Sequential(*shared_mlps))
+        self.pool_method = pool_method
+
+        self.init_weights()
+
+    def init_weights(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(m.weight)
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+            if isinstance(m, nn.BatchNorm2d):
+                nn.init.constant_(m.weight, 1.0)
+                nn.init.constant_(m.bias, 0)
+
+    def forward(self, xyz, xyz_batch_cnt, new_xyz, new_xyz_batch_cnt, features=None, empty_voxel_set_zeros=True):
+        """
+        :param xyz: (N1 + N2 ..., 3) tensor of the xyz coordinates of the features
+        :param xyz_batch_cnt: (batch_size), [N1, N2, ...]
+        :param new_xyz: (M1 + M2 ..., 3)
+        :param new_xyz_batch_cnt: (batch_size), [M1, M2, ...]
+        :param features: (N1 + N2 ..., C) tensor of the descriptors of the the features
+        :return:
+            new_xyz: (M1 + M2 ..., 3) tensor of the new features' xyz
+            new_features: (M1 + M2 ..., \sum_k(mlps[k][-1])) tensor of the new_features descriptors
+        """
+        new_features_list = []
+        for k in range(len(self.groupers)):
+            new_features, ball_idxs = self.groupers[k](
+                xyz, xyz_batch_cnt, new_xyz, new_xyz_batch_cnt, features
+            )  # (M1 + M2, C, nsample)
+            new_features = new_features.permute(1, 0, 2).unsqueeze(dim=0)  # (1, C, M1 + M2 ..., nsample)
+            new_features = self.mlps[k](new_features)  # (1, C, M1 + M2 ..., nsample)
+
+            if self.pool_method == 'max_pool':
+                new_features = F.max_pool2d(
+                    new_features, kernel_size=[1, new_features.size(3)]
+                ).squeeze(dim=-1)  # (1, C, M1 + M2 ...)
+            elif self.pool_method == 'avg_pool':
+                new_features = F.avg_pool2d(
+                    new_features, kernel_size=[1, new_features.size(3)]
+                ).squeeze(dim=-1)  # (1, C, M1 + M2 ...)
+            else:
+                raise NotImplementedError
+            new_features = new_features.squeeze(dim=0).permute(1, 0)  # (M1 + M2 ..., C)
+            new_features_list.append(new_features)
+
+        new_features = torch.cat(new_features_list, dim=1)  # (M1 + M2 ..., C)
+
+        return new_xyz, new_features
+
+
+class StackPointnetFPModule(nn.Module):
+    def __init__(self, *, mlp: List[int]):
+        """
+        Args:
+            mlp: list of int
+        """
+        super().__init__()
+        shared_mlps = []
+        for k in range(len(mlp) - 1):
+            shared_mlps.extend([
+                nn.Conv2d(mlp[k], mlp[k + 1], kernel_size=1, bias=False),
+                nn.BatchNorm2d(mlp[k + 1]),
+                nn.ReLU()
+            ])
+        self.mlp = nn.Sequential(*shared_mlps)
+
+    def forward(self, unknown, unknown_batch_cnt, known, known_batch_cnt, unknown_feats=None, known_feats=None):
+        """
+        Args:
+            unknown: (N1 + N2 ..., 3)
+            known: (M1 + M2 ..., 3)
+            unknow_feats: (N1 + N2 ..., C1)
+            known_feats: (M1 + M2 ..., C2)
+
+        Returns:
+            new_features: (N1 + N2 ..., C_out)
+        """
+        dist, idx = pointnet2_utils.three_nn(unknown, unknown_batch_cnt, known, known_batch_cnt)
+        dist_recip = 1.0 / (dist + 1e-8)
+        norm = torch.sum(dist_recip, dim=-1, keepdim=True)
+        weight = dist_recip / norm
+
+        interpolated_feats = pointnet2_utils.three_interpolate(known_feats, idx, weight)
+
+        if unknown_feats is not None:
+            new_features = torch.cat([interpolated_feats, unknown_feats], dim=1)  # (N1 + N2 ..., C2 + C1)
+        else:
+            new_features = interpolated_feats
+        new_features = new_features.permute(1, 0)[None, :, :, None]  # (1, C, N1 + N2 ..., 1)
+        new_features = self.mlp(new_features)
+
+        new_features = new_features.squeeze(dim=0).squeeze(dim=-1).permute(1, 0)  # (N1 + N2 ..., C)
+        return new_features
diff --git a/opencood/opencood/pcdet_utils/pointnet2/pointnet2_stack/pointnet2_utils.py b/opencood/opencood/pcdet_utils/pointnet2/pointnet2_stack/pointnet2_utils.py
new file mode 100755
index 00000000..df136251
--- /dev/null
+++ b/opencood/opencood/pcdet_utils/pointnet2/pointnet2_stack/pointnet2_utils.py
@@ -0,0 +1,266 @@
+import torch
+import torch.nn as nn
+from torch.autograd import Function, Variable
+
+from opencood.pcdet_utils.pointnet2.pointnet2_stack import pointnet2_stack_cuda as pointnet2
+
+
+class BallQuery(Function):
+
+    @staticmethod
+    def forward(ctx, radius: float, nsample: int, xyz: torch.Tensor, xyz_batch_cnt: torch.Tensor,
+                new_xyz: torch.Tensor, new_xyz_batch_cnt):
+        """
+        Args:
+            ctx:
+            radius: float, radius of the balls
+            nsample: int, maximum number of features in the balls
+            xyz: (N1 + N2 ..., 3) xyz coordinates of the features
+            xyz_batch_cnt: (batch_size), [N1, N2, ...]
+            new_xyz: (M1 + M2 ..., 3) centers of the ball query
+            new_xyz_batch_cnt: (batch_size), [M1, M2, ...]
+
+        Returns:
+            idx: (M1 + M2, nsample) tensor with the indicies of the features that form the query balls
+        """
+        assert new_xyz.is_contiguous()
+        assert new_xyz_batch_cnt.is_contiguous()
+        assert xyz.is_contiguous()
+        assert xyz_batch_cnt.is_contiguous()
+
+        B = xyz_batch_cnt.shape[0]
+        M = new_xyz.shape[0]
+        idx = torch.cuda.IntTensor(M, nsample).zero_()
+
+        pointnet2.ball_query_wrapper(B, M, radius, nsample, new_xyz, new_xyz_batch_cnt, xyz, xyz_batch_cnt, idx)
+        empty_ball_mask = (idx[:, 0] == -1)
+        idx[empty_ball_mask] = 0
+        return idx, empty_ball_mask
+
+    @staticmethod
+    def backward(ctx, a=None):
+        return None, None, None, None
+
+
+ball_query = BallQuery.apply
+
+
+class GroupingOperation(Function):
+
+    @staticmethod
+    def forward(ctx, features: torch.Tensor, features_batch_cnt: torch.Tensor,
+                idx: torch.Tensor, idx_batch_cnt: torch.Tensor):
+        """
+        Args:
+            ctx:
+            features: (N1 + N2 ..., C) tensor of features to group
+            features_batch_cnt: (batch_size) [N1 + N2 ...] tensor containing the indicies of features to group with
+            idx: (M1 + M2 ..., nsample) tensor containing the indicies of features to group with
+            idx_batch_cnt: (batch_size) [M1 + M2 ...] tensor containing the indicies of features to group with
+
+        Returns:
+            output: (M1 + M2, C, nsample) tensor
+        """
+        assert features.is_contiguous()
+        assert features_batch_cnt.is_contiguous()
+        assert idx.is_contiguous()
+        assert idx_batch_cnt.is_contiguous()
+
+        assert features.shape[0] == features_batch_cnt.sum(), \
+            'features: %s, features_batch_cnt: %s' % (str(features.shape), str(features_batch_cnt))
+        assert idx.shape[0] == idx_batch_cnt.sum(), \
+            'idx: %s, idx_batch_cnt: %s' % (str(idx.shape), str(idx_batch_cnt))
+
+        M, nsample = idx.size()
+        N, C = features.size()
+        B = idx_batch_cnt.shape[0]
+        output = torch.cuda.FloatTensor(M, C, nsample)
+
+        pointnet2.group_points_wrapper(B, M, C, nsample, features, features_batch_cnt, idx, idx_batch_cnt, output)
+
+        ctx.for_backwards = (B, N, idx, features_batch_cnt, idx_batch_cnt)
+        return output
+
+    @staticmethod
+    def backward(ctx, grad_out: torch.Tensor):
+        """
+        Args:
+            ctx:
+            grad_out: (M1 + M2 ..., C, nsample) tensor of the gradients of the output from forward
+
+        Returns:
+            grad_features: (N1 + N2 ..., C) gradient of the features
+        """
+        B, N, idx, features_batch_cnt, idx_batch_cnt = ctx.for_backwards
+
+        M, C, nsample = grad_out.size()
+        grad_features = Variable(torch.cuda.FloatTensor(N, C).zero_())
+
+        grad_out_data = grad_out.data.contiguous()
+        pointnet2.group_points_grad_wrapper(B, M, C, N, nsample, grad_out_data, idx,
+                                            idx_batch_cnt, features_batch_cnt, grad_features.data)
+        return grad_features, None, None, None
+
+
+grouping_operation = GroupingOperation.apply
+
+
+class QueryAndGroup(nn.Module):
+    def __init__(self, radius: float, nsample: int, use_xyz: bool = True):
+        """
+        Args:
+            radius: float, radius of ball
+            nsample: int, maximum number of features to gather in the ball
+            use_xyz:
+        """
+        super().__init__()
+        self.radius, self.nsample, self.use_xyz = radius, nsample, use_xyz
+
+    def forward(self, xyz: torch.Tensor, xyz_batch_cnt: torch.Tensor,
+                new_xyz: torch.Tensor, new_xyz_batch_cnt: torch.Tensor,
+                features: torch.Tensor = None):
+        """
+        Args:
+            xyz: (N1 + N2 ..., 3) xyz coordinates of the features
+            xyz_batch_cnt: (batch_size), [N1, N2, ...]
+            new_xyz: (M1 + M2 ..., 3) centers of the ball query
+            new_xyz_batch_cnt: (batch_size), [M1, M2, ...]
+            features: (N1 + N2 ..., C) tensor of features to group
+
+        Returns:
+            new_features: (M1 + M2, C, nsample) tensor
+        """
+        assert xyz.shape[0] == xyz_batch_cnt.sum(), 'xyz: %s, xyz_batch_cnt: %s' % (str(xyz.shape), str(new_xyz_batch_cnt))
+        assert new_xyz.shape[0] == new_xyz_batch_cnt.sum(), \
+            'new_xyz: %s, new_xyz_batch_cnt: %s' % (str(new_xyz.shape), str(new_xyz_batch_cnt))
+
+        # idx: (M1 + M2 ..., nsample), empty_ball_mask: (M1 + M2 ...)
+        idx, empty_ball_mask = ball_query(self.radius, self.nsample, xyz, xyz_batch_cnt, new_xyz, new_xyz_batch_cnt)
+        grouped_xyz = grouping_operation(xyz, xyz_batch_cnt, idx, new_xyz_batch_cnt)  # (M1 + M2, 3, nsample)
+        grouped_xyz -= new_xyz.unsqueeze(-1)
+
+        grouped_xyz[empty_ball_mask] = 0
+
+        if features is not None:
+            grouped_features = grouping_operation(features, xyz_batch_cnt, idx, new_xyz_batch_cnt)  # (M1 + M2, C, nsample)
+            grouped_features[empty_ball_mask] = 0
+            if self.use_xyz:
+                new_features = torch.cat([grouped_xyz, grouped_features], dim=1)  # (M1 + M2 ..., C + 3, nsample)
+            else:
+                new_features = grouped_features
+        else:
+            assert self.use_xyz, "Cannot have not features and not use xyz as a feature!"
+            new_features = grouped_xyz
+
+        return new_features, idx
+
+
+class FurthestPointSampling(Function):
+    @staticmethod
+    def forward(ctx, xyz: torch.Tensor, npoint: int):
+        """
+        Args:
+            ctx:
+            xyz: (B, N, 3) where N > npoint
+            npoint: int, number of features in the sampled set
+
+        Returns:
+            output: (B, npoint) tensor containing the set
+        """
+        assert xyz.is_contiguous()
+
+        B, N, _ = xyz.size()
+        output = torch.cuda.IntTensor(B, npoint)
+        temp = torch.cuda.FloatTensor(B, N).fill_(1e10)
+
+        pointnet2.furthest_point_sampling_wrapper(B, N, npoint, xyz, temp, output)
+        return output
+
+    @staticmethod
+    def backward(xyz, a=None):
+        return None, None
+
+
+furthest_point_sample = FurthestPointSampling.apply
+
+
+class ThreeNN(Function):
+    @staticmethod
+    def forward(ctx, unknown, unknown_batch_cnt, known, known_batch_cnt):
+        """
+        Args:
+            ctx:
+            unknown: (N1 + N2..., 3)
+            unknown_batch_cnt: (batch_size), [N1, N2, ...]
+            known: (M1 + M2..., 3)
+            known_batch_cnt: (batch_size), [M1, M2, ...]
+
+        Returns:
+            dist: (N1 + N2 ..., 3)  l2 distance to the three nearest neighbors
+            idx: (N1 + N2 ..., 3)  index of the three nearest neighbors, range [0, M1+M2+...]
+        """
+        assert unknown.shape.__len__() == 2 and unknown.shape[1] == 3
+        assert known.shape.__len__() == 2 and known.shape[1] == 3
+        assert unknown_batch_cnt.__len__() == known_batch_cnt.__len__()
+
+        dist2 = unknown.new_zeros(unknown.shape)
+        idx = unknown_batch_cnt.new_zeros(unknown.shape).int()
+
+        pointnet2.three_nn_wrapper(
+            unknown.contiguous(), unknown_batch_cnt.contiguous(),
+            known.contiguous(), known_batch_cnt.contiguous(), dist2, idx
+        )
+        return torch.sqrt(dist2), idx
+
+    @staticmethod
+    def backward(ctx, a=None, b=None):
+        return None, None
+
+
+three_nn = ThreeNN.apply
+
+
+class ThreeInterpolate(Function):
+
+    @staticmethod
+    def forward(ctx, features: torch.Tensor, idx: torch.Tensor, weight: torch.Tensor):
+        """
+        Args:
+            ctx:
+            features: (M1 + M2 ..., C)
+            idx: [N1 + N2 ..., 3]
+            weight: [N1 + N2 ..., 3]
+
+        Returns:
+            out_tensor: (N1 + N2 ..., C)
+        """
+        assert idx.shape[0] == weight.shape[0] and idx.shape[1] == weight.shape[1] == 3
+
+        ctx.three_interpolate_for_backward = (idx, weight, features.shape[0])
+        output = features.new_zeros((idx.shape[0], features.shape[1]))
+        pointnet2.three_interpolate_wrapper(features.contiguous(), idx.contiguous(), weight.contiguous(), output)
+        return output
+
+    @staticmethod
+    def backward(ctx, grad_out: torch.Tensor):
+        """
+        Args:
+            ctx:
+            grad_out: (N1 + N2 ..., C)
+
+        Returns:
+            grad_features: (M1 + M2 ..., C)
+        """
+        idx, weight, M = ctx.three_interpolate_for_backward
+        grad_features = grad_out.new_zeros((M, grad_out.shape[1]))
+        pointnet2.three_interpolate_grad_wrapper(
+            grad_out.contiguous(), idx.contiguous(), weight.contiguous(), grad_features
+        )
+        return grad_features, None, None
+
+
+three_interpolate = ThreeInterpolate.apply
+
+
+if __name__ == '__main__':
+    pass
diff --git a/opencood/opencood/pcdet_utils/pointnet2/pointnet2_stack/src/ball_query.cpp b/opencood/opencood/pcdet_utils/pointnet2/pointnet2_stack/src/ball_query.cpp
new file mode 100755
index 00000000..8ae8f235
--- /dev/null
+++ b/opencood/opencood/pcdet_utils/pointnet2/pointnet2_stack/src/ball_query.cpp
@@ -0,0 +1,47 @@
+/*
+Stacked-batch-data version of ball query, modified from the original implementation of official PointNet++ codes.
+Written by Shaoshuai Shi
+All Rights Reserved 2019-2020.
+*/
+
+
+#include <torch/serialize/tensor.h>
+#include <vector>
+#include <THC/THC.h>
+#include <cuda.h>
+#include <cuda_runtime_api.h>
+#include "ball_query_gpu.h"
+
+extern THCState *state;
+
+#define CHECK_CUDA(x) do { \
+  if (!x.type().is_cuda()) { \
+    fprintf(stderr, "%s must be CUDA tensor at %s:%d\n", #x, __FILE__, __LINE__); \
+    exit(-1); \
+  } \
+} while (0)
+#define CHECK_CONTIGUOUS(x) do { \
+  if (!x.is_contiguous()) { \
+    fprintf(stderr, "%s must be contiguous tensor at %s:%d\n", #x, __FILE__, __LINE__); \
+    exit(-1); \
+  } \
+} while (0)
+#define CHECK_INPUT(x) CHECK_CUDA(x);CHECK_CONTIGUOUS(x)
+
+int ball_query_wrapper_stack(int B, int M, float radius, int nsample,
+    at::Tensor new_xyz_tensor, at::Tensor new_xyz_batch_cnt_tensor,
+    at::Tensor xyz_tensor, at::Tensor xyz_batch_cnt_tensor, at::Tensor idx_tensor) {
+    CHECK_INPUT(new_xyz_tensor);
+    CHECK_INPUT(xyz_tensor);
+    CHECK_INPUT(new_xyz_batch_cnt_tensor);
+    CHECK_INPUT(xyz_batch_cnt_tensor);
+
+    const float *new_xyz = new_xyz_tensor.data<float>();
+    const float *xyz = xyz_tensor.data<float>();
+    const int *new_xyz_batch_cnt = new_xyz_batch_cnt_tensor.data<int>();
+    const int *xyz_batch_cnt = xyz_batch_cnt_tensor.data<int>();
+    int *idx = idx_tensor.data<int>();
+
+    ball_query_kernel_launcher_stack(B, M, radius, nsample, new_xyz, new_xyz_batch_cnt, xyz, xyz_batch_cnt, idx);
+    return 1;
+}
diff --git a/opencood/opencood/pcdet_utils/pointnet2/pointnet2_stack/src/ball_query_gpu.cu b/opencood/opencood/pcdet_utils/pointnet2/pointnet2_stack/src/ball_query_gpu.cu
new file mode 100755
index 00000000..adaa6b1e
--- /dev/null
+++ b/opencood/opencood/pcdet_utils/pointnet2/pointnet2_stack/src/ball_query_gpu.cu
@@ -0,0 +1,90 @@
+/*
+Stacked-batch-data version of ball query, modified from the original implementation of official PointNet++ codes.
+Written by Shaoshuai Shi
+All Rights Reserved 2019-2020.
+*/
+
+
+#include <math.h>
+#include <stdio.h>
+#include <stdlib.h>
+
+#include "ball_query_gpu.h"
+#include "cuda_utils.h"
+
+
+__global__ void ball_query_kernel_stack(int B, int M, float radius, int nsample, \
+    const float *new_xyz, const int *new_xyz_batch_cnt, const float *xyz, const int *xyz_batch_cnt, int *idx) {
+    // :param xyz: (N1 + N2 ..., 3) xyz coordinates of the features
+    // :param xyz_batch_cnt: (batch_size), [N1, N2, ...]
+    // :param new_xyz: (M1 + M2 ..., 3) centers of the ball query
+    // :param new_xyz_batch_cnt: (batch_size), [M1, M2, ...]
+    // output:
+    //      idx: (M, nsample)
+    int pt_idx = blockIdx.x * blockDim.x + threadIdx.x;
+    if (pt_idx >= M) return;
+
+    int bs_idx = 0, pt_cnt = new_xyz_batch_cnt[0];
+    for (int k = 1; k < B; k++){
+        if (pt_idx < pt_cnt) break;
+        pt_cnt += new_xyz_batch_cnt[k];
+        bs_idx = k;
+    }
+
+    int xyz_batch_start_idx = 0;
+    for (int k = 0; k < bs_idx; k++) xyz_batch_start_idx += xyz_batch_cnt[k];
+    // for (int k = 0; k < bs_idx; k++) new_xyz_batch_start_idx += new_xyz_batch_cnt[k];
+
+    new_xyz += pt_idx * 3;
+    xyz += xyz_batch_start_idx * 3;
+    idx += pt_idx * nsample;
+
+    float radius2 = radius * radius;
+    float new_x = new_xyz[0];
+    float new_y = new_xyz[1];
+    float new_z = new_xyz[2];
+    int n = xyz_batch_cnt[bs_idx];
+
+    int cnt = 0;
+    for (int k = 0; k < n; ++k) {
+        float x = xyz[k * 3 + 0];
+        float y = xyz[k * 3 + 1];
+        float z = xyz[k * 3 + 2];
+        float d2 = (new_x - x) * (new_x - x) + (new_y - y) * (new_y - y) + (new_z - z) * (new_z - z);
+        if (d2 < radius2){
+            if (cnt == 0){
+                for (int l = 0; l < nsample; ++l) {
+                    idx[l] = k;
+                }
+            }
+            idx[cnt] = k;
+            ++cnt;
+            if (cnt >= nsample) break;
+        }
+    }
+    if (cnt == 0) idx[0] = -1;
+}
+
+
+void ball_query_kernel_launcher_stack(int B, int M, float radius, int nsample,
+    const float *new_xyz, const int *new_xyz_batch_cnt, const float *xyz, const int *xyz_batch_cnt, int *idx){
+    // :param xyz: (N1 + N2 ..., 3) xyz coordinates of the features
+    // :param xyz_batch_cnt: (batch_size), [N1, N2, ...]
+    // :param new_xyz: (M1 + M2 ..., 3) centers of the ball query
+    // :param new_xyz_batch_cnt: (batch_size), [M1, M2, ...]
+    // output:
+    //      idx: (M, nsample)
+
+    cudaError_t err;
+
+    dim3 blocks(DIVUP(M, THREADS_PER_BLOCK));  // blockIdx.x(col), blockIdx.y(row)
+    dim3 threads(THREADS_PER_BLOCK);
+
+    ball_query_kernel_stack<<<blocks, threads>>>(B, M, radius, nsample, new_xyz, new_xyz_batch_cnt, xyz, xyz_batch_cnt, idx);
+    // cudaDeviceSynchronize();  // for using printf in kernel function
+    err = cudaGetLastError();
+    if (cudaSuccess != err) {
+        fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
+        exit(-1);
+    }
+}
diff --git a/opencood/opencood/pcdet_utils/pointnet2/pointnet2_stack/src/ball_query_gpu.h b/opencood/opencood/pcdet_utils/pointnet2/pointnet2_stack/src/ball_query_gpu.h
new file mode 100755
index 00000000..c74f1201
--- /dev/null
+++ b/opencood/opencood/pcdet_utils/pointnet2/pointnet2_stack/src/ball_query_gpu.h
@@ -0,0 +1,25 @@
+/*
+Stacked-batch-data version of ball query, modified from the original implementation of official PointNet++ codes.
+Written by Shaoshuai Shi
+All Rights Reserved 2019-2020.
+*/
+
+
+#ifndef _STACK_BALL_QUERY_GPU_H
+#define _STACK_BALL_QUERY_GPU_H
+
+#include <torch/serialize/tensor.h>
+#include <vector>
+#include <cuda.h>
+#include <cuda_runtime_api.h>
+
+int ball_query_wrapper_stack(int B, int M, float radius, int nsample,
+    at::Tensor new_xyz_tensor, at::Tensor new_xyz_batch_cnt_tensor,
+    at::Tensor xyz_tensor, at::Tensor xyz_batch_cnt_tensor, at::Tensor idx_tensor);
+
+
+void ball_query_kernel_launcher_stack(int B, int M, float radius, int nsample,
+    const float *new_xyz, const int *new_xyz_batch_cnt, const float *xyz, const int *xyz_batch_cnt, int *idx);
+
+
+#endif
diff --git a/opencood/opencood/pcdet_utils/pointnet2/pointnet2_stack/src/cuda_utils.h b/opencood/opencood/pcdet_utils/pointnet2/pointnet2_stack/src/cuda_utils.h
new file mode 100755
index 00000000..c1670f1c
--- /dev/null
+++ b/opencood/opencood/pcdet_utils/pointnet2/pointnet2_stack/src/cuda_utils.h
@@ -0,0 +1,9 @@
+#ifndef _STACK_CUDA_UTILS_H
+#define _STACK_CUDA_UTILS_H
+
+#include <cmath>
+
+#define THREADS_PER_BLOCK 256
+#define DIVUP(m,n) ((m) / (n) + ((m) % (n) > 0))
+
+#endif
diff --git a/opencood/opencood/pcdet_utils/pointnet2/pointnet2_stack/src/group_points.cpp b/opencood/opencood/pcdet_utils/pointnet2/pointnet2_stack/src/group_points.cpp
new file mode 100755
index 00000000..4f48b6d7
--- /dev/null
+++ b/opencood/opencood/pcdet_utils/pointnet2/pointnet2_stack/src/group_points.cpp
@@ -0,0 +1,70 @@
+/*
+Stacked-batch-data version of point grouping, modified from the original implementation of official PointNet++ codes.
+Written by Shaoshuai Shi
+All Rights Reserved 2019-2020.
+*/
+
+
+#include <torch/serialize/tensor.h>
+#include <cuda.h>
+#include <cuda_runtime_api.h>
+#include <vector>
+#include <THC/THC.h>
+#include "group_points_gpu.h"
+
+extern THCState *state;
+#define CHECK_CUDA(x) do { \
+  if (!x.type().is_cuda()) { \
+    fprintf(stderr, "%s must be CUDA tensor at %s:%d\n", #x, __FILE__, __LINE__); \
+    exit(-1); \
+  } \
+} while (0)
+#define CHECK_CONTIGUOUS(x) do { \
+  if (!x.is_contiguous()) { \
+    fprintf(stderr, "%s must be contiguous tensor at %s:%d\n", #x, __FILE__, __LINE__); \
+    exit(-1); \
+  } \
+} while (0)
+#define CHECK_INPUT(x) CHECK_CUDA(x);CHECK_CONTIGUOUS(x)
+
+
+int group_points_grad_wrapper_stack(int B, int M, int C, int N, int nsample,
+    at::Tensor grad_out_tensor, at::Tensor idx_tensor, at::Tensor idx_batch_cnt_tensor,
+    at::Tensor features_batch_cnt_tensor, at::Tensor grad_features_tensor) {
+
+    CHECK_INPUT(grad_out_tensor);
+    CHECK_INPUT(idx_tensor);
+    CHECK_INPUT(idx_batch_cnt_tensor);
+    CHECK_INPUT(features_batch_cnt_tensor);
+    CHECK_INPUT(grad_features_tensor);
+
+    const float *grad_out = grad_out_tensor.data<float>();
+    const int *idx = idx_tensor.data<int>();
+    const int *idx_batch_cnt = idx_batch_cnt_tensor.data<int>();
+    const int *features_batch_cnt = features_batch_cnt_tensor.data<int>();
+    float *grad_features = grad_features_tensor.data<float>();
+
+    group_points_grad_kernel_launcher_stack(B, M, C, N, nsample, grad_out, idx, idx_batch_cnt, features_batch_cnt, grad_features);
+    return 1;
+}
+
+
+int group_points_wrapper_stack(int B, int M, int C, int nsample,
+    at::Tensor features_tensor, at::Tensor features_batch_cnt_tensor,
+    at::Tensor idx_tensor, at::Tensor idx_batch_cnt_tensor, at::Tensor out_tensor) {
+
+    CHECK_INPUT(features_tensor);
+    CHECK_INPUT(features_batch_cnt_tensor);
+    CHECK_INPUT(idx_tensor);
+    CHECK_INPUT(idx_batch_cnt_tensor);
+    CHECK_INPUT(out_tensor);
+
+    const float *features = features_tensor.data<float>();
+    const int *idx = idx_tensor.data<int>();
+    const int *features_batch_cnt = features_batch_cnt_tensor.data<int>();
+    const int *idx_batch_cnt = idx_batch_cnt_tensor.data<int>();
+    float *out = out_tensor.data<float>();
+
+    group_points_kernel_launcher_stack(B, M, C, nsample, features, features_batch_cnt, idx, idx_batch_cnt, out);
+    return 1;
+}
\ No newline at end of file
diff --git a/opencood/opencood/pcdet_utils/pointnet2/pointnet2_stack/src/group_points_gpu.cu b/opencood/opencood/pcdet_utils/pointnet2/pointnet2_stack/src/group_points_gpu.cu
new file mode 100755
index 00000000..62e341e9
--- /dev/null
+++ b/opencood/opencood/pcdet_utils/pointnet2/pointnet2_stack/src/group_points_gpu.cu
@@ -0,0 +1,125 @@
+/*
+Stacked-batch-data version of point grouping, modified from the original implementation of official PointNet++ codes.
+Written by Shaoshuai Shi
+All Rights Reserved 2019-2020.
+*/
+
+
+#include <stdio.h>
+#include <stdlib.h>
+
+#include "cuda_utils.h"
+#include "group_points_gpu.h"
+
+
+__global__ void group_points_grad_kernel_stack(int B, int M, int C, int N, int nsample,
+    const float *grad_out, const int *idx, const int *idx_batch_cnt, const int *features_batch_cnt, float *grad_features) {
+    // :param grad_out: (M1 + M2 ..., C, nsample) tensor of the gradients of the output from forward
+    // :param idx: (M1 + M2 ..., nsample) tensor containing the indicies of features to group with
+    // :param idx_batch_cnt: (batch_size) [M1 + M2 ...] tensor containing the indicies of features to group with
+    // :param features_batch_cnt: (batch_size) [N1 + N2 ...] tensor containing the indicies of features to group with
+    // :return:
+    //     grad_features: (N1 + N2 ..., C) gradient of the features
+    int index = blockIdx.x * blockDim.x + threadIdx.x;
+    int sample_idx = index % nsample;
+    int C_idx = (index / nsample) % C;
+    int pt_idx = (index / nsample / C);
+
+    if (pt_idx >= M || C_idx >= C || sample_idx >= nsample) return;
+
+    int bs_idx = 0, pt_cnt = idx_batch_cnt[0];
+    for (int k = 1; k < B; k++){
+        if (pt_idx < pt_cnt) break;
+        pt_cnt += idx_batch_cnt[k];
+        bs_idx = k;
+    }
+
+    int features_batch_start_idx = 0;
+    for (int k = 0; k < bs_idx; k++) features_batch_start_idx += features_batch_cnt[k];
+
+    grad_out += pt_idx * C * nsample + C_idx * nsample + sample_idx;
+    idx += pt_idx * nsample + sample_idx;
+    grad_features += (features_batch_start_idx + idx[0]) * C + C_idx;
+
+    atomicAdd(grad_features, grad_out[0]);
+}
+
+void group_points_grad_kernel_launcher_stack(int B, int M, int C, int N, int nsample,
+    const float *grad_out, const int *idx, const int *idx_batch_cnt, const int *features_batch_cnt, float *grad_features) {
+    // :param grad_out: (M1 + M2 ..., C, nsample) tensor of the gradients of the output from forward
+    // :param idx: (M1 + M2 ..., nsample) tensor containing the indicies of features to group with
+    // :param idx_batch_cnt: (batch_size) [M1 + M2 ...] tensor containing the indicies of features to group with
+    // :param features_batch_cnt: (batch_size) [N1 + N2 ...] tensor containing the indicies of features to group with
+    // :return:
+    //     grad_features: (N1 + N2 ..., C) gradient of the features
+
+    cudaError_t err;
+    // dim3 blocks(DIVUP(npoints * nsample, THREADS_PER_BLOCK), c, b);  // blockIdx.x(col), blockIdx.y(row)
+    dim3 blocks(DIVUP(M * C * nsample, THREADS_PER_BLOCK));  // blockIdx.x(col), blockIdx.y(row)
+    dim3 threads(THREADS_PER_BLOCK);
+
+    group_points_grad_kernel_stack<<<blocks, threads>>>(B, M, C, N, nsample, grad_out, idx, idx_batch_cnt, features_batch_cnt, grad_features);
+
+    err = cudaGetLastError();
+    if (cudaSuccess != err) {
+        fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
+        exit(-1);
+    }
+}
+
+
+__global__ void group_points_kernel_stack(int B, int M, int C, int nsample,
+    const float *features, const int *features_batch_cnt, const int *idx, const int *idx_batch_cnt, float *out) {
+    // :param features: (N1 + N2 ..., C) tensor of features to group
+    // :param features_batch_cnt: (batch_size) [N1 + N2 ...] tensor containing the indicies of features to group with
+    // :param idx: (M1 + M2 ..., nsample) tensor containing the indicies of features to group with
+    // :param idx_batch_cnt: (batch_size) [M1 + M2 ...] tensor containing the indicies of features to group with
+    // :return:
+    //     output: (M1 + M2, C, nsample) tensor
+    int index = blockIdx.x * blockDim.x + threadIdx.x;
+    int sample_idx = index % nsample;
+    int C_idx = (index / nsample) % C;
+    int pt_idx = (index / nsample / C);
+
+    if (pt_idx >= M || C_idx >= C || sample_idx >= nsample) return;
+
+    int bs_idx = 0, pt_cnt = idx_batch_cnt[0];
+    for (int k = 1; k < B; k++){
+        if (pt_idx < pt_cnt) break;
+        pt_cnt += idx_batch_cnt[k];
+        bs_idx = k;
+    }
+
+    int features_batch_start_idx = 0;
+    for (int k = 0; k < bs_idx; k++) features_batch_start_idx += features_batch_cnt[k];
+    features += features_batch_start_idx * C;
+
+    idx += pt_idx * nsample + sample_idx;
+    int in_idx = idx[0] * C + C_idx;
+    int out_idx = pt_idx * C * nsample + C_idx * nsample + sample_idx;
+
+    out[out_idx] = features[in_idx];
+}
+
+
+void group_points_kernel_launcher_stack(int B, int M, int C, int nsample,
+    const float *features, const int *features_batch_cnt, const int *idx, const int *idx_batch_cnt, float *out) {
+    // :param features: (N1 + N2 ..., C) tensor of features to group
+    // :param features_batch_cnt: (batch_size) [N1 + N2 ...] tensor containing the indicies of features to group with
+    // :param idx: (M1 + M2 ..., nsample) tensor containing the indicies of features to group with
+    // :param idx_batch_cnt: (batch_size) [M1 + M2 ...] tensor containing the indicies of features to group with
+    // :return:
+    //     output: (M1 + M2, C, nsample) tensor
+
+    cudaError_t err;
+    dim3 blocks(DIVUP(M * C * nsample, THREADS_PER_BLOCK));  // blockIdx.x(col), blockIdx.y(row)
+    dim3 threads(THREADS_PER_BLOCK);
+
+    group_points_kernel_stack<<<blocks, threads>>>(B, M, C, nsample, features, features_batch_cnt, idx, idx_batch_cnt, out);
+    // cudaDeviceSynchronize();  // for using printf in kernel function
+    err = cudaGetLastError();
+    if (cudaSuccess != err) {
+        fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
+        exit(-1);
+    }
+}
diff --git a/opencood/opencood/pcdet_utils/pointnet2/pointnet2_stack/src/group_points_gpu.h b/opencood/opencood/pcdet_utils/pointnet2/pointnet2_stack/src/group_points_gpu.h
new file mode 100755
index 00000000..4a266216
--- /dev/null
+++ b/opencood/opencood/pcdet_utils/pointnet2/pointnet2_stack/src/group_points_gpu.h
@@ -0,0 +1,31 @@
+/*
+Stacked-batch-data version of point grouping, modified from the original implementation of official PointNet++ codes.
+Written by Shaoshuai Shi
+All Rights Reserved 2019-2020.
+*/
+
+
+#ifndef _STACK_GROUP_POINTS_GPU_H
+#define _STACK_GROUP_POINTS_GPU_H
+
+#include <torch/serialize/tensor.h>
+#include <cuda.h>
+#include <cuda_runtime_api.h>
+#include <vector>
+
+
+int group_points_wrapper_stack(int B, int M, int C, int nsample,
+    at::Tensor features_tensor, at::Tensor features_batch_cnt_tensor,
+    at::Tensor idx_tensor, at::Tensor idx_batch_cnt_tensor, at::Tensor out_tensor);
+
+void group_points_kernel_launcher_stack(int B, int M, int C, int nsample,
+    const float *features, const int *features_batch_cnt, const int *idx, const int *idx_batch_cnt, float *out);
+
+int group_points_grad_wrapper_stack(int B, int M, int C, int N, int nsample,
+    at::Tensor grad_out_tensor, at::Tensor idx_tensor, at::Tensor idx_batch_cnt_tensor,
+    at::Tensor features_batch_cnt_tensor, at::Tensor grad_features_tensor);
+
+void group_points_grad_kernel_launcher_stack(int B, int M, int C, int N, int nsample,
+    const float *grad_out, const int *idx, const int *idx_batch_cnt, const int *features_batch_cnt, float *grad_features);
+
+#endif
diff --git a/opencood/opencood/pcdet_utils/pointnet2/pointnet2_stack/src/interpolate.cpp b/opencood/opencood/pcdet_utils/pointnet2/pointnet2_stack/src/interpolate.cpp
new file mode 100755
index 00000000..76135eec
--- /dev/null
+++ b/opencood/opencood/pcdet_utils/pointnet2/pointnet2_stack/src/interpolate.cpp
@@ -0,0 +1,110 @@
+/*
+Stacked-batch-data version of point interpolation, modified from the original implementation of official PointNet++ codes.
+Written by Shaoshuai Shi
+All Rights Reserved 2019-2020.
+*/
+
+
+#include <torch/serialize/tensor.h>
+#include <vector>
+#include <THC/THC.h>
+#include <math.h>
+#include <stdio.h>
+#include <stdlib.h>
+#include <cuda.h>
+#include <cuda_runtime_api.h>
+#include "interpolate_gpu.h"
+
+extern THCState *state;
+
+#define CHECK_CUDA(x) do { \
+  if (!x.type().is_cuda()) { \
+    fprintf(stderr, "%s must be CUDA tensor at %s:%d\n", #x, __FILE__, __LINE__); \
+    exit(-1); \
+  } \
+} while (0)
+#define CHECK_CONTIGUOUS(x) do { \
+  if (!x.is_contiguous()) { \
+    fprintf(stderr, "%s must be contiguous tensor at %s:%d\n", #x, __FILE__, __LINE__); \
+    exit(-1); \
+  } \
+} while (0)
+#define CHECK_INPUT(x) CHECK_CUDA(x);CHECK_CONTIGUOUS(x)
+
+
+void three_nn_wrapper_stack(at::Tensor unknown_tensor, 
+    at::Tensor unknown_batch_cnt_tensor, at::Tensor known_tensor, 
+    at::Tensor known_batch_cnt_tensor, at::Tensor dist2_tensor, at::Tensor idx_tensor){
+    // unknown: (N1 + N2 ..., 3)
+    // unknown_batch_cnt: (batch_size), [N1, N2, ...]
+    // known: (M1 + M2 ..., 3)
+    // known_batch_cnt: (batch_size), [M1, M2, ...]
+    // Return:
+    // dist: (N1 + N2 ..., 3)  l2 distance to the three nearest neighbors
+    // idx: (N1 + N2 ..., 3)  index of the three nearest neighbors
+    CHECK_INPUT(unknown_tensor);
+    CHECK_INPUT(unknown_batch_cnt_tensor);
+    CHECK_INPUT(known_tensor);
+    CHECK_INPUT(known_batch_cnt_tensor);
+    CHECK_INPUT(dist2_tensor);
+    CHECK_INPUT(idx_tensor);
+
+    int batch_size = unknown_batch_cnt_tensor.size(0);
+    int N = unknown_tensor.size(0);
+    int M = known_tensor.size(0);
+    const float *unknown = unknown_tensor.data<float>();
+    const int *unknown_batch_cnt = unknown_batch_cnt_tensor.data<int>();
+    const float *known = known_tensor.data<float>();
+    const int *known_batch_cnt = known_batch_cnt_tensor.data<int>();
+    float *dist2 = dist2_tensor.data<float>();
+    int *idx = idx_tensor.data<int>();
+
+    three_nn_kernel_launcher_stack(batch_size, N, M, unknown, unknown_batch_cnt, known, known_batch_cnt, dist2, idx);
+}
+
+
+void three_interpolate_wrapper_stack(at::Tensor features_tensor, 
+    at::Tensor idx_tensor, at::Tensor weight_tensor, at::Tensor out_tensor) {
+    // features_tensor: (M1 + M2 ..., C)
+    // idx_tensor: [N1 + N2 ..., 3]
+    // weight_tensor: [N1 + N2 ..., 3]
+    // Return:
+    // out_tensor: (N1 + N2 ..., C)
+    CHECK_INPUT(features_tensor);
+    CHECK_INPUT(idx_tensor);
+    CHECK_INPUT(weight_tensor);
+    CHECK_INPUT(out_tensor);
+
+    int N = out_tensor.size(0);
+    int channels = features_tensor.size(1);
+    const float *features = features_tensor.data<float>();
+    const float *weight = weight_tensor.data<float>();
+    const int *idx = idx_tensor.data<int>();
+    float *out = out_tensor.data<float>();
+
+    three_interpolate_kernel_launcher_stack(N, channels, features, idx, weight, out);
+}
+
+
+void three_interpolate_grad_wrapper_stack(at::Tensor grad_out_tensor, at::Tensor idx_tensor,
+    at::Tensor weight_tensor, at::Tensor grad_features_tensor) {
+    // grad_out_tensor: (N1 + N2 ..., C)
+    // idx_tensor: [N1 + N2 ..., 3]
+    // weight_tensor: [N1 + N2 ..., 3]
+    // Return:
+    // grad_features_tensor: (M1 + M2 ..., C)
+    CHECK_INPUT(grad_out_tensor);
+    CHECK_INPUT(idx_tensor);
+    CHECK_INPUT(weight_tensor);
+    CHECK_INPUT(grad_features_tensor);
+
+    int N = grad_out_tensor.size(0);
+    int channels = grad_out_tensor.size(1);
+    const float *grad_out = grad_out_tensor.data<float>();
+    const float *weight = weight_tensor.data<float>();
+    const int *idx = idx_tensor.data<int>();
+    float *grad_features = grad_features_tensor.data<float>();
+    
+    // printf("N=%d, channels=%d\n", N, channels);
+    three_interpolate_grad_kernel_launcher_stack(N, channels, grad_out, idx, weight, grad_features);
+}
\ No newline at end of file
diff --git a/opencood/opencood/pcdet_utils/pointnet2/pointnet2_stack/src/interpolate_gpu.cu b/opencood/opencood/pcdet_utils/pointnet2/pointnet2_stack/src/interpolate_gpu.cu
new file mode 100755
index 00000000..66f64085
--- /dev/null
+++ b/opencood/opencood/pcdet_utils/pointnet2/pointnet2_stack/src/interpolate_gpu.cu
@@ -0,0 +1,195 @@
+/*
+Stacked-batch-data version of point interpolation, modified from the original implementation of official PointNet++ codes.
+Written by Shaoshuai Shi
+All Rights Reserved 2019-2020.
+*/
+
+
+#include <math.h>
+#include <stdio.h>
+#include <stdlib.h>
+
+#include "cuda_utils.h"
+#include "interpolate_gpu.h"
+
+
+__global__ void three_nn_kernel_stack(int batch_size, int N, int M, const float *unknown, 
+    const int *unknown_batch_cnt, const float *known, const int *known_batch_cnt,
+    float *dist2, int *idx) {
+    // unknown: (N1 + N2 ..., 3)
+    // unknown_batch_cnt: (batch_size), [N1, N2, ...]
+    // known: (M1 + M2 ..., 3)
+    // known_batch_cnt: (batch_size), [M1, M2, ...]
+    // Return:
+    // dist: (N1 + N2 ..., 3)  l2 distance to the three nearest neighbors
+    // idx: (N1 + N2 ..., 3)  index of the three nearest neighbors
+
+    int pt_idx = blockIdx.x * blockDim.x + threadIdx.x;
+    if (pt_idx >= N) return;
+
+    int bs_idx = 0, pt_cnt = unknown_batch_cnt[0];
+    for (int k = 1; k < batch_size; k++){
+        if (pt_idx < pt_cnt) break;
+        pt_cnt += unknown_batch_cnt[k];
+        bs_idx = k;
+    }
+
+    int cur_num_known_points = known_batch_cnt[bs_idx];
+
+    int known_batch_start_idx = 0;
+    for (int k = 0; k < bs_idx; k++) known_batch_start_idx += known_batch_cnt[k];
+
+    known += known_batch_start_idx * 3;
+    unknown += pt_idx * 3;
+    dist2 += pt_idx * 3;
+    idx += pt_idx * 3;
+
+    float ux = unknown[0];
+    float uy = unknown[1];
+    float uz = unknown[2];
+
+    double best1 = 1e40, best2 = 1e40, best3 = 1e40;
+    int besti1 = 0, besti2 = 0, besti3 = 0;
+    for (int k = 0; k < cur_num_known_points; ++k) {
+        float x = known[k * 3 + 0];
+        float y = known[k * 3 + 1];
+        float z = known[k * 3 + 2];
+        float d = (ux - x) * (ux - x) + (uy - y) * (uy - y) + (uz - z) * (uz - z);
+        if (d < best1) {
+            best3 = best2; besti3 = besti2;
+            best2 = best1; besti2 = besti1;
+            best1 = d; besti1 = k;
+        } 
+        else if (d < best2) {
+            best3 = best2; besti3 = besti2;
+            best2 = d; besti2 = k;
+        } 
+        else if (d < best3) {
+            best3 = d; besti3 = k;
+        }
+    }
+    dist2[0] = best1; dist2[1] = best2; dist2[2] = best3;
+    idx[0] = besti1 + known_batch_start_idx; 
+    idx[1] = besti2 + known_batch_start_idx; 
+    idx[2] = besti3 + known_batch_start_idx;
+}
+
+
+void three_nn_kernel_launcher_stack(int batch_size, int N, int M, const float *unknown, 
+    const int *unknown_batch_cnt, const float *known, const int *known_batch_cnt,
+    float *dist2, int *idx) {
+    // unknown: (N1 + N2 ..., 3)
+    // unknown_batch_cnt: (batch_size), [N1, N2, ...]
+    // known: (M1 + M2 ..., 3)
+    // known_batch_cnt: (batch_size), [M1, M2, ...]
+    // Return:
+    // dist: (N1 + N2 ..., 3)  l2 distance to the three nearest neighbors
+    // idx: (N1 + N2 ..., 3)  index of the three nearest neighbors
+
+    cudaError_t err;
+    dim3 blocks(DIVUP(N, THREADS_PER_BLOCK));  // blockIdx.x(col), blockIdx.y(row)
+    dim3 threads(THREADS_PER_BLOCK);
+
+    three_nn_kernel_stack<<<blocks, threads>>>(
+        batch_size, N, M, unknown, unknown_batch_cnt, 
+        known, known_batch_cnt, dist2, idx
+    );
+
+    err = cudaGetLastError();
+    if (cudaSuccess != err) {
+        fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
+        exit(-1);
+    }
+}
+
+
+
+__global__ void three_interpolate_kernel_stack(int N, int channels, const float *features, 
+    const int *idx, const float *weight, float *out) {
+    // features: (M1 + M2 ..., C)
+    // idx: [N1 + N2 ..., 3]
+    // weight: [N1 + N2 ..., 3]
+    // Return:
+    // out: (N1 + N2 ..., C)
+
+    int c_idx = blockIdx.y;
+    int pt_idx = blockIdx.x * blockDim.x + threadIdx.x;
+    if (pt_idx >= N || c_idx >= channels) return;
+
+    weight += pt_idx * 3;
+    idx += pt_idx * 3;
+    out += pt_idx * channels + c_idx;
+
+    out[0] = weight[0] * features[idx[0] * channels + c_idx] + 
+        weight[1] * features[idx[1] * channels + c_idx] + 
+        weight[2] * features[idx[2] * channels + c_idx];
+}
+
+
+
+void three_interpolate_kernel_launcher_stack(int N, int channels,
+    const float *features, const int *idx, const float *weight, float *out) {
+    // features: (M1 + M2 ..., C)
+    // idx: [N1 + N2 ..., 3]
+    // weight: [N1 + N2 ..., 3]
+    // Return:
+    // out: (N1 + N2 ..., C)
+
+    cudaError_t err;
+    dim3 blocks(DIVUP(N, THREADS_PER_BLOCK), channels);
+    dim3 threads(THREADS_PER_BLOCK);
+    three_interpolate_kernel_stack<<<blocks, threads>>>(N, channels, features, idx, weight, out);
+
+    err = cudaGetLastError();
+    if (cudaSuccess != err) {
+        fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
+        exit(-1);
+    }
+}
+
+
+__global__ void three_interpolate_grad_kernel_stack(int N, int channels, const float *grad_out, 
+    const int *idx, const float *weight, float *grad_features) {
+    // grad_out_tensor: (N1 + N2 ..., C)
+    // idx_tensor: [N1 + N2 ..., 3]
+    // weight_tensor: [N1 + N2 ..., 3]
+    // Return:
+    // grad_features_tensor: (M1 + M2 ..., C)
+
+    int c_idx = blockIdx.y;
+    int pt_idx = blockIdx.x * blockDim.x + threadIdx.x;
+    if (pt_idx >= N || c_idx >= channels) return;
+
+    grad_out += pt_idx * channels + c_idx;
+    weight += pt_idx * 3;
+    idx += pt_idx * 3;
+    
+    // printf("pt_idx=%d, c_idx=%d, idx=(%d, %d, %d), grad_out=%f\n", pt_idx, c_idx, idx[0], idx[1], idx[2], grad_out[0]);
+
+    atomicAdd(grad_features + idx[0] * channels + c_idx, grad_out[0] * weight[0]);
+    atomicAdd(grad_features + idx[1] * channels + c_idx, grad_out[0] * weight[1]);
+    atomicAdd(grad_features + idx[2] * channels + c_idx, grad_out[0] * weight[2]);
+}
+
+
+void three_interpolate_grad_kernel_launcher_stack(int N, int channels, const float *grad_out, 
+    const int *idx, const float *weight, float *grad_features) {
+    // grad_out_tensor: (N1 + N2 ..., C)
+    // idx_tensor: [N1 + N2 ..., 3]
+    // weight_tensor: [N1 + N2 ..., 3]
+    // Return:
+    // grad_features_tensor: (M1 + M2 ..., C)
+
+    cudaError_t err;
+    dim3 blocks(DIVUP(N, THREADS_PER_BLOCK), channels);  // blockIdx.x(col), blockIdx.y(row)
+    dim3 threads(THREADS_PER_BLOCK);
+    three_interpolate_grad_kernel_stack<<<blocks, threads>>>(
+        N, channels, grad_out, idx, weight, grad_features
+    );
+
+    err = cudaGetLastError();
+    if (cudaSuccess != err) {
+        fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
+        exit(-1);
+    }
+}
\ No newline at end of file
diff --git a/opencood/opencood/pcdet_utils/pointnet2/pointnet2_stack/src/interpolate_gpu.h b/opencood/opencood/pcdet_utils/pointnet2/pointnet2_stack/src/interpolate_gpu.h
new file mode 100755
index 00000000..12775ec3
--- /dev/null
+++ b/opencood/opencood/pcdet_utils/pointnet2/pointnet2_stack/src/interpolate_gpu.h
@@ -0,0 +1,39 @@
+#ifndef _INTERPOLATE_GPU_H
+#define _INTERPOLATE_GPU_H
+
+#include <torch/serialize/tensor.h>
+#include<vector>
+#include <cuda.h>
+#include <cuda_runtime_api.h>
+
+
+void three_nn_wrapper_stack(at::Tensor unknown_tensor, 
+    at::Tensor unknown_batch_cnt_tensor, at::Tensor known_tensor, 
+    at::Tensor known_batch_cnt_tensor, at::Tensor dist2_tensor, at::Tensor idx_tensor);
+
+
+void three_interpolate_wrapper_stack(at::Tensor features_tensor, 
+    at::Tensor idx_tensor, at::Tensor weight_tensor, at::Tensor out_tensor);
+
+
+
+void three_interpolate_grad_wrapper_stack(at::Tensor grad_out_tensor, at::Tensor idx_tensor,
+    at::Tensor weight_tensor, at::Tensor grad_features_tensor);
+
+
+void three_nn_kernel_launcher_stack(int batch_size, int N, int M, const float *unknown, 
+    const int *unknown_batch_cnt, const float *known, const int *known_batch_cnt,
+    float *dist2, int *idx);
+
+
+void three_interpolate_kernel_launcher_stack(int N, int channels,
+    const float *features, const int *idx, const float *weight, float *out);
+
+
+
+void three_interpolate_grad_kernel_launcher_stack(int N, int channels, const float *grad_out, 
+    const int *idx, const float *weight, float *grad_features);
+
+
+
+#endif
\ No newline at end of file
diff --git a/opencood/opencood/pcdet_utils/pointnet2/pointnet2_stack/src/pointnet2_api.cpp b/opencood/opencood/pcdet_utils/pointnet2/pointnet2_stack/src/pointnet2_api.cpp
new file mode 100755
index 00000000..591f11a7
--- /dev/null
+++ b/opencood/opencood/pcdet_utils/pointnet2/pointnet2_stack/src/pointnet2_api.cpp
@@ -0,0 +1,21 @@
+#include <torch/serialize/tensor.h>
+#include <torch/extension.h>
+
+#include "ball_query_gpu.h"
+#include "group_points_gpu.h"
+#include "sampling_gpu.h"
+#include "interpolate_gpu.h"
+
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+    m.def("ball_query_wrapper", &ball_query_wrapper_stack, "ball_query_wrapper_stack");
+
+    m.def("furthest_point_sampling_wrapper", &furthest_point_sampling_wrapper, "furthest_point_sampling_wrapper");
+
+    m.def("group_points_wrapper", &group_points_wrapper_stack, "group_points_wrapper_stack");
+    m.def("group_points_grad_wrapper", &group_points_grad_wrapper_stack, "group_points_grad_wrapper_stack");
+
+    m.def("three_nn_wrapper", &three_nn_wrapper_stack, "three_nn_wrapper_stack");
+    m.def("three_interpolate_wrapper", &three_interpolate_wrapper_stack, "three_interpolate_wrapper_stack");
+    m.def("three_interpolate_grad_wrapper", &three_interpolate_grad_wrapper_stack, "three_interpolate_grad_wrapper_stack");
+}
diff --git a/opencood/opencood/pcdet_utils/pointnet2/pointnet2_stack/src/sampling.cpp b/opencood/opencood/pcdet_utils/pointnet2/pointnet2_stack/src/sampling.cpp
new file mode 100755
index 00000000..c85e9df2
--- /dev/null
+++ b/opencood/opencood/pcdet_utils/pointnet2/pointnet2_stack/src/sampling.cpp
@@ -0,0 +1,37 @@
+#include <torch/serialize/tensor.h>
+#include <ATen/cuda/CUDAContext.h>
+#include <vector>
+#include <THC/THC.h>
+
+#include "sampling_gpu.h"
+
+extern THCState *state;
+#define CHECK_CUDA(x) do { \
+  if (!x.type().is_cuda()) { \
+    fprintf(stderr, "%s must be CUDA tensor at %s:%d\n", #x, __FILE__, __LINE__); \
+    exit(-1); \
+  } \
+} while (0)
+#define CHECK_CONTIGUOUS(x) do { \
+  if (!x.is_contiguous()) { \
+    fprintf(stderr, "%s must be contiguous tensor at %s:%d\n", #x, __FILE__, __LINE__); \
+    exit(-1); \
+  } \
+} while (0)
+#define CHECK_INPUT(x) CHECK_CUDA(x);CHECK_CONTIGUOUS(x)
+
+
+int furthest_point_sampling_wrapper(int b, int n, int m,
+    at::Tensor points_tensor, at::Tensor temp_tensor, at::Tensor idx_tensor) {
+
+    CHECK_INPUT(points_tensor);
+    CHECK_INPUT(temp_tensor);
+    CHECK_INPUT(idx_tensor);
+
+    const float *points = points_tensor.data<float>();
+    float *temp = temp_tensor.data<float>();
+    int *idx = idx_tensor.data<int>();
+
+    furthest_point_sampling_kernel_launcher(b, n, m, points, temp, idx);
+    return 1;
+}
diff --git a/opencood/opencood/pcdet_utils/pointnet2/pointnet2_stack/src/sampling_gpu.cu b/opencood/opencood/pcdet_utils/pointnet2/pointnet2_stack/src/sampling_gpu.cu
new file mode 100755
index 00000000..02e8f3bf
--- /dev/null
+++ b/opencood/opencood/pcdet_utils/pointnet2/pointnet2_stack/src/sampling_gpu.cu
@@ -0,0 +1,184 @@
+#include <stdio.h>
+#include <stdlib.h>
+
+#include "cuda_utils.h"
+#include "sampling_gpu.h"
+#define TOTAL_THREADS 1024
+
+
+inline int opt_n_threads(int work_size) {
+    const int pow_2 = std::log(static_cast<double>(work_size)) / std::log(2.0);
+
+    return max(min(1 << pow_2, TOTAL_THREADS), 1);
+}
+
+
+__device__ void __update(float *__restrict__ dists, int *__restrict__ dists_i, int idx1, int idx2){
+    const float v1 = dists[idx1], v2 = dists[idx2];
+    const int i1 = dists_i[idx1], i2 = dists_i[idx2];
+    dists[idx1] = max(v1, v2);
+    dists_i[idx1] = v2 > v1 ? i2 : i1;
+}
+
+
+template <unsigned int block_size>
+__global__ void furthest_point_sampling_kernel(int b, int n, int m,
+    const float *__restrict__ dataset, float *__restrict__ temp, int *__restrict__ idxs) {
+    // dataset: (B, N, 3)
+    // tmp: (B, N)
+    // output:
+    //      idx: (B, M)
+
+    if (m <= 0) return;
+    __shared__ float dists[block_size];
+    __shared__ int dists_i[block_size];
+
+    int batch_index = blockIdx.x;
+    dataset += batch_index * n * 3;
+    temp += batch_index * n;
+    idxs += batch_index * m;
+
+    int tid = threadIdx.x;
+    const int stride = block_size;
+
+    int old = 0;
+    if (threadIdx.x == 0)
+    idxs[0] = old;
+
+    __syncthreads();
+    for (int j = 1; j < m; j++) {
+    int besti = 0;
+    float best = -1;
+    float x1 = dataset[old * 3 + 0];
+    float y1 = dataset[old * 3 + 1];
+    float z1 = dataset[old * 3 + 2];
+    for (int k = tid; k < n; k += stride) {
+        float x2, y2, z2;
+        x2 = dataset[k * 3 + 0];
+        y2 = dataset[k * 3 + 1];
+        z2 = dataset[k * 3 + 2];
+        // float mag = (x2 * x2) + (y2 * y2) + (z2 * z2);
+        // if (mag <= 1e-3)
+        // continue;
+
+        float d = (x2 - x1) * (x2 - x1) + (y2 - y1) * (y2 - y1) + (z2 - z1) * (z2 - z1);
+        float d2 = min(d, temp[k]);
+        temp[k] = d2;
+        besti = d2 > best ? k : besti;
+        best = d2 > best ? d2 : best;
+    }
+    dists[tid] = best;
+    dists_i[tid] = besti;
+    __syncthreads();
+
+    if (block_size >= 1024) {
+        if (tid < 512) {
+            __update(dists, dists_i, tid, tid + 512);
+        }
+        __syncthreads();
+    }
+
+    if (block_size >= 512) {
+        if (tid < 256) {
+            __update(dists, dists_i, tid, tid + 256);
+        }
+        __syncthreads();
+    }
+    if (block_size >= 256) {
+        if (tid < 128) {
+            __update(dists, dists_i, tid, tid + 128);
+        }
+        __syncthreads();
+    }
+    if (block_size >= 128) {
+        if (tid < 64) {
+            __update(dists, dists_i, tid, tid + 64);
+        }
+        __syncthreads();
+    }
+    if (block_size >= 64) {
+        if (tid < 32) {
+            __update(dists, dists_i, tid, tid + 32);
+        }
+        __syncthreads();
+    }
+    if (block_size >= 32) {
+        if (tid < 16) {
+            __update(dists, dists_i, tid, tid + 16);
+        }
+        __syncthreads();
+    }
+    if (block_size >= 16) {
+        if (tid < 8) {
+            __update(dists, dists_i, tid, tid + 8);
+        }
+        __syncthreads();
+    }
+    if (block_size >= 8) {
+        if (tid < 4) {
+            __update(dists, dists_i, tid, tid + 4);
+        }
+        __syncthreads();
+    }
+    if (block_size >= 4) {
+        if (tid < 2) {
+            __update(dists, dists_i, tid, tid + 2);
+        }
+        __syncthreads();
+    }
+    if (block_size >= 2) {
+        if (tid < 1) {
+            __update(dists, dists_i, tid, tid + 1);
+        }
+        __syncthreads();
+    }
+
+    old = dists_i[0];
+    if (tid == 0)
+        idxs[j] = old;
+    }
+}
+
+void furthest_point_sampling_kernel_launcher(int b, int n, int m,
+    const float *dataset, float *temp, int *idxs) {
+    // dataset: (B, N, 3)
+    // tmp: (B, N)
+    // output:
+    //      idx: (B, M)
+
+    cudaError_t err;
+    unsigned int n_threads = opt_n_threads(n);
+
+    switch (n_threads) {
+        case 1024:
+        furthest_point_sampling_kernel<1024><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        case 512:
+        furthest_point_sampling_kernel<512><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        case 256:
+        furthest_point_sampling_kernel<256><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        case 128:
+        furthest_point_sampling_kernel<128><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        case 64:
+        furthest_point_sampling_kernel<64><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        case 32:
+        furthest_point_sampling_kernel<32><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        case 16:
+        furthest_point_sampling_kernel<16><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        case 8:
+        furthest_point_sampling_kernel<8><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        case 4:
+        furthest_point_sampling_kernel<4><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        case 2:
+        furthest_point_sampling_kernel<2><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        case 1:
+        furthest_point_sampling_kernel<1><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        default:
+        furthest_point_sampling_kernel<512><<<b, n_threads>>>(b, n, m, dataset, temp, idxs);
+    }
+
+    err = cudaGetLastError();
+    if (cudaSuccess != err) {
+        fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
+        exit(-1);
+    }
+}
diff --git a/opencood/opencood/pcdet_utils/pointnet2/pointnet2_stack/src/sampling_gpu.h b/opencood/opencood/pcdet_utils/pointnet2/pointnet2_stack/src/sampling_gpu.h
new file mode 100755
index 00000000..2ad8aa5b
--- /dev/null
+++ b/opencood/opencood/pcdet_utils/pointnet2/pointnet2_stack/src/sampling_gpu.h
@@ -0,0 +1,15 @@
+#ifndef _SAMPLING_GPU_H
+#define _SAMPLING_GPU_H
+
+#include <torch/serialize/tensor.h>
+#include <ATen/cuda/CUDAContext.h>
+#include<vector>
+
+
+int furthest_point_sampling_wrapper(int b, int n, int m,
+    at::Tensor points_tensor, at::Tensor temp_tensor, at::Tensor idx_tensor);
+
+void furthest_point_sampling_kernel_launcher(int b, int n, int m,
+    const float *dataset, float *temp, int *idxs);
+
+#endif
diff --git a/opencood/opencood/pcdet_utils/roiaware_pool3d/roiaware_pool3d_utils.py b/opencood/opencood/pcdet_utils/roiaware_pool3d/roiaware_pool3d_utils.py
new file mode 100755
index 00000000..f4c21e2d
--- /dev/null
+++ b/opencood/opencood/pcdet_utils/roiaware_pool3d/roiaware_pool3d_utils.py
@@ -0,0 +1,127 @@
+import torch
+import torch.nn as nn
+from torch.autograd import Function
+
+from opencood.utils import common_utils
+from opencood.pcdet_utils.roiaware_pool3d import roiaware_pool3d_cuda
+
+
+def points_in_boxes_cpu(points, boxes):
+    """
+    Args:
+        points: (num_points, 3)
+        boxes: [x, y, z, dx, dy, dz, heading], (x, y, z) is the box center, each box DO NOT overlaps
+    Returns:
+        point_indices: (N, num_points)
+    """
+    assert boxes.shape[1] == 7
+    assert points.shape[1] == 3
+    points, is_numpy = common_utils.check_numpy_to_torch(points)
+    boxes, is_numpy = common_utils.check_numpy_to_torch(boxes)
+
+    point_indices = points.new_zeros((boxes.shape[0], points.shape[0]), dtype=torch.int)
+    roiaware_pool3d_cuda.points_in_boxes_cpu(boxes.float().contiguous(), points.float().contiguous(), point_indices)
+
+    return point_indices.numpy() if is_numpy else point_indices
+
+
+def points_in_boxes_gpu(points, boxes):
+    """
+    :param points: (B, M, 3)
+    :param boxes: (B, T, 7), num_valid_boxes <= T
+    :return box_idxs_of_pts: (B, M), default background = -1
+    """
+    assert boxes.shape[0] == points.shape[0]
+    assert boxes.shape[2] == 7 and points.shape[2] == 3
+    # #######
+    # import matplotlib.pyplot as plt
+    # ax = plt.figure(figsize=(8, 8)).add_subplot(1, 1, 1)
+    # ax.set_aspect('equal', 'box')
+    # ax.set(xlim=(-50, 50),
+    #        ylim=(-41.6, 41.6))
+    # points0 = points[0].cpu().detach().numpy()
+    # boxes0 = boxes[0].cpu().detach().numpy()
+    # ax.plot(points0[:, 0], points0[:, 1], 'y.', markersize=3)
+    # ax.plot(boxes0[:, 0], boxes0[:, 1], 'r.', markersize=10)
+    # plt.xlabel('x')
+    # plt.ylabel('y')
+    #
+    # plt.show()
+    # plt.close()
+    # ########
+    batch_size, num_points, _ = points.shape
+
+    box_idxs_of_pts = points.new_zeros((batch_size, num_points), dtype=torch.int).fill_(-1)
+    roiaware_pool3d_cuda.points_in_boxes_gpu(boxes.contiguous(), points.contiguous(), box_idxs_of_pts)
+
+    return box_idxs_of_pts
+
+
+class RoIAwarePool3d(nn.Module):
+    def __init__(self, out_size, max_pts_each_voxel=128):
+        super().__init__()
+        self.out_size = out_size
+        self.max_pts_each_voxel = max_pts_each_voxel
+
+    def forward(self, rois, pts, pts_feature, pool_method='max'):
+        assert pool_method in ['max', 'avg']
+        return RoIAwarePool3dFunction.apply(rois, pts, pts_feature, self.out_size, self.max_pts_each_voxel, pool_method)
+
+
+class RoIAwarePool3dFunction(Function):
+    @staticmethod
+    def forward(ctx, rois, pts, pts_feature, out_size, max_pts_each_voxel, pool_method):
+        """
+        Args:
+            ctx:
+            rois: (N, 7) [x, y, z, dx, dy, dz, heading] (x, y, z) is the box center
+            pts: (npoints, 3)
+            pts_feature: (npoints, C)
+            out_size: int or tuple, like 7 or (7, 7, 7)
+            max_pts_each_voxel:
+            pool_method: 'max' or 'avg'
+
+        Returns:
+            pooled_features: (N, out_x, out_y, out_z, C)
+        """
+        assert rois.shape[1] == 7 and pts.shape[1] == 3
+        if isinstance(out_size, int):
+            out_x = out_y = out_z = out_size
+        else:
+            assert len(out_size) == 3
+            for k in range(3):
+                assert isinstance(out_size[k], int)
+            out_x, out_y, out_z = out_size
+
+        num_rois = rois.shape[0]
+        num_channels = pts_feature.shape[-1]
+        num_pts = pts.shape[0]
+
+        pooled_features = pts_feature.new_zeros((num_rois, out_x, out_y, out_z, num_channels))
+        argmax = pts_feature.new_zeros((num_rois, out_x, out_y, out_z, num_channels), dtype=torch.int)
+        pts_idx_of_voxels = pts_feature.new_zeros((num_rois, out_x, out_y, out_z, max_pts_each_voxel), dtype=torch.int)
+
+        pool_method_map = {'max': 0, 'avg': 1}
+        pool_method = pool_method_map[pool_method]
+        roiaware_pool3d_cuda.forward(rois, pts, pts_feature, argmax, pts_idx_of_voxels, pooled_features, pool_method)
+
+        ctx.roiaware_pool3d_for_backward = (pts_idx_of_voxels, argmax, pool_method, num_pts, num_channels)
+        return pooled_features
+
+    @staticmethod
+    def backward(ctx, grad_out):
+        """
+        :param grad_out: (N, out_x, out_y, out_z, C)
+        :return:
+            grad_in: (npoints, C)
+        """
+        pts_idx_of_voxels, argmax, pool_method, num_pts, num_channels = ctx.roiaware_pool3d_for_backward
+
+        grad_in = grad_out.new_zeros((num_pts, num_channels))
+        roiaware_pool3d_cuda.backward(pts_idx_of_voxels, argmax, grad_out.contiguous(), grad_in, pool_method)
+
+        return None, None, grad_in, None, None, None
+
+
+if __name__ == '__main__':
+    pass
diff --git a/opencood/opencood/pcdet_utils/roiaware_pool3d/src/roiaware_pool3d.cpp b/opencood/opencood/pcdet_utils/roiaware_pool3d/src/roiaware_pool3d.cpp
new file mode 100755
index 00000000..00edfef8
--- /dev/null
+++ b/opencood/opencood/pcdet_utils/roiaware_pool3d/src/roiaware_pool3d.cpp
@@ -0,0 +1,177 @@
+/*
+RoI-aware point cloud feature pooling
+Reference paper:  https://arxiv.org/abs/1907.03670
+Written by Shaoshuai Shi
+All Rights Reserved 2019-2020.
+*/
+
+
+#include <torch/serialize/tensor.h>
+#include <torch/extension.h>
+#include <assert.h>
+
+
+//#define CHECK_CUDA(x) AT_CHECK(x.type().is_cuda(), #x, " must be a CUDAtensor ")
+//#define CHECK_CONTIGUOUS(x) AT_CHECK(x.is_contiguous(), #x, " must be contiguous ")
+//#define CHECK_INPUT(x) CHECK_CUDA(x);CHECK_CONTIGUOUS(x)
+
+
+void roiaware_pool3d_launcher(int boxes_num, int pts_num, int channels, int max_pts_each_voxel,
+    int out_x, int out_y, int out_z, const float *rois, const float *pts, const float *pts_feature,
+    int *argmax, int *pts_idx_of_voxels, float *pooled_features, int pool_method);
+
+void roiaware_pool3d_backward_launcher(int boxes_num, int out_x, int out_y, int out_z, int channels, int max_pts_each_voxel,
+    const int *pts_idx_of_voxels, const int *argmax, const float *grad_out, float *grad_in, int pool_method);
+
+void points_in_boxes_launcher(int batch_size, int boxes_num, int pts_num, const float *boxes,
+    const float *pts, int *box_idx_of_points);
+
+int roiaware_pool3d_gpu(at::Tensor rois, at::Tensor pts, at::Tensor pts_feature, at::Tensor argmax,
+    at::Tensor pts_idx_of_voxels, at::Tensor pooled_features, int pool_method){
+    // params rois: (N, 7) [x, y, z, dx, dy, dz, heading] (x, y, z) is the box center
+    // params pts: (npoints, 3) [x, y, z]
+    // params pts_feature: (npoints, C)
+    // params argmax: (N, out_x, out_y, out_z, C)
+    // params pts_idx_of_voxels: (N, out_x, out_y, out_z, max_pts_each_voxel)
+    // params pooled_features: (N, out_x, out_y, out_z, C)
+    // params pool_method: 0: max_pool 1: avg_pool
+
+//    CHECK_INPUT(rois);
+//    CHECK_INPUT(pts);
+//    CHECK_INPUT(pts_feature);
+//    CHECK_INPUT(argmax);
+//    CHECK_INPUT(pts_idx_of_voxels);
+//    CHECK_INPUT(pooled_features);
+
+    int boxes_num = rois.size(0);
+    int pts_num = pts.size(0);
+    int channels = pts_feature.size(1);
+    int max_pts_each_voxel = pts_idx_of_voxels.size(4);  // index 0 is the counter
+    int out_x = pts_idx_of_voxels.size(1);
+    int out_y = pts_idx_of_voxels.size(2);
+    int out_z = pts_idx_of_voxels.size(3);
+    assert ((out_x < 256) && (out_y < 256) && (out_z < 256));  // we encode index with 8bit
+
+    const float *rois_data = rois.data<float>();
+    const float *pts_data = pts.data<float>();
+    const float *pts_feature_data = pts_feature.data<float>();
+    int *argmax_data = argmax.data<int>();
+    int *pts_idx_of_voxels_data = pts_idx_of_voxels.data<int>();
+    float *pooled_features_data = pooled_features.data<float>();
+
+    roiaware_pool3d_launcher(boxes_num, pts_num, channels, max_pts_each_voxel, out_x, out_y, out_z,
+        rois_data, pts_data, pts_feature_data, argmax_data, pts_idx_of_voxels_data, pooled_features_data, pool_method);
+
+    return 1;
+}
+
+int roiaware_pool3d_gpu_backward(at::Tensor pts_idx_of_voxels, at::Tensor argmax, at::Tensor grad_out, at::Tensor grad_in, int pool_method){
+    // params pts_idx_of_voxels: (N, out_x, out_y, out_z, max_pts_each_voxel)
+    // params argmax: (N, out_x, out_y, out_z, C)
+    // params grad_out: (N, out_x, out_y, out_z, C)
+    // params grad_in: (npoints, C), return value
+    // params pool_method: 0: max_pool 1: avg_pool
+
+//    CHECK_INPUT(pts_idx_of_voxels);
+//    CHECK_INPUT(argmax);
+//    CHECK_INPUT(grad_out);
+//    CHECK_INPUT(grad_in);
+
+    int boxes_num = pts_idx_of_voxels.size(0);
+    int out_x = pts_idx_of_voxels.size(1);
+    int out_y = pts_idx_of_voxels.size(2);
+    int out_z = pts_idx_of_voxels.size(3);
+    int max_pts_each_voxel = pts_idx_of_voxels.size(4);  // index 0 is the counter
+    int channels = grad_out.size(4);
+
+    const int *pts_idx_of_voxels_data = pts_idx_of_voxels.data<int>();
+    const int *argmax_data = argmax.data<int>();
+    const float *grad_out_data = grad_out.data<float>();
+    float *grad_in_data = grad_in.data<float>();
+
+    roiaware_pool3d_backward_launcher(boxes_num, out_x, out_y, out_z, channels, max_pts_each_voxel,
+        pts_idx_of_voxels_data, argmax_data, grad_out_data, grad_in_data, pool_method);
+
+    return 1;
+}
+
+int points_in_boxes_gpu(at::Tensor boxes_tensor, at::Tensor pts_tensor, at::Tensor box_idx_of_points_tensor){
+    // params boxes: (B, N, 7) [x, y, z, dx, dy, dz, heading] (x, y, z) is the box center
+    // params pts: (B, npoints, 3) [x, y, z]
+    // params boxes_idx_of_points: (B, npoints), default -1
+
+//    CHECK_INPUT(boxes_tensor);
+//    CHECK_INPUT(pts_tensor);
+//    CHECK_INPUT(box_idx_of_points_tensor);
+
+    int batch_size = boxes_tensor.size(0);
+    int boxes_num = boxes_tensor.size(1);
+    int pts_num = pts_tensor.size(1);
+
+    const float *boxes = boxes_tensor.data<float>();
+    const float *pts = pts_tensor.data<float>();
+    int *box_idx_of_points = box_idx_of_points_tensor.data<int>();
+
+    points_in_boxes_launcher(batch_size, boxes_num, pts_num, boxes, pts, box_idx_of_points);
+
+    return 1;
+}
+
+
+inline void lidar_to_local_coords_cpu(float shift_x, float shift_y, float rot_angle, float &local_x, float &local_y){
+    float cosa = cos(-rot_angle), sina = sin(-rot_angle);
+    local_x = shift_x * cosa + shift_y * (-sina);
+    local_y = shift_x * sina + shift_y * cosa;
+}
+
+
+inline int check_pt_in_box3d_cpu(const float *pt, const float *box3d, float &local_x, float &local_y){
+    // param pt: (x, y, z)
+    // param box3d: [x, y, z, dx, dy, dz, heading], (x, y, z) is the box center
+    const float MARGIN = 1e-2;
+    float x = pt[0], y = pt[1], z = pt[2];
+    float cx = box3d[0], cy = box3d[1], cz = box3d[2];
+    float dx = box3d[3], dy = box3d[4], dz = box3d[5], rz = box3d[6];
+
+    if (fabsf(z - cz) > dz / 2.0) return 0;
+    lidar_to_local_coords_cpu(x - cx, y - cy, rz, local_x, local_y);
+    float in_flag = (fabs(local_x) < dx / 2.0 + MARGIN) & (fabs(local_y) < dy / 2.0 + MARGIN);
+    return in_flag;
+}
+
+
+int points_in_boxes_cpu(at::Tensor boxes_tensor, at::Tensor pts_tensor, at::Tensor pts_indices_tensor){
+    // params boxes: (N, 7) [x, y, z, dx, dy, dz, heading], (x, y, z) is the box center, each box DO NOT overlaps
+    // params pts: (num_points, 3) [x, y, z]
+    // params pts_indices: (N, num_points)
+
+//    CHECK_CONTIGUOUS(boxes_tensor);
+//    CHECK_CONTIGUOUS(pts_tensor);
+//    CHECK_CONTIGUOUS(pts_indices_tensor);
+
+    int boxes_num = boxes_tensor.size(0);
+    int pts_num = pts_tensor.size(0);
+
+    const float *boxes = boxes_tensor.data<float>();
+    const float *pts = pts_tensor.data<float>();
+    int *pts_indices = pts_indices_tensor.data<int>();
+
+    float local_x = 0, local_y = 0;
+    for (int i = 0; i < boxes_num; i++){
+        for (int j = 0; j < pts_num; j++){
+            int cur_in_flag = check_pt_in_box3d_cpu(pts + j * 3, boxes + i * 7, local_x, local_y);
+            pts_indices[i * pts_num + j] = cur_in_flag;
+        }
+    }
+
+    return 1;
+}
+
+
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+    m.def("forward", &roiaware_pool3d_gpu, "roiaware pool3d forward (CUDA)");
+    m.def("backward", &roiaware_pool3d_gpu_backward, "roiaware pool3d backward (CUDA)");
+    m.def("points_in_boxes_gpu", &points_in_boxes_gpu, "points_in_boxes_gpu forward (CUDA)");
+    m.def("points_in_boxes_cpu", &points_in_boxes_cpu, "points_in_boxes_cpu forward (CUDA)");
+}
diff --git a/opencood/opencood/pcdet_utils/roiaware_pool3d/src/roiaware_pool3d_kernel.cu b/opencood/opencood/pcdet_utils/roiaware_pool3d/src/roiaware_pool3d_kernel.cu
new file mode 100755
index 00000000..5b52937f
--- /dev/null
+++ b/opencood/opencood/pcdet_utils/roiaware_pool3d/src/roiaware_pool3d_kernel.cu
@@ -0,0 +1,359 @@
+/*
+RoI-aware point cloud feature pooling
+Written by Shaoshuai Shi
+All Rights Reserved 2019-2020.
+*/
+
+
+#include <math.h>
+#include <stdio.h>
+
+#define THREADS_PER_BLOCK 256
+#define DIVUP(m,n) ((m) / (n) + ((m) % (n) > 0))
+// #define DEBUG
+
+
+__device__ inline void lidar_to_local_coords(float shift_x, float shift_y, float rot_angle, float &local_x, float &local_y){
+    float cosa = cos(-rot_angle), sina = sin(-rot_angle);
+    local_x = shift_x * cosa + shift_y * (-sina);
+    local_y = shift_x * sina + shift_y * cosa;
+}
+
+
+__device__ inline int check_pt_in_box3d(const float *pt, const float *box3d, float &local_x, float &local_y){
+    // param pt: (x, y, z)
+    // param box3d: [x, y, z, dx, dy, dz, heading] (x, y, z) is the box center
+
+    const float MARGIN = 1e-5;
+    float x = pt[0], y = pt[1], z = pt[2];
+    float cx = box3d[0], cy = box3d[1], cz = box3d[2];
+    float dx = box3d[3], dy = box3d[4], dz = box3d[5], rz = box3d[6];
+
+    if (fabsf(z - cz) > dz / 2.0) return 0;
+    lidar_to_local_coords(x - cx, y - cy, rz, local_x, local_y);
+    float in_flag = (fabs(local_x) < dx / 2.0 + MARGIN) & (fabs(local_y) < dy / 2.0 + MARGIN);
+    return in_flag;
+}
+
+
+__global__ void generate_pts_mask_for_box3d(int boxes_num, int pts_num, int out_x, int out_y, int out_z,
+    const float *rois, const float *pts, int *pts_mask){
+    // params rois: [x, y, z, dx, dy, dz, heading] (x, y, z) is the box center
+    // params pts: (npoints, 3) [x, y, z]
+    // params pts_mask: (N, npoints): -1 means point doesnot in this box, otherwise: encode (x_idxs, y_idxs, z_idxs) by binary bit
+    int pt_idx = blockIdx.x * blockDim.x + threadIdx.x;
+    int box_idx = blockIdx.y;
+    if (pt_idx >= pts_num || box_idx >= boxes_num) return;
+
+    pts += pt_idx * 3;
+    rois += box_idx * 7;
+    pts_mask += box_idx * pts_num + pt_idx;
+
+    float local_x = 0, local_y = 0;
+    int cur_in_flag = check_pt_in_box3d(pts, rois, local_x, local_y);
+
+    pts_mask[0] = -1;
+    if (cur_in_flag > 0){
+        float local_z = pts[2] - rois[2];
+        float dx = rois[3], dy = rois[4], dz = rois[5];
+
+        float x_res = dx / out_x;
+        float y_res = dy / out_y;
+        float z_res = dz / out_z;
+
+        unsigned int x_idx = int((local_x + dx / 2) / x_res);
+        unsigned int y_idx = int((local_y + dy / 2) / y_res);
+        unsigned int z_idx = int((local_z + dz / 2) / z_res);
+
+        x_idx = min(max(x_idx, 0), out_x - 1);
+        y_idx = min(max(y_idx, 0), out_y - 1);
+        z_idx = min(max(z_idx, 0), out_z - 1);
+
+        unsigned int idx_encoding = (x_idx << 16) + (y_idx << 8) + z_idx;
+        pts_mask[0] = idx_encoding;
+    }
+}
+
+
+__global__ void collect_inside_pts_for_box3d(int boxes_num, int pts_num, int max_pts_each_voxel,
+    int out_x, int out_y, int out_z, const int *pts_mask, int *pts_idx_of_voxels){
+    // params pts_mask: (N, npoints)  0 or 1
+    // params pts_idx_of_voxels: (N, out_x, out_y, out_z, max_pts_each_voxel)
+
+    int box_idx = blockIdx.x * blockDim.x + threadIdx.x;
+    if (box_idx >= boxes_num) return;
+
+    int max_num_pts = max_pts_each_voxel - 1;  // index 0 is the counter
+    pts_idx_of_voxels += box_idx * out_x * out_y * out_z * max_pts_each_voxel;
+
+    for (int k = 0; k < pts_num; k++){
+        if (pts_mask[box_idx * pts_num + k] != -1){
+            unsigned int idx_encoding = pts_mask[box_idx * pts_num + k];
+            unsigned int x_idx = (idx_encoding >> 16) & 0xFF;
+            unsigned int y_idx = (idx_encoding >> 8) & 0xFF;
+            unsigned int z_idx = idx_encoding & 0xFF;
+            unsigned int base_offset = x_idx * out_y * out_z * max_pts_each_voxel + y_idx * out_z * max_pts_each_voxel + z_idx * max_pts_each_voxel;
+            unsigned int cnt = pts_idx_of_voxels[base_offset];
+            if (cnt < max_num_pts){
+                pts_idx_of_voxels[base_offset + cnt + 1] = k;
+                pts_idx_of_voxels[base_offset]++;
+            }
+#ifdef DEBUG
+        printf("collect: pts_%d, idx(%d, %d, %d), idx_encoding=%x\n",
+            k, x_idx, y_idx, z_idx, idx_encoding);
+#endif
+
+        }
+    }
+}
+
+
+__global__ void roiaware_maxpool3d(int boxes_num, int pts_num, int channels, int max_pts_each_voxel, int out_x,
+    int out_y, int out_z, const float *pts_feature, const int *pts_idx_of_voxels, float *pooled_features, int *argmax){
+    // params pts_feature: (npoints, C)
+    // params pts_idx_of_voxels: (N, out_x, out_y, out_z, max_pts_each_voxel), index 0 is the counter
+    // params pooled_features: (N, out_x, out_y, out_z, C)
+    // params argmax: (N, out_x, out_y, out_z, C)
+
+    int box_idx = blockIdx.z;
+    int channel_idx = blockIdx.y;
+    int voxel_idx_flat = blockIdx.x * blockDim.x + threadIdx.x;
+
+    int x_idx = voxel_idx_flat / (out_y * out_z);
+    int y_idx = (voxel_idx_flat - x_idx * (out_y * out_z)) / out_z;
+    int z_idx = voxel_idx_flat % out_z;
+    if (box_idx >= boxes_num || channel_idx >= channels|| x_idx >= out_x || y_idx >= out_y || z_idx >= out_z) return;
+
+#ifdef DEBUG
+    printf("src pts_idx_of_voxels: (%p, ), argmax: %p\n", pts_idx_of_voxels, argmax);
+#endif
+
+    int offset_base = x_idx * out_y * out_z + y_idx * out_z + z_idx;
+    pts_idx_of_voxels += box_idx * out_x * out_y * out_z * max_pts_each_voxel + offset_base * max_pts_each_voxel;
+    pooled_features += box_idx * out_x * out_y * out_z * channels + offset_base * channels + channel_idx;
+    argmax += box_idx * out_x * out_y * out_z * channels + offset_base * channels + channel_idx;
+
+    int argmax_idx = -1;
+    float max_val = -1e50;
+
+    int total_pts = pts_idx_of_voxels[0];
+
+    for (int k = 1; k <= total_pts; k++){
+        if (pts_feature[pts_idx_of_voxels[k] * channels + channel_idx] > max_val){
+            max_val = pts_feature[pts_idx_of_voxels[k] * channels + channel_idx];
+            argmax_idx = pts_idx_of_voxels[k];
+        }
+    }
+
+    if (argmax_idx != -1){
+        pooled_features[0] = max_val;
+    }
+    argmax[0] = argmax_idx;
+
+#ifdef DEBUG
+    printf("channel_%d idx(%d, %d, %d), argmax_idx=(%d, %.3f), total=%d, after pts_idx: %p, argmax: (%p, %d)\n",
+        channel_idx, x_idx, y_idx, z_idx, argmax_idx, max_val, total_pts, pts_idx_of_voxels, argmax, argmax_idx);
+#endif
+}
+
+
+__global__ void roiaware_avgpool3d(int boxes_num, int pts_num, int channels, int max_pts_each_voxel, int out_x,
+    int out_y, int out_z, const float *pts_feature, const int *pts_idx_of_voxels, float *pooled_features){
+    // params pts_feature: (npoints, C)
+    // params pts_idx_of_voxels: (N, out_x, out_y, out_z, max_pts_each_voxel), index 0 is the counter
+    // params pooled_features: (N, out_x, out_y, out_z, C)
+    // params argmax: (N, out_x, out_y, out_z, C)
+
+    int box_idx = blockIdx.z;
+    int channel_idx = blockIdx.y;
+    int voxel_idx_flat = blockIdx.x * blockDim.x + threadIdx.x;
+
+    int x_idx = voxel_idx_flat / (out_y * out_z);
+    int y_idx = (voxel_idx_flat - x_idx * (out_y * out_z)) / out_z;
+    int z_idx = voxel_idx_flat % out_z;
+    if (box_idx >= boxes_num || channel_idx >= channels|| x_idx >= out_x || y_idx >= out_y || z_idx >= out_z) return;
+
+    int offset_base = x_idx * out_y * out_z + y_idx * out_z + z_idx;
+    pts_idx_of_voxels += box_idx * out_x * out_y * out_z * max_pts_each_voxel + offset_base * max_pts_each_voxel;
+    pooled_features += box_idx * out_x * out_y * out_z * channels + offset_base * channels + channel_idx;
+
+    float sum_val = 0;
+    int total_pts = pts_idx_of_voxels[0];
+
+    for (int k = 1; k <= total_pts; k++){
+        sum_val += pts_feature[pts_idx_of_voxels[k] * channels + channel_idx];
+    }
+
+    if (total_pts > 0){
+        pooled_features[0] = sum_val / total_pts;
+    }
+}
+
+
+void roiaware_pool3d_launcher(int boxes_num, int pts_num, int channels, int max_pts_each_voxel, int out_x, int out_y, int out_z,
+    const float *rois, const float *pts, const float *pts_feature, int *argmax, int *pts_idx_of_voxels, float *pooled_features, int pool_method){
+    // params rois: (N, 7) [x, y, z, dx, dy, dz, heading] (x, y, z) is the box center
+    // params pts: (npoints, 3) [x, y, z]
+    // params pts_feature: (npoints, C)
+    // params argmax: (N, out_x, out_y, out_z, C)
+    // params pts_idx_of_voxels: (N, out_x, out_y, out_z, max_pts_each_voxel)
+    // params pooled_features: (N, out_x, out_y, out_z, C)
+    // params pool_method: 0: max_pool 1: avg_pool
+
+    int *pts_mask = NULL;
+    cudaMalloc(&pts_mask, boxes_num * pts_num * sizeof(int));  // (N, M)
+    cudaMemset(pts_mask, -1, boxes_num * pts_num * sizeof(int));
+
+    dim3 blocks_mask(DIVUP(pts_num, THREADS_PER_BLOCK), boxes_num);
+    dim3 threads(THREADS_PER_BLOCK);
+    generate_pts_mask_for_box3d<<<blocks_mask, threads>>>(boxes_num, pts_num, out_x, out_y, out_z, rois, pts, pts_mask);
+
+    // TODO: Merge the collect and pool functions, SS
+
+    dim3 blocks_collect(DIVUP(boxes_num, THREADS_PER_BLOCK));
+    collect_inside_pts_for_box3d<<<blocks_collect, threads>>>(boxes_num, pts_num, max_pts_each_voxel,
+        out_x, out_y, out_z, pts_mask, pts_idx_of_voxels);
+
+    dim3 blocks_pool(DIVUP(out_x * out_y * out_z, THREADS_PER_BLOCK), channels, boxes_num);
+    if (pool_method == 0){
+        roiaware_maxpool3d<<<blocks_pool, threads>>>(boxes_num, pts_num, channels, max_pts_each_voxel, out_x, out_y, out_z,
+            pts_feature, pts_idx_of_voxels, pooled_features, argmax);
+    }
+    else if (pool_method == 1){
+        roiaware_avgpool3d<<<blocks_pool, threads>>>(boxes_num, pts_num, channels, max_pts_each_voxel, out_x, out_y, out_z,
+            pts_feature, pts_idx_of_voxels, pooled_features);
+    }
+
+
+    cudaFree(pts_mask);
+
+#ifdef DEBUG
+    cudaDeviceSynchronize();  // for using printf in kernel function
+#endif
+}
+
+
+__global__ void roiaware_maxpool3d_backward(int boxes_num, int channels, int out_x, int out_y, int out_z,
+    const int *argmax, const float *grad_out, float *grad_in){
+    // params argmax: (N, out_x, out_y, out_z, C)
+    // params grad_out: (N, out_x, out_y, out_z, C)
+    // params grad_in: (npoints, C), return value
+
+    int box_idx = blockIdx.z;
+    int channel_idx = blockIdx.y;
+    int voxel_idx_flat = blockIdx.x * blockDim.x + threadIdx.x;
+
+    int x_idx = voxel_idx_flat / (out_y * out_z);
+    int y_idx = (voxel_idx_flat - x_idx * (out_y * out_z)) / out_z;
+    int z_idx = voxel_idx_flat % out_z;
+    if (box_idx >= boxes_num || channel_idx >= channels|| x_idx >= out_x || y_idx >= out_y || z_idx >= out_z) return;
+
+    int offset_base = x_idx * out_y * out_z + y_idx * out_z + z_idx;
+    argmax += box_idx * out_x * out_y * out_z * channels + offset_base * channels + channel_idx;
+    grad_out += box_idx * out_x * out_y * out_z * channels + offset_base * channels + channel_idx;
+
+    if (argmax[0] == -1) return;
+
+    atomicAdd(grad_in + argmax[0] * channels + channel_idx, grad_out[0] * 1);
+}
+
+
+__global__ void roiaware_avgpool3d_backward(int boxes_num, int channels, int out_x, int out_y, int out_z,
+    int max_pts_each_voxel, const int *pts_idx_of_voxels, const float *grad_out, float *grad_in){
+    // params pts_idx_of_voxels: (N, out_x, out_y, out_z, max_pts_each_voxel)
+    // params grad_out: (N, out_x, out_y, out_z, C)
+    // params grad_in: (npoints, C), return value
+
+    int box_idx = blockIdx.z;
+    int channel_idx = blockIdx.y;
+    int voxel_idx_flat = blockIdx.x * blockDim.x + threadIdx.x;
+
+    int x_idx = voxel_idx_flat / (out_y * out_z);
+    int y_idx = (voxel_idx_flat - x_idx * (out_y * out_z)) / out_z;
+    int z_idx = voxel_idx_flat % out_z;
+    if (box_idx >= boxes_num || channel_idx >= channels|| x_idx >= out_x || y_idx >= out_y || z_idx >= out_z) return;
+
+    int offset_base = x_idx * out_y * out_z + y_idx * out_z + z_idx;
+    pts_idx_of_voxels += box_idx * out_x * out_y * out_z * max_pts_each_voxel + offset_base * max_pts_each_voxel;
+    grad_out += box_idx * out_x * out_y * out_z * channels + offset_base * channels + channel_idx;
+
+
+    int total_pts = pts_idx_of_voxels[0];
+    float cur_grad = 1 / fmaxf(float(total_pts), 1.0);
+    for (int k = 1; k <= total_pts; k++){
+        atomicAdd(grad_in + pts_idx_of_voxels[k] * channels + channel_idx, grad_out[0] * cur_grad);
+    }
+}
+
+
+void roiaware_pool3d_backward_launcher(int boxes_num, int out_x, int out_y, int out_z, int channels, int max_pts_each_voxel,
+    const int *pts_idx_of_voxels, const int *argmax, const float *grad_out, float *grad_in, int pool_method){
+    // params pts_idx_of_voxels: (N, out_x, out_y, out_z, max_pts_each_voxel)
+    // params argmax: (N, out_x, out_y, out_z, C)
+    // params grad_out: (N, out_x, out_y, out_z, C)
+    // params grad_in: (npoints, C), return value
+    // params pool_method: 0: max_pool, 1: avg_pool
+
+    dim3 blocks(DIVUP(out_x * out_y * out_z, THREADS_PER_BLOCK), channels, boxes_num);
+    dim3 threads(THREADS_PER_BLOCK);
+    if (pool_method == 0){
+        roiaware_maxpool3d_backward<<<blocks, threads>>>(
+            boxes_num, channels, out_x, out_y, out_z, argmax, grad_out, grad_in
+        );
+    }
+    else if (pool_method == 1){
+        roiaware_avgpool3d_backward<<<blocks, threads>>>(
+            boxes_num, channels, out_x, out_y, out_z, max_pts_each_voxel, pts_idx_of_voxels, grad_out, grad_in
+        );
+    }
+
+}
+
+
+__global__ void points_in_boxes_kernel(int batch_size, int boxes_num, int pts_num, const float *boxes,
+    const float *pts, int *box_idx_of_points){
+    // params boxes: (B, N, 7) [x, y, z, dx, dy, dz, heading] (x, y, z) is the box center
+    // params pts: (B, npoints, 3) [x, y, z] in LiDAR coordinate
+    // params boxes_idx_of_points: (B, npoints), default -1
+
+    int bs_idx = blockIdx.y;
+    int pt_idx = blockIdx.x * blockDim.x + threadIdx.x;
+    if (bs_idx >= batch_size || pt_idx >= pts_num) return;
+
+    boxes += bs_idx * boxes_num * 7;
+    pts += bs_idx * pts_num * 3 + pt_idx * 3;
+    box_idx_of_points += bs_idx * pts_num + pt_idx;
+
+    float local_x = 0, local_y = 0;
+    int cur_in_flag = 0;
+    for (int k = 0; k < boxes_num; k++){
+        cur_in_flag = check_pt_in_box3d(pts, boxes + k * 7, local_x, local_y);
+        if (cur_in_flag){
+            box_idx_of_points[0] = k;
+            break;
+        }
+    }
+}
+
+
+void points_in_boxes_launcher(int batch_size, int boxes_num, int pts_num, const float *boxes,
+    const float *pts, int *box_idx_of_points){
+    // params boxes: (B, N, 7) [x, y, z, dx, dy, dz, heading] (x, y, z) is the box center
+    // params pts: (B, npoints, 3) [x, y, z]
+    // params boxes_idx_of_points: (B, npoints), default -1
+    cudaError_t err;
+
+    dim3 blocks(DIVUP(pts_num, THREADS_PER_BLOCK), batch_size);
+    dim3 threads(THREADS_PER_BLOCK);
+    points_in_boxes_kernel<<<blocks, threads>>>(batch_size, boxes_num, pts_num, boxes, pts, box_idx_of_points);
+
+    err = cudaGetLastError();
+    if (cudaSuccess != err) {
+        fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
+        exit(-1);
+    }
+
+#ifdef DEBUG
+    cudaDeviceSynchronize();  // for using printf in kernel function
+#endif
+}
diff --git a/opencood/opencood/pcdet_utils/setup.py b/opencood/opencood/pcdet_utils/setup.py
new file mode 100644
index 00000000..41b29b84
--- /dev/null
+++ b/opencood/opencood/pcdet_utils/setup.py
@@ -0,0 +1,66 @@
+import os
+
+from setuptools import find_packages, setup
+from torch.utils.cpp_extension import BuildExtension, CUDAExtension
+
+
+def make_cuda_ext(name, module, sources):
+    cuda_ext = CUDAExtension(
+        name='%s.%s' % (module, name),
+        sources=[os.path.join(*module.split('.'), src) for src in sources]
+    )
+    return cuda_ext
+
+
+setup(
+    name='pcd utils',
+    cmdclass={'build_ext': BuildExtension},
+    ext_modules=[make_cuda_ext(
+                name='iou3d_nms_cuda',
+                module='opencood.pcdet_utils.iou3d_nms',
+                sources=[
+                    'src/iou3d_cpu.cpp',
+                    'src/iou3d_nms_api.cpp',
+                    'src/iou3d_nms.cpp',
+                    'src/iou3d_nms_kernel.cu'
+                ]),
+        make_cuda_ext(
+            name='roiaware_pool3d_cuda',
+            module='opencood.pcdet_utils.roiaware_pool3d',
+            sources=[
+                'src/roiaware_pool3d.cpp',
+                'src/roiaware_pool3d_kernel.cu',
+            ]
+        ),
+        make_cuda_ext(
+            name='pointnet2_stack_cuda',
+            module='opencood.pcdet_utils.pointnet2.pointnet2_stack',
+            sources=[
+                'src/pointnet2_api.cpp',
+                'src/ball_query.cpp',
+                'src/ball_query_gpu.cu',
+                'src/group_points.cpp',
+                'src/group_points_gpu.cu',
+                'src/sampling.cpp',
+                'src/sampling_gpu.cu',
+                'src/interpolate.cpp',
+                'src/interpolate_gpu.cu',
+            ],
+        ),
+        make_cuda_ext(
+            name='pointnet2_batch_cuda',
+            module='opencood.pcdet_utils.pointnet2.pointnet2_batch',
+            sources=[
+                'src/pointnet2_api.cpp',
+                'src/ball_query.cpp',
+                'src/ball_query_gpu.cu',
+                'src/group_points.cpp',
+                'src/group_points_gpu.cu',
+                'src/interpolate.cpp',
+                'src/interpolate_gpu.cu',
+                'src/sampling.cpp',
+                'src/sampling_gpu.cu',
+            ],
+        )]
+
+)
\ No newline at end of file
diff --git a/opencood/opencood/tools/__init__.py b/opencood/opencood/tools/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/opencood/opencood/tools/debug_utils.py b/opencood/opencood/tools/debug_utils.py
new file mode 100644
index 00000000..ee51cf3a
--- /dev/null
+++ b/opencood/opencood/tools/debug_utils.py
@@ -0,0 +1,71 @@
+# -*- coding: utf-8 -*-
+# Author: Runsheng Xu <rxx3386@ucla.edu>, Hao Xiang <haxiang@g.ucla.edu>,
+# License: TDG-Attribution-NonCommercial-NoDistrib
+
+
+import argparse
+
+import torch
+from torch.utils.data import DataLoader
+
+import opencood.hypes_yaml.yaml_utils as yaml_utils
+from opencood.tools import train_utils
+from opencood.data_utils.datasets import build_dataset
+from opencood.visualization import vis_utils
+
+
+def test_parser():
+    parser = argparse.ArgumentParser(description="synthetic data generation")
+    parser.add_argument('--model_dir', type=str, required=True,
+                        help='Continued training path')
+    parser.add_argument('--fusion_method', type=str, default='late',
+                        help='late, early or intermediate')
+    opt = parser.parse_args()
+    return opt
+
+
+def test_bev_post_processing():
+    opt = test_parser()
+    assert opt.fusion_method in ['late', 'early', 'intermediate']
+
+    hypes = yaml_utils.load_yaml(None, opt)
+
+    print('Dataset Building')
+    opencood_dataset = build_dataset(hypes, visualize=True, train=False)
+    data_loader = DataLoader(opencood_dataset,
+                             batch_size=1,
+                             num_workers=0,
+                             collate_fn=opencood_dataset.collate_batch_test,
+                             shuffle=False,
+                             pin_memory=False,
+                             drop_last=False)
+
+    print('Creating Model')
+    model = train_utils.create_model(hypes)
+    # we assume gpu is necessary
+    if torch.cuda.is_available():
+        model.cuda()
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+    print('Loading Model from checkpoint')
+    saved_path = opt.model_dir
+    _, model = train_utils.load_saved_model(saved_path, model)
+    model.eval()
+    for i, batch_data in enumerate(data_loader):
+        batch_data = train_utils.to_device(batch_data, device)
+        label_map = batch_data["ego"]["label_dict"]["label_map"]
+        output_dict = {
+            "cls": label_map[:, 0, :, :],
+            "reg": label_map[:, 1:, :, :]
+        }
+        gt_box_tensor, _ = opencood_dataset.post_processor.post_process_debug(
+            batch_data["ego"], output_dict)
+        vis_utils.visualize_single_sample_output_bev(gt_box_tensor,
+                                                     batch_data['ego'][
+                                                         'origin_lidar'].squeeze(
+                                                         0),
+                                                     opencood_dataset)
+
+
+if __name__ == '__main__':
+    test_bev_post_processing()
diff --git a/opencood/opencood/tools/inference.py b/opencood/opencood/tools/inference.py
new file mode 100644
index 00000000..244951bd
--- /dev/null
+++ b/opencood/opencood/tools/inference.py
@@ -0,0 +1,202 @@
+# -*- coding: utf-8 -*-
+# Author: Runsheng Xu <rxx3386@ucla.edu>, Hao Xiang <haxiang@g.ucla.edu>,
+# License: TDG-Attribution-NonCommercial-NoDistrib
+
+
+import argparse
+import os
+import time
+from tqdm import tqdm
+
+import torch
+import open3d as o3d
+from torch.utils.data import DataLoader
+
+import opencood.hypes_yaml.yaml_utils as yaml_utils
+from opencood.tools import train_utils, inference_utils
+from opencood.data_utils.datasets import build_dataset
+from opencood.utils import eval_utils
+from opencood.visualization import vis_utils
+import matplotlib.pyplot as plt
+
+
+def test_parser():
+    parser = argparse.ArgumentParser(description="synthetic data generation")
+    parser.add_argument('--model_dir', type=str, required=True,
+                        help='Continued training path')
+    parser.add_argument('--fusion_method', required=True, type=str,
+                        default='late',
+                        help='late, early or intermediate')
+    parser.add_argument('--show_vis', action='store_true',
+                        help='whether to show image visualization result')
+    parser.add_argument('--show_sequence', action='store_true',
+                        help='whether to show video visualization result.'
+                             'it can note be set true with show_vis together ')
+    parser.add_argument('--save_vis', action='store_true',
+                        help='whether to save visualization result')
+    parser.add_argument('--save_npy', action='store_true',
+                        help='whether to save prediction and gt result'
+                             'in npy_test file')
+    opt = parser.parse_args()
+    return opt
+
+
+def main():
+    opt = test_parser()
+    assert opt.fusion_method in ['late', 'early', 'intermediate']
+    assert not (opt.show_vis and opt.show_sequence), 'you can only visualize ' \
+                                                    'the results in single ' \
+                                                    'image mode or video mode'
+
+    hypes = yaml_utils.load_yaml(None, opt)
+
+    print('Dataset Building')
+    opencood_dataset = build_dataset(hypes, visualize=True, train=False)
+    print(f"{len(opencood_dataset)} samples found.")
+    data_loader = DataLoader(opencood_dataset,
+                             batch_size=1,
+                             num_workers=4,
+                             collate_fn=opencood_dataset.collate_batch_test,
+                             shuffle=False,
+                             pin_memory=False,
+                             drop_last=False)
+
+    print('Creating Model')
+    model = train_utils.create_model(hypes)
+    # we assume gpu is necessary
+    if torch.cuda.is_available():
+        model.cuda()
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+    print('Loading Model from checkpoint')
+    saved_path = opt.model_dir
+    _, model = train_utils.load_saved_model(saved_path, model)
+    model.eval()
+
+    # Create the dictionary for evaluation
+    result_stat = {0.3: {'tp': [], 'fp': [], 'gt': 0},
+                   0.5: {'tp': [], 'fp': [], 'gt': 0},
+                   0.7: {'tp': [], 'fp': [], 'gt': 0}}
+
+    if opt.show_sequence:
+        vis = o3d.visualization.Visualizer()
+        vis.create_window()
+
+        vis.get_render_option().background_color = [0.05, 0.05, 0.05]
+        vis.get_render_option().point_size = 1.0
+        vis.get_render_option().show_coordinate_frame = True
+
+        # used to visualize lidar points
+        vis_pcd = o3d.geometry.PointCloud()
+        # used to visualize object bounding box, maximum 50
+        vis_aabbs_gt = []
+        vis_aabbs_pred = []
+        for _ in range(50):
+            vis_aabbs_gt.append(o3d.geometry.LineSet())
+            vis_aabbs_pred.append(o3d.geometry.LineSet())
+
+    for i, batch_data in tqdm(enumerate(data_loader)):
+        # print(i)
+        with torch.no_grad():
+            batch_data = train_utils.to_device(batch_data, device)
+            if opt.fusion_method == 'late':
+                pred_box_tensor, pred_score, gt_box_tensor = \
+                    inference_utils.inference_late_fusion(batch_data,
+                                                          model,
+                                                          opencood_dataset)
+            elif opt.fusion_method == 'early':
+                pred_box_tensor, pred_score, gt_box_tensor = \
+                    inference_utils.inference_early_fusion(batch_data,
+                                                           model,
+                                                           opencood_dataset)
+            elif opt.fusion_method == 'intermediate':
+                pred_box_tensor, pred_score, gt_box_tensor = \
+                    inference_utils.inference_intermediate_fusion(batch_data,
+                                                                  model,
+                                                                  opencood_dataset)
+            else:
+                raise NotImplementedError('Only early, late and intermediate'
+                                          'fusion is supported.')
+
+            eval_utils.caluclate_tp_fp(pred_box_tensor,
+                                       pred_score,
+                                       gt_box_tensor,
+                                       result_stat,
+                                       0.3)
+            eval_utils.caluclate_tp_fp(pred_box_tensor,
+                                       pred_score,
+                                       gt_box_tensor,
+                                       result_stat,
+                                       0.5)
+            eval_utils.caluclate_tp_fp(pred_box_tensor,
+                                       pred_score,
+                                       gt_box_tensor,
+                                       result_stat,
+                                       0.7)
+            if opt.save_npy:
+                npy_save_path = os.path.join(opt.model_dir, 'npy')
+                if not os.path.exists(npy_save_path):
+                    os.makedirs(npy_save_path)
+                inference_utils.save_prediction_gt(pred_box_tensor,
+                                                   gt_box_tensor,
+                                                   batch_data['ego'][
+                                                       'origin_lidar'][0],
+                                                   i,
+                                                   npy_save_path)
+
+            if opt.show_vis or opt.save_vis:
+                vis_save_path = ''
+                if opt.save_vis:
+                    vis_save_path = os.path.join(opt.model_dir, 'vis')
+                    if not os.path.exists(vis_save_path):
+                        os.makedirs(vis_save_path)
+                    vis_save_path = os.path.join(vis_save_path, '%05d.png' % i)
+
+                opencood_dataset.visualize_result(pred_box_tensor,
+                                                  gt_box_tensor,
+                                                  batch_data['ego'][
+                                                      'origin_lidar'],
+                                                  opt.show_vis,
+                                                  vis_save_path,
+                                                  dataset=opencood_dataset)
+
+            if opt.show_sequence:
+                pcd, pred_o3d_box, gt_o3d_box = \
+                    vis_utils.visualize_inference_sample_dataloader(
+                        pred_box_tensor,
+                        gt_box_tensor,
+                        batch_data['ego']['origin_lidar'],
+                        vis_pcd,
+                        mode='constant'
+                        )
+                if i == 0:
+                    vis.add_geometry(pcd)
+                    vis_utils.linset_assign_list(vis,
+                                                 vis_aabbs_pred,
+                                                 pred_o3d_box,
+                                                 update_mode='add')
+
+                    vis_utils.linset_assign_list(vis,
+                                                 vis_aabbs_gt,
+                                                 gt_o3d_box,
+                                                 update_mode='add')
+
+                vis_utils.linset_assign_list(vis,
+                                             vis_aabbs_pred,
+                                             pred_o3d_box)
+                vis_utils.linset_assign_list(vis,
+                                             vis_aabbs_gt,
+                                             gt_o3d_box)
+                vis.update_geometry(pcd)
+                vis.poll_events()
+                vis.update_renderer()
+                time.sleep(0.001)
+
+    eval_utils.eval_final_results(result_stat,
+                                  opt.model_dir)
+    if opt.show_sequence:
+        vis.destroy_window()
+
+
+if __name__ == '__main__':
+    main()
diff --git a/opencood/opencood/tools/inference_utils.py b/opencood/opencood/tools/inference_utils.py
new file mode 100644
index 00000000..f9375927
--- /dev/null
+++ b/opencood/opencood/tools/inference_utils.py
@@ -0,0 +1,103 @@
+# -*- coding: utf-8 -*-
+# Author: Runsheng Xu <rxx3386@ucla.edu>
+# License: TDG-Attribution-NonCommercial-NoDistrib
+
+
+import os
+from collections import OrderedDict
+
+import numpy as np
+import torch
+
+from opencood.utils.common_utils import torch_tensor_to_numpy
+
+
+def inference_late_fusion(batch_data, model, dataset):
+    """
+    Model inference for late fusion.
+
+    Parameters
+    ----------
+    batch_data : dict
+    model : opencood.object
+    dataset : opencood.LateFusionDataset
+
+    Returns
+    -------
+    pred_box_tensor : torch.Tensor
+        The tensor of prediction bounding box after NMS.
+    gt_box_tensor : torch.Tensor
+        The tensor of gt bounding box.
+    """
+    output_dict = OrderedDict()
+
+    for cav_id, cav_content in batch_data.items():
+        output_dict[cav_id] = model(cav_content)
+
+    pred_box_tensor, pred_score, gt_box_tensor = \
+        dataset.post_process(batch_data,
+                             output_dict)
+
+    return pred_box_tensor, pred_score, gt_box_tensor
+
+
+def inference_early_fusion(batch_data, model, dataset):
+    """
+    Model inference for early fusion.
+
+    Parameters
+    ----------
+    batch_data : dict
+    model : opencood.object
+    dataset : opencood.EarlyFusionDataset
+
+    Returns
+    -------
+    pred_box_tensor : torch.Tensor
+        The tensor of prediction bounding box after NMS.
+    gt_box_tensor : torch.Tensor
+        The tensor of gt bounding box.
+    """
+    output_dict = OrderedDict()
+    cav_content = batch_data['ego']
+
+    output_dict['ego'] = model(cav_content)
+
+    pred_box_tensor, pred_score, gt_box_tensor = \
+        dataset.post_process(batch_data,
+                             output_dict)
+
+    return pred_box_tensor, pred_score, gt_box_tensor
+
+
+def inference_intermediate_fusion(batch_data, model, dataset):
+    """
+    Model inference for early fusion.
+
+    Parameters
+    ----------
+    batch_data : dict
+    model : opencood.object
+    dataset : opencood.EarlyFusionDataset
+
+    Returns
+    -------
+    pred_box_tensor : torch.Tensor
+        The tensor of prediction bounding box after NMS.
+    gt_box_tensor : torch.Tensor
+        The tensor of gt bounding box.
+    """
+    return inference_early_fusion(batch_data, model, dataset)
+
+
+def save_prediction_gt(pred_tensor, gt_tensor, pcd, timestamp, save_path):
+    """
+    Save prediction and gt tensor to txt file.
+    """
+    pred_np = torch_tensor_to_numpy(pred_tensor)
+    gt_np = torch_tensor_to_numpy(gt_tensor)
+    pcd_np = torch_tensor_to_numpy(pcd)
+
+    np.save(os.path.join(save_path, '%04d_pcd.npy' % timestamp), pcd_np)
+    np.save(os.path.join(save_path, '%04d_pred.npy' % timestamp), pred_np)
+    np.save(os.path.join(save_path, '%04d_gt.npy_test' % timestamp), gt_np)
diff --git a/opencood/opencood/tools/multi_gpu_utils.py b/opencood/opencood/tools/multi_gpu_utils.py
new file mode 100644
index 00000000..51c69725
--- /dev/null
+++ b/opencood/opencood/tools/multi_gpu_utils.py
@@ -0,0 +1,54 @@
+import os
+import torch
+import torch.distributed as dist
+
+
+def get_dist_info():
+    if dist.is_available() and dist.is_initialized():
+        rank = dist.get_rank()
+        world_size = dist.get_world_size()
+    else:
+        rank = 0
+        world_size = 1
+    return rank, world_size
+
+
+def init_distributed_mode(args):
+    if 'RANK' in os.environ and 'WORLD_SIZE' in os.environ:
+        args.rank = int(os.environ["RANK"])
+        args.world_size = int(os.environ['WORLD_SIZE'])
+        args.gpu = int(os.environ['LOCAL_RANK'])
+    elif 'SLURM_PROCID' in os.environ:
+        args.rank = int(os.environ['SLURM_PROCID'])
+        args.gpu = args.rank % torch.cuda.device_count()
+    else:
+        print('Not using distributed mode')
+        args.distributed = False
+        return
+
+    args.distributed = True
+
+    torch.cuda.set_device(args.gpu)
+    args.dist_backend = 'nccl'
+    print('| distributed init (rank {}): {}'.format(
+        args.rank, args.dist_url), flush=True)
+    torch.distributed.init_process_group(backend=args.dist_backend, init_method=args.dist_url,
+                                         world_size=args.world_size, rank=args.rank)
+    torch.distributed.barrier()
+    setup_for_distributed(args.rank == 0)
+
+
+def setup_for_distributed(is_master):
+    """
+    This function disables printing when not in master process
+    """
+    import builtins as __builtin__
+    builtin_print = __builtin__.print
+
+    def print(*args, **kwargs):
+        force = kwargs.pop('force', False)
+        if is_master or force:
+            builtin_print(*args, **kwargs)
+
+    __builtin__.print = print
+
diff --git a/opencood/opencood/tools/train.py b/opencood/opencood/tools/train.py
new file mode 100644
index 00000000..fd612f59
--- /dev/null
+++ b/opencood/opencood/tools/train.py
@@ -0,0 +1,207 @@
+# -*- coding: utf-8 -*-
+# Author: Runsheng Xu <rxx3386@ucla.edu>
+# License: TDG-Attribution-NonCommercial-NoDistrib
+
+
+import argparse
+import os
+import statistics
+
+import torch
+import tqdm
+from tensorboardX import SummaryWriter
+from torch.utils.data import DataLoader, DistributedSampler
+
+import opencood.hypes_yaml.yaml_utils as yaml_utils
+from opencood.tools import train_utils
+from opencood.tools import multi_gpu_utils
+from opencood.data_utils.datasets import build_dataset
+from opencood.tools import train_utils
+
+
+def train_parser():
+    parser = argparse.ArgumentParser(description="synthetic data generation")
+    parser.add_argument("--hypes_yaml", type=str, required=True,
+                        help='data generation yaml file needed ')
+    parser.add_argument('--model_dir', default='',
+                        help='Continued training path')
+    parser.add_argument("--half", action='store_true',
+                        help="whether train with half precision.")
+    parser.add_argument('--dist_url', default='env://',
+                        help='url used to set up distributed training')
+    opt = parser.parse_args()
+    return opt
+
+
+def main():
+    opt = train_parser()
+    hypes = yaml_utils.load_yaml(opt.hypes_yaml, opt)
+
+    multi_gpu_utils.init_distributed_mode(opt)
+
+    print('-----------------Dataset Building------------------')
+    opencood_train_dataset = build_dataset(hypes, visualize=False, train=True)
+    opencood_validate_dataset = build_dataset(hypes, visualize=False, train=False)
+
+    if opt.distributed:
+        sampler_train = DistributedSampler(opencood_train_dataset)
+        sampler_val = DistributedSampler(opencood_validate_dataset,
+                                         shuffle=False)
+
+        batch_sampler_train = torch.utils.data.BatchSampler(
+            sampler_train, hypes['train_params']['batch_size'], drop_last=True)
+
+        train_loader = DataLoader(opencood_train_dataset,
+                                  batch_sampler=batch_sampler_train,
+                                  num_workers=8,
+                                  collate_fn=opencood_train_dataset.collate_batch_train)
+        val_loader = DataLoader(opencood_validate_dataset,
+                                sampler=sampler_val,
+                                num_workers=8,
+                                collate_fn=opencood_train_dataset.collate_batch_train,
+                                drop_last=False)
+    else:
+        train_loader = DataLoader(opencood_train_dataset,
+                                  batch_size=hypes['train_params']['batch_size'],
+                                  num_workers=8,
+                                  collate_fn=opencood_train_dataset.collate_batch_train,
+                                  shuffle=True,
+                                  pin_memory=False,
+                                  drop_last=True)
+        val_loader = DataLoader(opencood_validate_dataset,
+                                batch_size=hypes['train_params']['batch_size'],
+                                num_workers=8,
+                                collate_fn=opencood_train_dataset.collate_batch_train,
+                                shuffle=False,
+                                pin_memory=False,
+                                drop_last=True)
+
+    print('---------------Creating Model------------------')
+    model = train_utils.create_model(hypes)
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+    # if we want to train from last checkpoint.
+    if opt.model_dir:
+        saved_path = opt.model_dir
+        init_epoch, model = train_utils.load_saved_model(saved_path,
+                                                         model)
+
+    else:
+        init_epoch = 0
+        # if we train the model from scratch, we need to create a folder
+        # to save the model,
+        saved_path = train_utils.setup_train(hypes)
+
+    # we assume gpu is necessary
+    if torch.cuda.is_available():
+        model.to(device)
+    model_without_ddp = model
+
+    if opt.distributed:
+        model = \
+            torch.nn.parallel.DistributedDataParallel(model,
+                                                      device_ids=[opt.gpu],
+                                                      find_unused_parameters=True)
+        model_without_ddp = model.module
+
+    # define the loss
+    criterion = train_utils.create_loss(hypes)
+
+    # optimizer setup
+    optimizer = train_utils.setup_optimizer(hypes, model_without_ddp)
+    # lr scheduler setup
+    num_steps = len(train_loader)
+    scheduler = train_utils.setup_lr_schedular(hypes, optimizer, num_steps)
+
+    # record training
+    writer = SummaryWriter(saved_path)
+
+    # half precision training
+    if opt.half:
+        scaler = torch.cuda.amp.GradScaler()
+
+    print('Training start')
+    epoches = hypes['train_params']['epoches']
+    # used to help schedule learning rate
+
+    for epoch in range(init_epoch, max(epoches, init_epoch)):
+        if hypes['lr_scheduler']['core_method'] != 'cosineannealwarm':
+            scheduler.step(epoch)
+        if hypes['lr_scheduler']['core_method'] == 'cosineannealwarm':
+            scheduler.step_update(epoch * num_steps + 0)
+        for param_group in optimizer.param_groups:
+            print('learning rate %.7f' % param_group["lr"])
+
+        if opt.distributed:
+            sampler_train.set_epoch(epoch)
+
+        pbar2 = tqdm.tqdm(total=len(train_loader), leave=True)
+
+        for i, batch_data in enumerate(train_loader):
+            # the model will be evaluation mode during validation
+            model.train()
+            model.zero_grad()
+            optimizer.zero_grad()
+
+            batch_data = train_utils.to_device(batch_data, device)
+
+            # case1 : late fusion train --> only ego needed,
+            # and ego is random selected
+            # case2 : early fusion train --> all data projected to ego
+            # case3 : intermediate fusion --> ['ego']['processed_lidar']
+            # becomes a list, which containing all data from other cavs
+            # as well
+            if not opt.half:
+                ouput_dict = model(batch_data['ego'])
+                # first argument is always your output dictionary,
+                # second argument is always your label dictionary.
+                final_loss = criterion(ouput_dict,
+                                       batch_data['ego']['label_dict'])
+            else:
+                with torch.cuda.amp.autocast():
+                    ouput_dict = model(batch_data['ego'])
+                    final_loss = criterion(ouput_dict,
+                                           batch_data['ego']['label_dict'])
+
+
+            criterion.logging(epoch, i, len(train_loader), writer, pbar=pbar2)
+            pbar2.update(1)
+
+            if not opt.half:
+                final_loss.backward()
+                optimizer.step()
+            else:
+                scaler.scale(final_loss).backward()
+                scaler.step(optimizer)
+                scaler.update()
+
+            if hypes['lr_scheduler']['core_method'] == 'cosineannealwarm':
+                scheduler.step_update(epoch * num_steps + i)
+
+        if epoch % hypes['train_params']['save_freq'] == 0:
+            torch.save(model_without_ddp.state_dict(),
+                os.path.join(saved_path, 'net_epoch%d.pth' % (epoch + 1)))
+
+        if epoch % hypes['train_params']['eval_freq'] == 0:
+            valid_ave_loss = []
+
+            with torch.no_grad():
+                for i, batch_data in enumerate(val_loader):
+                    model.eval()
+
+                    batch_data = train_utils.to_device(batch_data, device)
+                    ouput_dict = model(batch_data['ego'])
+
+                    final_loss = criterion(ouput_dict,
+                                           batch_data['ego']['label_dict'])
+                    valid_ave_loss.append(final_loss.item())
+            valid_ave_loss = statistics.mean(valid_ave_loss)
+            print('At epoch %d, the validation loss is %f' % (epoch,
+                                                              valid_ave_loss))
+            writer.add_scalar('Validate_Loss', valid_ave_loss, epoch)
+
+    print('Training Finished, checkpoints saved to %s' % saved_path)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/opencood/opencood/tools/train_utils.py b/opencood/opencood/tools/train_utils.py
new file mode 100644
index 00000000..b88de8cf
--- /dev/null
+++ b/opencood/opencood/tools/train_utils.py
@@ -0,0 +1,268 @@
+# -*- coding: utf-8 -*-
+# Author: Runsheng Xu <rxx3386@ucla.edu>, Hao Xiang <haxiang@g.ucla.edu>,
+# License: TDG-Attribution-NonCommercial-NoDistrib
+
+
+import glob
+import importlib
+import yaml
+import sys
+import os
+import re
+from datetime import datetime
+
+import torch
+import torch.optim as optim
+import timm
+
+def load_saved_model(saved_path, model):
+    """
+    Load saved model if exiseted
+
+    Parameters
+    __________
+    saved_path : str
+       model saved path
+    model : opencood object
+        The model instance.
+
+    Returns
+    -------
+    model : opencood object
+        The model instance loaded pretrained params.
+    """
+    assert os.path.exists(saved_path), '{} not found'.format(saved_path)
+
+    def findLastCheckpoint(save_dir):
+        if os.path.exists(os.path.join(saved_path, 'latest.pth')):
+            return 10000
+        file_list = glob.glob(os.path.join(save_dir, '*epoch*.pth'))
+        if file_list:
+            epochs_exist = []
+            for file_ in file_list:
+                result = re.findall(".*epoch(.*).pth.*", file_)
+                epochs_exist.append(int(result[0]))
+            initial_epoch_ = max(epochs_exist)
+        else:
+            initial_epoch_ = 0
+        return initial_epoch_
+
+    initial_epoch = findLastCheckpoint(saved_path)
+    if initial_epoch > 0:
+        model_file = os.path.join(saved_path,
+                         'net_epoch%d.pth' % initial_epoch) \
+            if initial_epoch != 10000 else os.path.join(saved_path,
+                         'latest.pth')
+        print('resuming by loading epoch %d' % initial_epoch)
+        checkpoint = torch.load(
+            model_file,
+            map_location='cpu')
+        model.load_state_dict(checkpoint, strict=False)
+
+        del checkpoint
+
+    return initial_epoch, model
+
+
+def setup_train(hypes):
+    """
+    Create folder for saved model based on current timestep and model name
+
+    Parameters
+    ----------
+    hypes: dict
+        Config yaml dictionary for training:
+    """
+    model_name = hypes['name']
+    current_time = datetime.now()
+
+    folder_name = current_time.strftime("_%Y_%m_%d_%H_%M_%S")
+    folder_name = model_name + folder_name
+
+    current_path = os.path.dirname(__file__)
+    current_path = os.path.join(current_path, '../logs')
+
+    full_path = os.path.join(current_path, folder_name)
+
+    if not os.path.exists(full_path):
+        if not os.path.exists(full_path):
+            try:
+                os.makedirs(full_path)
+            except FileExistsError:
+                pass
+        # save the yaml file
+        save_name = os.path.join(full_path, 'config.yaml')
+        with open(save_name, 'w') as outfile:
+            yaml.dump(hypes, outfile)
+
+    return full_path
+
+
+
+def create_model(hypes):
+    """
+    Import the module "models/[model_name].py
+
+    Parameters
+    __________
+    hypes : dict
+        Dictionary containing parameters.
+
+    Returns
+    -------
+    model : opencood,object
+        Model object.
+    """
+    backbone_name = hypes['model']['core_method']
+    backbone_config = hypes['model']['args']
+
+    model_filename = "opencood.models." + backbone_name
+    model_lib = importlib.import_module(model_filename)
+    model = None
+    target_model_name = backbone_name.replace('_', '')
+
+    for name, cls in model_lib.__dict__.items():
+        if name.lower() == target_model_name.lower():
+            model = cls
+
+    if model is None:
+        print('backbone not found in models folder. Please make sure you '
+              'have a python file named %s and has a class '
+              'called %s ignoring upper/lower case' % (model_filename,
+                                                       target_model_name))
+        exit(0)
+    instance = model(backbone_config)
+    return instance
+
+
+def create_loss(hypes):
+    """
+    Create the loss function based on the given loss name.
+
+    Parameters
+    ----------
+    hypes : dict
+        Configuration params for training.
+    Returns
+    -------
+    criterion : opencood.object
+        The loss function.
+    """
+    loss_func_name = hypes['loss']['core_method']
+    loss_func_config = hypes['loss']['args']
+
+    loss_filename = "opencood.loss." + loss_func_name
+    loss_lib = importlib.import_module(loss_filename)
+    loss_func = None
+    target_loss_name = loss_func_name.replace('_', '')
+
+    for name, lfunc in loss_lib.__dict__.items():
+        if name.lower() == target_loss_name.lower():
+            loss_func = lfunc
+
+    if loss_func is None:
+        print('loss function not found in loss folder. Please make sure you '
+              'have a python file named %s and has a class '
+              'called %s ignoring upper/lower case' % (loss_filename,
+                                                       target_loss_name))
+        exit(0)
+
+    criterion = loss_func(loss_func_config)
+    return criterion
+
+
+def setup_optimizer(hypes, model):
+    """
+    Create optimizer corresponding to the yaml file
+
+    Parameters
+    ----------
+    hypes : dict
+        The training configurations.
+    model : opencood model
+        The pytorch model
+    """
+    method_dict = hypes['optimizer']
+    optimizer_method = getattr(optim, method_dict['core_method'], None)
+    print('optimizer method is: %s' % optimizer_method)
+
+    if not optimizer_method:
+        raise ValueError('{} is not supported'.format(method_dict['name']))
+    if 'args' in method_dict:
+        return optimizer_method(filter(lambda p: p.requires_grad,
+                                       model.parameters()),
+                                lr=method_dict['lr'],
+                                **method_dict['args'])
+    else:
+        return optimizer_method(filter(lambda p: p.requires_grad,
+                                       model.parameters()),
+                                lr=method_dict['lr'])
+
+
+def setup_lr_schedular(hypes, optimizer, n_iter_per_epoch):
+    """
+    Set up the learning rate schedular.
+
+    Parameters
+    ----------
+    hypes : dict
+        The training configurations.
+
+    optimizer : torch.optimizer
+    """
+    lr_schedule_config = hypes['lr_scheduler']
+
+    if lr_schedule_config['core_method'] == 'step':
+        from torch.optim.lr_scheduler import StepLR
+        step_size = lr_schedule_config['step_size']
+        gamma = lr_schedule_config['gamma']
+        scheduler = StepLR(optimizer, step_size=step_size, gamma=gamma)
+
+    elif lr_schedule_config['core_method'] == 'multistep':
+        from torch.optim.lr_scheduler import MultiStepLR
+        milestones = lr_schedule_config['step_size']
+        gamma = lr_schedule_config['gamma']
+        scheduler = MultiStepLR(optimizer,
+                                milestones=milestones,
+                                gamma=gamma)
+
+    elif lr_schedule_config['core_method'] == 'exponential':
+        print('ExponentialLR is chosen for lr scheduler')
+        from torch.optim.lr_scheduler import ExponentialLR
+        gamma = lr_schedule_config['gamma']
+        scheduler = ExponentialLR(optimizer, gamma)
+
+    elif lr_schedule_config['core_method'] == 'cosineannealwarm':
+        print('cosine annealing is chosen for lr scheduler')
+        from timm.scheduler.cosine_lr import CosineLRScheduler
+
+        num_steps = lr_schedule_config['epoches'] * n_iter_per_epoch
+        warmup_lr = lr_schedule_config['warmup_lr']
+        warmup_steps = lr_schedule_config['warmup_epoches'] * n_iter_per_epoch
+        lr_min = lr_schedule_config['lr_min']
+
+        scheduler = CosineLRScheduler(
+            optimizer,
+            t_initial=num_steps,
+            lr_min=lr_min,
+            warmup_lr_init=warmup_lr,
+            warmup_t=warmup_steps,
+            cycle_limit=1,
+            t_in_epochs=False,
+        )
+    else:
+        sys.exit('not supported lr schedular')
+
+    return scheduler
+
+
+def to_device(inputs, device):
+    if isinstance(inputs, list):
+        return [to_device(x, device) for x in inputs]
+    elif isinstance(inputs, dict):
+        return {k: to_device(v, device) for k, v in inputs.items()}
+    else:
+        if isinstance(inputs, int) or isinstance(inputs, float) \
+                or isinstance(inputs, str):
+            return inputs
+        return inputs.to(device)
diff --git a/opencood/opencood/utils/__init__.py b/opencood/opencood/utils/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/opencood/opencood/utils/box_overlaps.pyx b/opencood/opencood/utils/box_overlaps.pyx
new file mode 100644
index 00000000..cd543ce4
--- /dev/null
+++ b/opencood/opencood/utils/box_overlaps.pyx
@@ -0,0 +1,147 @@
+# --------------------------------------------------------
+# Fast R-CNN
+# Copyright (c) 2015 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Sergey Karayev
+# --------------------------------------------------------
+
+import numpy as np
+cimport numpy as np
+from cython.parallel import prange, parallel
+
+
+DTYPE = np.float32
+ctypedef float DTYPE_t
+
+
+def bbox_overlaps(
+        np.ndarray[DTYPE_t, ndim=2] boxes,
+        np.ndarray[DTYPE_t, ndim=2] query_boxes):
+    """
+    Parameters
+    ----------
+    boxes: (N, 4) ndarray of float
+    query_boxes: (K, 4) ndarray of float
+    Returns
+    -------
+    overlaps: (N, K) ndarray of overlap between boxes and query_boxes
+    """
+    cdef unsigned int N = boxes.shape[0]
+    cdef unsigned int K = query_boxes.shape[0]
+    cdef np.ndarray[DTYPE_t, ndim=2] overlaps = np.zeros((N, K), dtype=DTYPE)
+    cdef DTYPE_t iw, ih, box_area
+    cdef DTYPE_t ua
+    cdef unsigned int k, n
+    for k in range(K):
+        box_area = (
+            (query_boxes[k, 2] - query_boxes[k, 0] + 1) *
+            (query_boxes[k, 3] - query_boxes[k, 1] + 1)
+        )
+        for n in range(N):
+            iw = (
+                min(boxes[n, 2], query_boxes[k, 2]) -
+                max(boxes[n, 0], query_boxes[k, 0]) + 1
+            )
+            if iw > 0:
+                ih = (
+                    min(boxes[n, 3], query_boxes[k, 3]) -
+                    max(boxes[n, 1], query_boxes[k, 1]) + 1
+                )
+                if ih > 0:
+                    ua = float(
+                        (boxes[n, 2] - boxes[n, 0] + 1) *
+                        (boxes[n, 3] - boxes[n, 1] + 1) +
+                        box_area - iw * ih
+                    )
+                    overlaps[n, k] = iw * ih / ua
+    return overlaps
+
+def bbox_intersections(
+        np.ndarray[DTYPE_t, ndim=2] boxes,
+        np.ndarray[DTYPE_t, ndim=2] query_boxes):
+    """
+    For each query box compute the intersection ratio covered by boxes
+    ----------
+    Parameters
+    ----------
+    boxes: (N, 4) ndarray of float
+    query_boxes: (K, 4) ndarray of float
+    Returns
+    -------
+    overlaps: (N, K) ndarray of intersec between boxes and query_boxes
+    """
+    cdef unsigned int N = boxes.shape[0]
+    cdef unsigned int K = query_boxes.shape[0]
+    cdef np.ndarray[DTYPE_t, ndim=2] intersec = np.zeros((N, K), dtype=DTYPE)
+    cdef DTYPE_t iw, ih, box_area
+    cdef DTYPE_t ua
+    cdef unsigned int k, n
+    for k in range(K):
+        box_area = (
+            (query_boxes[k, 2] - query_boxes[k, 0] + 1) *
+            (query_boxes[k, 3] - query_boxes[k, 1] + 1)
+        )
+        for n in range(N):
+            iw = (
+                min(boxes[n, 2], query_boxes[k, 2]) -
+                max(boxes[n, 0], query_boxes[k, 0]) + 1
+            )
+            if iw > 0:
+                ih = (
+                    min(boxes[n, 3], query_boxes[k, 3]) -
+                    max(boxes[n, 1], query_boxes[k, 1]) + 1
+                )
+                if ih > 0:
+                    intersec[n, k] = iw * ih / box_area
+    return intersec
+
+# Compute bounding box voting
+def box_vote(
+        np.ndarray[float, ndim=2] dets_NMS,
+        np.ndarray[float, ndim=2] dets_all):
+    cdef np.ndarray[float, ndim=2] dets_voted = np.zeros((dets_NMS.shape[0], dets_NMS.shape[1]), dtype=np.float32)
+    cdef unsigned int N = dets_NMS.shape[0]
+    cdef unsigned int M = dets_all.shape[0]
+
+    cdef np.ndarray[float, ndim=1] det
+    cdef np.ndarray[float, ndim=1] acc_box
+    cdef float acc_score
+
+    cdef np.ndarray[float, ndim=1] det2
+    cdef float bi0, bi1, bit2, bi3
+    cdef float iw, ih, ua
+
+    cdef float thresh=0.5
+
+    for i in range(N):
+        det = dets_NMS[i, :]
+        acc_box = np.zeros((4), dtype=np.float32)
+        acc_score = 0.0
+
+        for m in range(M):
+            det2 = dets_all[m, :]
+
+            bi0 = max(det[0], det2[0])
+            bi1 = max(det[1], det2[1])
+            bi2 = min(det[2], det2[2])
+            bi3 = min(det[3], det2[3])
+
+            iw = bi2 - bi0 + 1
+            ih = bi3 - bi1 + 1
+
+            if not (iw > 0 and ih > 0):
+                continue
+
+            ua = (det[2] - det[0] + 1) * (det[3] - det[1] + 1) + (det2[2] - det2[0] + 1) * (det2[3] - det2[1] + 1) - iw * ih
+            ov = iw * ih / ua
+
+            if (ov < thresh):
+                continue
+
+            acc_box += det2[4] * det2[0:4]
+            acc_score += det2[4]
+
+        dets_voted[i][0:4] = acc_box / acc_score
+        dets_voted[i][4] = det[4]       # Keep the original score
+
+    return dets_voted
diff --git a/opencood/opencood/utils/box_utils.py b/opencood/opencood/utils/box_utils.py
new file mode 100644
index 00000000..222dc36c
--- /dev/null
+++ b/opencood/opencood/utils/box_utils.py
@@ -0,0 +1,956 @@
+# -*- coding: utf-8 -*-
+# Author: Runsheng Xu <rxx3386@ucla.edu>, Hao Xiang <haxiang@g.ucla.edu>,
+# License: TDG-Attribution-NonCommercial-NoDistrib
+
+
+"""
+Bounding box related utility functions
+"""
+import sys
+
+import numpy as np
+
+import torch
+import torch.nn.functional as F
+import opencood.utils.common_utils as common_utils
+from opencood.utils.transformation_utils import x1_to_x2
+
+
+def corner_to_center(corner3d, order='lwh'):
+    """
+    Convert 8 corners to x, y, z, dx, dy, dz, yaw.
+
+    Parameters
+    ----------
+    corner3d : np.ndarray
+        (N, 8, 3)
+
+    order : str
+        'lwh' or 'hwl'
+
+    Returns
+    -------
+    box3d : np.ndarray
+        (N, 7)
+    """
+    assert corner3d.ndim == 3
+    batch_size = corner3d.shape[0]
+
+    xyz = np.mean(corner3d[:, [0, 3, 5, 6], :], axis=1)
+    h = abs(np.mean(corner3d[:, 4:, 2] - corner3d[:, :4, 2], axis=1,
+                    keepdims=True))
+    l = (np.sqrt(np.sum((corner3d[:, 0, [0, 1]] - corner3d[:, 3, [0, 1]]) ** 2,
+                        axis=1, keepdims=True)) +
+         np.sqrt(np.sum((corner3d[:, 2, [0, 1]] - corner3d[:, 1, [0, 1]]) ** 2,
+                        axis=1, keepdims=True)) +
+         np.sqrt(np.sum((corner3d[:, 4, [0, 1]] - corner3d[:, 7, [0, 1]]) ** 2,
+                        axis=1, keepdims=True)) +
+         np.sqrt(np.sum((corner3d[:, 5, [0, 1]] - corner3d[:, 6, [0, 1]]) ** 2,
+                        axis=1, keepdims=True))) / 4
+
+    w = (np.sqrt(
+        np.sum((corner3d[:, 0, [0, 1]] - corner3d[:, 1, [0, 1]]) ** 2, axis=1,
+               keepdims=True)) +
+         np.sqrt(np.sum((corner3d[:, 2, [0, 1]] - corner3d[:, 3, [0, 1]]) ** 2,
+                        axis=1, keepdims=True)) +
+         np.sqrt(np.sum((corner3d[:, 4, [0, 1]] - corner3d[:, 5, [0, 1]]) ** 2,
+                        axis=1, keepdims=True)) +
+         np.sqrt(np.sum((corner3d[:, 6, [0, 1]] - corner3d[:, 7, [0, 1]]) ** 2,
+                        axis=1, keepdims=True))) / 4
+
+    theta = (np.arctan2(corner3d[:, 1, 1] - corner3d[:, 2, 1],
+                        corner3d[:, 1, 0] - corner3d[:, 2, 0]) +
+             np.arctan2(corner3d[:, 0, 1] - corner3d[:, 3, 1],
+                        corner3d[:, 0, 0] - corner3d[:, 3, 0]) +
+             np.arctan2(corner3d[:, 5, 1] - corner3d[:, 6, 1],
+                        corner3d[:, 5, 0] - corner3d[:, 6, 0]) +
+             np.arctan2(corner3d[:, 4, 1] - corner3d[:, 7, 1],
+                        corner3d[:, 4, 0] - corner3d[:, 7, 0]))[:,
+            np.newaxis] / 4
+
+    if order == 'lwh':
+        return np.concatenate([xyz, l, w, h, theta], axis=1).reshape(
+            batch_size, 7)
+    elif order == 'hwl':
+        return np.concatenate([xyz, h, w, l, theta], axis=1).reshape(
+            batch_size, 7)
+    else:
+        sys.exit('Unknown order')
+
+
+def boxes_to_corners2d(boxes3d, order):
+    """
+      0 -------- 1
+      |          |
+      |          |
+      |          |
+      3 -------- 2
+    Parameters
+    __________
+    boxes3d: np.ndarray or torch.Tensor
+        (N, 7) [x, y, z, dx, dy, dz, heading], (x, y, z) is the box center.
+
+    order : str
+        'lwh' or 'hwl'
+
+    Returns:
+        corners2d: np.ndarray or torch.Tensor
+        (N, 4, 3), the 4 corners of the bounding box.
+
+    """
+    corners3d = boxes_to_corners_3d(boxes3d, order)
+    corners2d = corners3d[:, :4, :]
+    return corners2d
+
+
+def boxes2d_to_corners2d(boxes2d, order="lwh"):
+    """
+      0 -------- 1
+      |          |
+      |          |
+      |          |
+      3 -------- 2
+    Parameters
+    __________
+    boxes2d: np.ndarray or torch.Tensor
+        (..., 5) [x, y, dx, dy, heading], (x, y) is the box center.
+
+    order : str
+        'lwh' or 'hwl'
+
+    Returns:
+        corners2d: np.ndarray or torch.Tensor
+        (..., 4, 2), the 4 corners of the bounding box.
+
+    """
+    assert order == "lwh", \
+        "boxes2d_to_corners_2d only supports lwh order for now."
+    boxes2d, is_numpy = common_utils.check_numpy_to_torch(boxes2d)
+    template = boxes2d.new_tensor((
+        [1, -1], [1, 1], [-1, 1], [-1, -1]
+    )) / 2
+    input_shape = boxes2d.shape
+    boxes2d = boxes2d.view(-1, 5)
+    corners2d = boxes2d[:, None, 2:4].repeat(1, 4, 1) * template[None, :, :]
+    corners2d = common_utils.rotate_points_along_z_2d(corners2d.view(-1, 2),
+                                                      boxes2d[:,
+                                                      4].repeat_interleave(
+                                                          4)).view(-1, 4,
+                                                                   2)
+    corners2d += boxes2d[:, None, 0:2]
+    corners2d = corners2d.view(*(input_shape[:-1]), 4, 2)
+    return corners2d
+
+
+def boxes_to_corners_3d(boxes3d, order):
+    """
+        4 -------- 5
+       /|         /|
+      7 -------- 6 .
+      | |        | |
+      . 0 -------- 1
+      |/         |/
+      3 -------- 2
+    Parameters
+    __________
+    boxes3d: np.ndarray or torch.Tensor
+        (N, 7) [x, y, z, dx, dy, dz, heading], (x, y, z) is the box center.
+
+    order : str
+        'lwh' or 'hwl'
+
+    Returns:
+        corners3d: np.ndarray or torch.Tensor
+        (N, 8, 3), the 8 corners of the bounding box.
+
+    """
+    # ^ z
+    # |
+    # |
+    # | . x
+    # |/
+    # +-------> y
+
+    boxes3d, is_numpy = common_utils.check_numpy_to_torch(boxes3d)
+    boxes3d_ = boxes3d
+
+    if order == 'hwl':
+        boxes3d_ = boxes3d[:, [0, 1, 2, 5, 4, 3, 6]]
+
+    template = boxes3d_.new_tensor((
+        [1, -1, -1], [1, 1, -1], [-1, 1, -1], [-1, -1, -1],
+        [1, -1, 1], [1, 1, 1], [-1, 1, 1], [-1, -1, 1],
+    )) / 2
+
+    corners3d = boxes3d_[:, None, 3:6].repeat(1, 8, 1) * template[None, :, :]
+    corners3d = common_utils.rotate_points_along_z(corners3d.view(-1, 8, 3),
+                                                   boxes3d_[:, 6]).view(-1, 8,
+                                                                        3)
+    corners3d += boxes3d_[:, None, 0:3]
+
+    return corners3d.numpy() if is_numpy else corners3d
+
+
+
+def box3d_to_2d(box3d):
+    """
+    Convert 3D bounding box to 2D.
+
+    Parameters
+    ----------
+    box3d : np.ndarray
+        (n, 8, 3)
+
+    Returns
+    -------
+    box2d : np.ndarray
+        (n, 4, 2), project 3d to 2d.
+    """
+    box2d = box3d[:, :4, :2]
+    return box2d
+
+
+def corner2d_to_standup_box(box2d):
+    """
+    Find the minmaxx, minmaxy for each 2d box. (N, 4, 2) -> (N, 4)
+    x1, y1, x2, y2
+
+    Parameters
+    ----------
+    box2d : np.ndarray
+        (n, 4, 2), four corners of the 2d bounding box.
+
+    Returns
+    -------
+    standup_box2d : np.ndarray
+        (n, 4)
+    """
+    N = box2d.shape[0]
+    standup_boxes2d = np.zeros((N, 4))
+
+    standup_boxes2d[:, 0] = np.min(box2d[:, :, 0], axis=1)
+    standup_boxes2d[:, 1] = np.min(box2d[:, :, 1], axis=1)
+    standup_boxes2d[:, 2] = np.max(box2d[:, :, 0], axis=1)
+    standup_boxes2d[:, 3] = np.max(box2d[:, :, 1], axis=1)
+
+    return standup_boxes2d
+
+
+def corner_to_standup_box_torch(box_corner):
+    """
+    Find the minmax x and y for each bounding box.
+
+    Parameters
+    ----------
+    box_corner : torch.Tensor
+        Shape: (N, 8, 3) or (N, 4)
+
+    Returns
+    -------
+    standup_box2d : torch.Tensor
+        (n, 4)
+    """
+    N = box_corner.shape[0]
+    standup_boxes2d = torch.zeros((N, 4))
+
+    standup_boxes2d = standup_boxes2d.to(box_corner.device)
+
+    standup_boxes2d[:, 0] = torch.min(box_corner[:, :, 0], dim=1).values
+    standup_boxes2d[:, 1] = torch.min(box_corner[:, :, 1], dim=1).values
+    standup_boxes2d[:, 2] = torch.max(box_corner[:, :, 0], dim=1).values
+    standup_boxes2d[:, 3] = torch.max(box_corner[:, :, 1], dim=1).values
+
+    return standup_boxes2d
+
+
+def project_box3d(box3d, transformation_matrix):
+    """
+    Project the 3d bounding box to another coordinate system based on the
+    transfomration matrix.
+
+    Parameters
+    ----------
+    box3d : torch.Tensor or np.ndarray
+        3D bounding box, (N, 8, 3)
+
+    transformation_matrix : torch.Tensor or np.ndarray
+        Transformation matrix, (4, 4)
+
+    Returns
+    -------
+    projected_box3d : torch.Tensor
+        The projected bounding box, (N, 8, 3)
+    """
+    assert transformation_matrix.shape == (4, 4)
+    box3d, is_numpy = \
+        common_utils.check_numpy_to_torch(box3d)
+    transformation_matrix, _ = \
+        common_utils.check_numpy_to_torch(transformation_matrix)
+
+    # (N, 3, 8)
+    box3d_corner = box3d.transpose(1, 2)
+    # (N, 1, 8)
+    torch_ones = torch.ones((box3d_corner.shape[0], 1, 8))
+    torch_ones = torch_ones.to(box3d_corner.device)
+    # (N, 4, 8)
+    box3d_corner = torch.cat((box3d_corner, torch_ones),
+                             dim=1)
+    # (N, 4, 8)
+    projected_box3d = torch.matmul(transformation_matrix,
+                                   box3d_corner)
+    # (N, 8, 3)
+    projected_box3d = projected_box3d[:, :3, :].transpose(1, 2)
+
+    return projected_box3d if not is_numpy else projected_box3d.numpy()
+
+
+def project_points_by_matrix_torch(points, transformation_matrix):
+    """
+    Project the points to another coordinate system based on the
+    transfomration matrix.
+
+    Parameters
+    ----------
+    points : torch.Tensor
+        3D points, (N, 3)
+
+    transformation_matrix : torch.Tensor
+        Transformation matrix, (4, 4)
+
+    Returns
+    -------
+    projected_points : torch.Tensor
+        The projected points, (N, 3)
+    """
+    # convert to homogeneous  coordinates via padding 1 at the last dimension.
+    # (N, 4)
+    points_homogeneous = F.pad(points, (0, 1), mode="constant", value=1)
+    # (N, 4)
+    projected_points = torch.einsum("ik, jk->ij", points_homogeneous,
+                                    transformation_matrix)
+    return projected_points[:, :3]
+
+
+def get_mask_for_boxes_within_range_torch(boxes):
+    """
+    Generate mask to remove the bounding boxes
+    outside the range.
+
+    Parameters
+    ----------
+    boxes : torch.Tensor
+        Groundtruth bbx, shape: N,8,3 or N,4,2
+    Returns
+    -------
+    mask: torch.Tensor
+        The mask for bounding box -- True means the
+        bbx is within the range and False means the
+        bbx is outside the range.
+    """
+    from opencood.data_utils.datasets import GT_RANGE
+
+    # mask out the gt bounding box out fixed range (-140, -40, -3, 140, 40 1)
+    device = boxes.device
+    boundary_lower_range = \
+        torch.Tensor(GT_RANGE[:2]).reshape(1, 1, -1).to(device)
+    boundary_higher_range = \
+        torch.Tensor(GT_RANGE[3:5]).reshape(1, 1, -1).to(device)
+
+    mask = torch.all(
+        torch.all(boxes[:, :, :2] >= boundary_lower_range,
+                  dim=-1) & \
+        torch.all(boxes[:, :, :2] <= boundary_higher_range,
+                  dim=-1), dim=-1)
+
+    return mask
+
+
+def mask_boxes_outside_range_numpy(boxes, limit_range, order,
+                                   min_num_corners=8, return_mask=False):
+    """
+    Parameters
+    ----------
+    boxes: np.ndarray
+        (N, 7) [x, y, z, dx, dy, dz, heading], (x, y, z) is the box center
+
+    limit_range: list
+        [minx, miny, minz, maxx, maxy, maxz]
+
+    min_num_corners: int
+        The required minimum number of corners to be considered as in range.
+
+    order : str
+        'lwh' or 'hwl'
+
+    return_mask : bool
+        Whether return the mask.
+
+    Returns
+    -------
+    boxes: np.ndarray
+        The filtered boxes.
+    """
+    assert boxes.shape[1] == 8 or boxes.shape[1] == 7
+
+    new_boxes = boxes.copy()
+    if boxes.shape[1] == 7:
+        new_boxes = boxes_to_corners_3d(new_boxes, order)
+
+    mask = ((new_boxes >= limit_range[0:3]) &
+            (new_boxes <= limit_range[3:6])).all(axis=2)
+    mask = mask.sum(axis=1) >= min_num_corners  # (N)
+
+    if return_mask:
+        return boxes[mask], mask
+    return boxes[mask]
+
+
+def create_bbx(extent):
+    """
+    Create bounding box with 8 corners under obstacle vehicle reference.
+
+    Parameters
+    ----------
+    extent : list
+        Width, height, length of the bbx.
+
+    Returns
+    -------
+    bbx : np.array
+        The bounding box with 8 corners, shape: (8, 3)
+    """
+
+    bbx = np.array([[extent[0], -extent[1], -extent[2]],
+                    [extent[0], extent[1], -extent[2]],
+                    [-extent[0], extent[1], -extent[2]],
+                    [-extent[0], -extent[1], -extent[2]],
+                    [extent[0], -extent[1], extent[2]],
+                    [extent[0], extent[1], extent[2]],
+                    [-extent[0], extent[1], extent[2]],
+                    [-extent[0], -extent[1], extent[2]]])
+
+    return bbx
+
+
+def project_world_objects(object_dict,
+                          output_dict,
+                          lidar_pose,
+                          lidar_range,
+                          order):
+    """
+    Project the objects under world coordinates into another coordinate
+    based on the provided extrinsic.
+
+    Parameters
+    ----------
+    object_dict : dict
+        The dictionary contains all objects surrounding a certain cav.
+
+    output_dict : dict
+        key: object id, value: object bbx (xyzlwhyaw).
+
+    lidar_pose : list
+        (6, ), lidar pose under world coordinate, [x, y, z, roll, yaw, pitch].
+
+    lidar_range : list
+         [minx, miny, minz, maxx, maxy, maxz]
+
+    order : str
+        'lwh' or 'hwl'
+    """
+    for object_id, object_content in object_dict.items():
+        location = object_content['location']
+        rotation = object_content['angle']
+        center = object_content['center']
+        extent = object_content['extent']
+
+        object_pose = [location[0] + center[0],
+                       location[1] + center[1],
+                       location[2] + center[2],
+                       rotation[0], rotation[1], rotation[2]]
+        object2lidar = x1_to_x2(object_pose, lidar_pose)
+
+        # shape (3, 8)
+        bbx = create_bbx(extent).T
+        # bounding box under ego coordinate shape (4, 8)
+        bbx = np.r_[bbx, [np.ones(bbx.shape[1])]]
+
+        # project the 8 corners to world coordinate
+        bbx_lidar = np.dot(object2lidar, bbx).T
+        bbx_lidar = np.expand_dims(bbx_lidar[:, :3], 0)
+        bbx_lidar = corner_to_center(bbx_lidar, order=order)
+        bbx_lidar = mask_boxes_outside_range_numpy(bbx_lidar,
+                                                   lidar_range,
+                                                   order)
+
+        if bbx_lidar.shape[0] > 0:
+            output_dict.update({object_id: bbx_lidar})
+
+
+def get_points_in_rotated_box(p, box_corner):
+    """
+    Get points within a rotated bounding box (2D version).
+
+    Parameters
+    ----------
+    p : numpy.array
+        Points to be tested with shape (N, 2).
+    box_corner : numpy.array
+        Corners of bounding box with shape (4, 2).
+
+    Returns
+    -------
+    p_in_box : numpy.array
+        Points within the box.
+
+    """
+    edge1 = box_corner[1, :] - box_corner[0, :]
+    edge2 = box_corner[3, :] - box_corner[0, :]
+    p_rel = p - box_corner[0, :].reshape(1, -1)
+
+    l1 = get_projection_length_for_vector_projection(p_rel, edge1)
+    l2 = get_projection_length_for_vector_projection(p_rel, edge2)
+    # A point is within the box, if and only after projecting the
+    # point onto the two edges s.t. p_rel = [edge1, edge2] @ [l1, l2]^T,
+    # we have 0<=l1<=1 and 0<=l2<=1.
+    mask = np.logical_and(l1 >= 0, l1 <= 1)
+    mask = np.logical_and(mask, l2 >= 0)
+    mask = np.logical_and(mask, l2 <= 1)
+    p_in_box = p[mask, :]
+    return p_in_box
+
+
+def get_points_in_rotated_box_3d(p, box_corner):
+    """
+    Get points within a rotated bounding box (3D version).
+
+    Parameters
+    ----------
+    p : numpy.array
+        Points to be tested with shape (N, 3).
+    box_corner : numpy.array
+        Corners of bounding box with shape (8, 3).
+
+    Returns
+    -------
+    p_in_box : numpy.array
+        Points within the box.
+
+    """
+    edge1 = box_corner[1, :] - box_corner[0, :]
+    edge2 = box_corner[3, :] - box_corner[0, :]
+    edge3 = box_corner[4, :] - box_corner[0, :]
+
+    p_rel = p - box_corner[0, :].reshape(1, -1)
+
+    l1 = get_projection_length_for_vector_projection(p_rel, edge1)
+    l2 = get_projection_length_for_vector_projection(p_rel, edge2)
+    l3 = get_projection_length_for_vector_projection(p_rel, edge3)
+    # A point is within the box, if and only after projecting the
+    # point onto the two edges s.t. p_rel = [edge1, edge2] @ [l1, l2]^T,
+    # we have 0<=l1<=1 and 0<=l2<=1.
+    mask1 = np.logical_and(l1 >= 0, l1 <= 1)
+    mask2 = np.logical_and(l2 >= 0, l2 <= 1)
+    mask3 = np.logical_and(l3 >= 0, l3 <= 1)
+
+    mask = np.logical_and(mask1, mask2)
+    mask = np.logical_and(mask, mask3)
+    p_in_box = p[mask, :]
+
+    return p_in_box
+
+
+def get_projection_length_for_vector_projection(a, b):
+    """
+    Get projection length for the Vector projection of a onto b s.t.
+    a_projected = length * b. (2D version) See
+    https://en.wikipedia.org/wiki/Vector_projection#Vector_projection_2
+    for more details.
+
+    Parameters
+    ----------
+    a : numpy.array
+        The vectors to be projected with shape (N, 2).
+
+    b : numpy.array
+        The vector that is projected onto with shape (2).
+
+    Returns
+    -------
+    length : numpy.array
+        The length of projected a with respect to b.
+    """
+    assert np.sum(b ** 2, axis=-1) > 1e-6
+    length = a.dot(b) / np.sum(b ** 2, axis=-1)
+    return length
+
+
+def nms_rotated(boxes, scores, threshold):
+    """Performs rorated non-maximum suppression and returns indices of kept
+    boxes.
+
+    Parameters
+    ----------
+    boxes : torch.tensor
+        The location preds with shape (N, 4, 2).
+
+    scores : torch.tensor
+        The predicted confidence score with shape (N,)
+
+    threshold: float
+        IoU threshold to use for filtering.
+
+    Returns
+    -------
+        An array of index
+    """
+    if boxes.shape[0] == 0:
+        return np.array([], dtype=np.int32)
+    boxes = boxes.cpu().detach().numpy()
+    scores = scores.cpu().detach().numpy()
+
+    polygons = common_utils.convert_format(boxes)
+
+    top = 1000
+    # Get indicies of boxes sorted by scores (highest first)
+    ixs = scores.argsort()[::-1][:top]
+
+    pick = []
+    while len(ixs) > 0:
+        # Pick top box and add its index to the list
+        i = ixs[0]
+        pick.append(i)
+        # Compute IoU of the picked box with the rest
+        iou = common_utils.compute_iou(polygons[i], polygons[ixs[1:]])
+        # Identify boxes with IoU over the threshold. This
+        # returns indices into ixs[1:], so add 1 to get
+        # indices into ixs.
+        remove_ixs = np.where(iou > threshold)[0] + 1
+        # Remove indices of the picked and overlapped boxes.
+        ixs = np.delete(ixs, remove_ixs)
+        ixs = np.delete(ixs, 0)
+
+    return np.array(pick, dtype=np.int32)
+
+
+def nms_pytorch(boxes: torch.tensor, thresh_iou: float):
+    """
+    Apply non-maximum suppression to avoid detecting too many
+    overlapping bounding boxes for a given object.
+
+    Parameters
+    ----------
+    boxes : torch.tensor
+        The location preds along with the class predscores,
+         Shape: [num_boxes,5].
+    thresh_iou : float
+        (float) The overlap thresh for suppressing unnecessary boxes.
+    Returns
+    -------
+        A list of index
+    """
+
+    # we extract coordinates for every
+    # prediction box present in P
+    x1 = boxes[:, 0]
+    y1 = boxes[:, 1]
+    x2 = boxes[:, 2]
+    y2 = boxes[:, 3]
+
+    # we extract the confidence scores as well
+    scores = boxes[:, 4]
+
+    # calculate area of every block in P
+    areas = (x2 - x1) * (y2 - y1)
+
+    # sort the prediction boxes in P
+    # according to their confidence scores
+    order = scores.argsort()
+
+    # initialise an empty list for
+    # filtered prediction boxes
+    keep = []
+
+    while len(order) > 0:
+
+        # extract the index of the
+        # prediction with highest score
+        # we call this prediction S
+        idx = order[-1]
+
+        # push S in filtered predictions list
+        keep.append(idx.numpy().item()
+                    if not idx.is_cuda else idx.cpu().detach().numpy().item())
+
+        # remove S from P
+        order = order[:-1]
+
+        # sanity check
+        if len(order) == 0:
+            break
+
+        # select coordinates of BBoxes according to
+        # the indices in order
+        xx1 = torch.index_select(x1, dim=0, index=order)
+        xx2 = torch.index_select(x2, dim=0, index=order)
+        yy1 = torch.index_select(y1, dim=0, index=order)
+        yy2 = torch.index_select(y2, dim=0, index=order)
+
+        # find the coordinates of the intersection boxes
+        xx1 = torch.max(xx1, x1[idx])
+        yy1 = torch.max(yy1, y1[idx])
+        xx2 = torch.min(xx2, x2[idx])
+        yy2 = torch.min(yy2, y2[idx])
+
+        # find height and width of the intersection boxes
+        w = xx2 - xx1
+        h = yy2 - yy1
+
+        # take max with 0.0 to avoid negative w and h
+        # due to non-overlapping boxes
+        w = torch.clamp(w, min=0.0)
+        h = torch.clamp(h, min=0.0)
+
+        # find the intersection area
+        inter = w * h
+
+        # find the areas of BBoxes according the indices in order
+        rem_areas = torch.index_select(areas, dim=0, index=order)
+
+        # find the union of every prediction T in P
+        # with the prediction S
+        # Note that areas[idx] represents area of S
+        union = (rem_areas - inter) + areas[idx]
+
+        # find the IoU of every prediction in P with S
+        IoU = inter / union
+
+        # keep the boxes with IoU less than thresh_iou
+        mask = IoU < thresh_iou
+        order = order[mask]
+
+    return keep
+
+
+def remove_large_pred_bbx(bbx_3d):
+    """
+    Remove large bounding box.
+
+    Parameters
+    ----------
+    bbx_3d : torch.Tensor
+        Predcited 3d bounding box, shape:(N,8,3)
+
+    Returns
+    -------
+    index : torch.Tensor
+        The keep index.
+    """
+    bbx_x_max = torch.max(bbx_3d[:, :, 0], dim=1)[0]
+    bbx_x_min = torch.min(bbx_3d[:, :, 0], dim=1)[0]
+    x_len = bbx_x_max - bbx_x_min
+
+    bbx_y_max = torch.max(bbx_3d[:, :, 1], dim=1)[0]
+    bbx_y_min = torch.min(bbx_3d[:, :, 1], dim=1)[0]
+    y_len = bbx_y_max - bbx_y_min
+
+    bbx_z_max = torch.max(bbx_3d[:, :, 1], dim=1)[0]
+    bbx_z_min = torch.min(bbx_3d[:, :, 1], dim=1)[0]
+    z_len = bbx_z_max - bbx_z_min
+
+    index = torch.logical_and(x_len <= 6, y_len <= 6)
+    index = torch.logical_and(index, z_len)
+
+    return index
+
+
+def remove_bbx_abnormal_z(bbx_3d):
+    """
+    Remove bounding box that has negative z axis.
+
+    Parameters
+    ----------
+    bbx_3d : torch.Tensor
+        Predcited 3d bounding box, shape:(N,8,3)
+
+    Returns
+    -------
+    index : torch.Tensor
+        The keep index.
+    """
+    bbx_z_min = torch.min(bbx_3d[:, :, 2], dim=1)[0]
+    bbx_z_max = torch.max(bbx_3d[:, :, 2], dim=1)[0]
+    index = torch.logical_and(bbx_z_min >= -3, bbx_z_max <= 1)
+
+    return index
+
+
+def project_points_by_matrix_torch(points, transformation_matrix):
+    """
+    Project the points to another coordinate system based on the
+    transformation matrix.
+
+    Parameters
+    ----------
+    points : torch.Tensor
+        3D points, (N, 3)
+    transformation_matrix : torch.Tensor
+        Transformation matrix, (4, 4)
+    Returns
+    -------
+    projected_points : torch.Tensor
+        The projected points, (N, 3)
+    """
+    points, is_numpy = \
+        common_utils.check_numpy_to_torch(points)
+    transformation_matrix, _ = \
+        common_utils.check_numpy_to_torch(transformation_matrix)
+
+    # convert to homogeneous coordinates via padding 1 at the last dimension.
+    # (N, 4)
+    points_homogeneous = F.pad(points, (0, 1), mode="constant", value=1)
+    # (N, 4)
+    projected_points = torch.einsum("ik, jk->ij", points_homogeneous,
+                                    transformation_matrix)
+
+    return projected_points[:, :3] if not is_numpy \
+        else projected_points[:, :3].numpy()
+
+
+def box_encode(
+        boxes,
+        anchors,
+        encode_angle_to_vector=False,
+        encode_angle_with_residual=False,
+        smooth_dim=False,
+        norm_velo=False
+):
+    """box encode for VoxelNet
+        Args:
+            boxes ([N, 7] Tensor): normal boxes: x, y, z, w, l, h, r.
+            anchors ([N, 7] Tensor): anchors.
+    """
+
+    box_ndim = anchors.shape[-1]
+
+    if box_ndim == 7:
+        xa, ya, za, wa, la, ha, ra = torch.split(anchors, 1, dim=-1)
+        xg, yg, zg, wg, lg, hg, rg = torch.split(boxes, 1, dim=-1)
+    else:
+        xa, ya, za, wa, la, ha, vxa, vya, ra = torch.split(anchors, 1, dim=-1)
+        xg, yg, zg, wg, lg, hg, vxg, vyg, rg = torch.split(boxes, 1, dim=-1)
+
+    diagonal = torch.sqrt(la ** 2 + wa ** 2)
+    xt = (xg - xa) / diagonal
+    yt = (yg - ya) / diagonal
+    zt = (zg - za) / ha
+
+    if smooth_dim:
+        lt = lg / la - 1
+        wt = wg / wa - 1
+        ht = hg / ha - 1
+    else:
+        lt = torch.log(lg / la)
+        wt = torch.log(wg / wa)
+        ht = torch.log(hg / ha)
+
+    ret = [xt, yt, zt, wt, lt, ht]
+
+    if box_ndim > 7:
+        if norm_velo:
+            vxt = (vxg - vxa) / diagonal
+            vyt = (vyg - vya) / diagonal
+        else:
+            vxt = vxg - vxa
+            vyt = vyg - vya
+        ret.extend([vxt, vyt])
+
+    if encode_angle_to_vector:
+        rgx = torch.cos(rg)
+        rgy = torch.sin(rg)
+        if encode_angle_with_residual:
+            rax = torch.cos(ra)
+            ray = torch.sin(ra)
+            rtx = rgx - rax
+            rty = rgy - ray
+            ret.extend([rtx, rty])
+        else:
+            ret.extend([rgx, rgy])
+    else:
+        rt = rg - ra
+        ret.append(rt)
+
+    return torch.cat(ret, dim=-1)
+
+
+def box_decode(
+        box_encodings,
+        anchors,
+        encode_angle_to_vector=False,
+        encode_angle_with_residual=False,
+        bin_loss=False,
+        smooth_dim=False,
+        norm_velo=False,
+):
+    """box decode for VoxelNet in lidar
+    Args:
+        boxes ([N, 7] Tensor): normal boxes: x, y, z, w, l, h, r
+        anchors ([N, 7] Tensor): anchors
+    """
+    box_ndim = anchors.shape[-1]
+
+    if box_ndim == 9:  # False
+        xa, ya, za, wa, la, ha, vxa, vya, ra = torch.split(anchors, 1, dim=-1)
+        if encode_angle_to_vector:
+            xt, yt, zt, wt, lt, ht, vxt, vyt, rtx, rty = torch.split(box_encodings, 1, dim=-1)
+        else:
+            xt, yt, zt, wt, lt, ht, vxt, vyt, rt = torch.split(box_encodings, 1, dim=-1)
+
+    elif box_ndim == 7:
+        xa, ya, za, wa, la, ha, ra = torch.split(anchors, 1, dim=-1)
+        if encode_angle_to_vector:  # False
+            xt, yt, zt, wt, lt, ht, rtx, rty = torch.split(box_encodings, 1, dim=-1)
+        else:
+            xt, yt, zt, wt, lt, ht, rt = torch.split(box_encodings, 1, dim=-1)
+
+    diagonal = torch.sqrt(la ** 2 + wa ** 2)
+    xg = xt * diagonal + xa
+    yg = yt * diagonal + ya
+    zg = zt * ha + za
+
+    ret = [xg, yg, zg]
+
+    if smooth_dim:  # False
+        lg = (lt + 1) * la
+        wg = (wt + 1) * wa
+        hg = (ht + 1) * ha
+    else:
+        lg = torch.exp(lt) * la
+        wg = torch.exp(wt) * wa
+        hg = torch.exp(ht) * ha
+    ret.extend([wg, lg, hg])
+
+    if encode_angle_to_vector:  # False
+        if encode_angle_with_residual:
+            rax = torch.cos(ra)
+            ray = torch.sin(ra)
+            rgx = rtx + rax
+            rgy = rty + ray
+            rg = torch.atan2(rgy, rgx)
+        else:
+            rg = torch.atan2(rty, rtx)
+    else:
+        rg = rt + ra
+
+    if box_ndim > 7:  # False
+        if norm_velo:
+            vxg = vxt * diagonal + vxa
+            vyg = vyt * diagonal + vya
+        else:
+            vxg = vxt + vxa
+            vyg = vyt + vya
+        ret.extend([vxg, vyg])
+
+    ret.append(rg)
+
+    return torch.cat(ret, dim=-1)
\ No newline at end of file
diff --git a/opencood/opencood/utils/common_utils.py b/opencood/opencood/utils/common_utils.py
new file mode 100644
index 00000000..6929e4a7
--- /dev/null
+++ b/opencood/opencood/utils/common_utils.py
@@ -0,0 +1,202 @@
+# -*- coding: utf-8 -*-
+# Author: Runsheng Xu <rxx3386@ucla.edu>, Hao Xiang <haxiang@g.ucla.edu>,
+# License: TDG-Attribution-NonCommercial-NoDistrib
+
+
+"""
+Common utilities
+"""
+
+import numpy as np
+import torch
+from shapely.geometry import Polygon
+
+
+def check_numpy_to_torch(x):
+    if isinstance(x, np.ndarray):
+        return torch.from_numpy(x).float(), True
+    return x, False
+
+
+def check_contain_nan(x):
+    if isinstance(x, dict):
+        return any(check_contain_nan(v) for k, v in x.items())
+    if isinstance(x, list):
+        return any(check_contain_nan(itm) for itm in x)
+    if isinstance(x, int) or isinstance(x, float):
+        return False
+    if isinstance(x, np.ndarray):
+        return np.any(np.isnan(x))
+    return torch.any(x.isnan()).detach().cpu().item()
+
+
+def rotate_points_along_z(points, angle):
+    """
+    Args:
+        points: (B, N, 3 + C)
+        angle: (B), radians, angle along z-axis, angle increases x ==> y
+    Returns:
+
+    """
+    points, is_numpy = check_numpy_to_torch(points)
+    angle, _ = check_numpy_to_torch(angle)
+
+    cosa = torch.cos(angle)
+    sina = torch.sin(angle)
+    zeros = angle.new_zeros(points.shape[0])
+    ones = angle.new_ones(points.shape[0])
+    rot_matrix = torch.stack((
+        cosa, sina, zeros,
+        -sina, cosa, zeros,
+        zeros, zeros, ones
+    ), dim=1).view(-1, 3, 3).float()
+    points_rot = torch.matmul(points[:, :, 0:3].float(), rot_matrix)
+    points_rot = torch.cat((points_rot, points[:, :, 3:]), dim=-1)
+    return points_rot.numpy() if is_numpy else points_rot
+
+
+def rotate_points_along_z_2d(points, angle):
+    """
+    Rorate the points along z-axis.
+    Parameters
+    ----------
+    points : torch.Tensor / np.ndarray
+        (N, 2).
+    angle : torch.Tensor / np.ndarray
+        (N,)
+
+    Returns
+    -------
+    points_rot : torch.Tensor / np.ndarray
+        Rorated points with shape (N, 2)
+
+    """
+    points, is_numpy = check_numpy_to_torch(points)
+    angle, _ = check_numpy_to_torch(angle)
+    cosa = torch.cos(angle)
+    sina = torch.sin(angle)
+    # (N, 2, 2)
+    rot_matrix = torch.stack((cosa, sina, -sina, cosa), dim=1).view(-1, 2,
+                                                                    2).float()
+    points_rot = torch.einsum("ik, ikj->ij", points.float(), rot_matrix)
+    return points_rot.numpy() if is_numpy else points_rot
+
+
+def remove_ego_from_objects(objects, ego_id):
+    """
+    Avoid adding ego vehicle to the object dictionary.
+
+    Parameters
+    ----------
+    objects : dict
+        The dictionary contained all objects.
+
+    ego_id : int
+        Ego id.
+    """
+    if ego_id in objects:
+        del objects[ego_id]
+
+
+def retrieve_ego_id(base_data_dict):
+    """
+    Retrieve the ego vehicle id from sample(origin format).
+
+    Parameters
+    ----------
+    base_data_dict : dict
+        Data sample in origin format.
+
+    Returns
+    -------
+    ego_id : str
+        The id of ego vehicle.
+    """
+    ego_id = None
+
+    for cav_id, cav_content in base_data_dict.items():
+        if cav_content['ego']:
+            ego_id = cav_id
+            break
+    return ego_id
+
+
+def compute_iou(box, boxes):
+    """
+    Compute iou between box and boxes list
+    Parameters
+    ----------
+    box : shapely.geometry.Polygon
+        Bounding box Polygon.
+
+    boxes : list
+        List of shapely.geometry.Polygon.
+
+    Returns
+    -------
+    iou : np.ndarray
+        Array of iou between box and boxes.
+
+    """
+    # Calculate intersection areas
+    if np.any(np.array([box.union(b).area for b in boxes])==0):
+        print('debug')
+    iou = [box.intersection(b).area / box.union(b).area for b in boxes]
+
+    return np.array(iou, dtype=np.float32)
+
+
+def convert_format(boxes_array):
+    """
+    Convert boxes array to shapely.geometry.Polygon format.
+    Parameters
+    ----------
+    boxes_array : np.ndarray
+        (N, 4, 2) or (N, 8, 3).
+
+    Returns
+    -------
+        list of converted shapely.geometry.Polygon object.
+
+    """
+    polygons = [Polygon([(box[i, 0], box[i, 1]) for i in range(4)]) for box in
+                boxes_array]
+    return np.array(polygons)
+
+
+def torch_tensor_to_numpy(torch_tensor):
+    """
+    Convert a torch tensor to numpy.
+
+    Parameters
+    ----------
+    torch_tensor : torch.Tensor
+
+    Returns
+    -------
+    A numpy array.
+    """
+    return torch_tensor.numpy() if not torch_tensor.is_cuda else \
+        torch_tensor.cpu().detach().numpy()
+
+
+def get_voxel_centers(voxel_coords,
+                      downsample_times,
+                      voxel_size,
+                      point_cloud_range):
+    """
+    Args:
+        voxel_coords: (N, 3)
+        downsample_times:
+        voxel_size:
+        point_cloud_range:
+
+    Returns:
+
+    """
+    assert voxel_coords.shape[1] == 3
+    voxel_centers = voxel_coords[:, [2, 1, 0]].float()  # (xyz)
+    voxel_size = torch.tensor(voxel_size, device=voxel_centers.device).float() * downsample_times
+    pc_range = torch.tensor(point_cloud_range[0:3], device=voxel_centers.device).float()
+    voxel_centers = (voxel_centers + 0.5) * voxel_size + pc_range
+    return voxel_centers
diff --git a/opencood/opencood/utils/draco_compression.py b/opencood/opencood/utils/draco_compression.py
new file mode 100644
index 00000000..00a0fa8b
--- /dev/null
+++ b/opencood/opencood/utils/draco_compression.py
@@ -0,0 +1,69 @@
+"""
+To use this script, draco [https://github.com/google/draco.git] should be installed.
+1. during test, keypoints coordinats and features should be saved as .ply file using
+   the funcion save_ply.
+2. Compress and anylize the CPM size using function draco_compression.
+"""
+import random, os, re
+import numpy as np
+import torch
+from glob import glob
+import subprocess
+
+draco = "/media/hdd/yuan/draco/build_dir/draco_encoder"
+
+
+def save_ply(path, batch_coords, batch_features):
+    # path = "/media/hdd/yuan/OpenCOOD/opencood/logs/fpvrcnn_intermediate_fusion/cpms/"
+    dirname = "{:06d}".format(random.randint(0, 999999))
+    os.mkdir(path + dirname)
+    for bi, (coords, features) in enumerate(zip(batch_coords[1:],
+                                                batch_features[1:])):
+        header = "ply\n" \
+                 "format ascii 1.0\n" \
+                 f"element vertex {len(coords)}\n" \
+                 "property float x\n" \
+                 "property float y\n" \
+                 "property float z\n"
+        header = header + "".join([f"property float feat{i}\n" for i in range(32)]) + "end_header"
+        data = torch.cat([coords, features], dim=1).detach().cpu().numpy()
+        np.savetxt(path + dirname + f"/{bi + 1}.ply", data,
+                   delimiter=' ', header=header, comments='')
+
+
+def draco_compression(ply_path):
+    files = glob(os.path.join(ply_path, '*/*.ply'))
+    cpm_sizes = list(map(draco_compression_one, files))
+    return cpm_sizes
+
+
+def draco_compression_one(file):
+    out_file = file.replace('ply', 'drc')
+    std_out = subprocess.getoutput(f"{draco} -point_cloud -i {file} -o {out_file}")
+    size_str = re.findall('[0-9]+ bytes', std_out)
+    if len(size_str)<1:
+        print("Compression failed:", file)
+        cpm_size = 0
+    else:
+        cpm_size = int(size_str[0].split(' ')[0])
+
+    return cpm_size
+
+
+def cal_avg_num_kpts(ply_path):
+    files = glob(os.path.join(ply_path, '*/*.ply'))
+
+    def read_vertex_num(file):
+        with open(file, 'r') as f:
+            size_str = re.findall('element vertex [0-9]+', f.read())[0]
+        return float(size_str.split(' ')[-1]) * 4 * 32 / 1024
+
+    sizes = list(map(read_vertex_num, files))
+
+    return sizes
+
+
+if __name__=="__main__":
+    cpm_sizes = cal_avg_num_kpts("/media/hdd/yuan/OpenCOOD/opencood/logs/fpvrcnn_intermediate_fusion/cpms")
+    # cpm_sizes = draco_compression("/media/hdd/yuan/OpenCOOD/opencood/logs/fpvrcnn_intermediate_fusion/cpms")
+    print(np.array(cpm_sizes).mean())
\ No newline at end of file
diff --git a/opencood/opencood/utils/eval_utils.py b/opencood/opencood/utils/eval_utils.py
new file mode 100644
index 00000000..d1661525
--- /dev/null
+++ b/opencood/opencood/utils/eval_utils.py
@@ -0,0 +1,155 @@
+# -*- coding: utf-8 -*-
+# Author: Runsheng Xu <rxx3386@ucla.edu>
+# License: TDG-Attribution-NonCommercial-NoDistrib
+
+
+import os
+
+import numpy as np
+import torch
+
+from opencood.utils import common_utils
+from opencood.hypes_yaml import yaml_utils
+
+
+def voc_ap(rec, prec):
+    """
+    VOC 2010 Average Precision.
+    """
+    rec.insert(0, 0.0)
+    rec.append(1.0)
+    mrec = rec[:]
+
+    prec.insert(0, 0.0)
+    prec.append(0.0)
+    mpre = prec[:]
+
+    for i in range(len(mpre) - 2, -1, -1):
+        mpre[i] = max(mpre[i], mpre[i + 1])
+
+    i_list = []
+    for i in range(1, len(mrec)):
+        if mrec[i] != mrec[i - 1]:
+            i_list.append(i)
+
+    ap = 0.0
+    for i in i_list:
+        ap += ((mrec[i] - mrec[i - 1]) * mpre[i])
+    return ap, mrec, mpre
+
+
+def caluclate_tp_fp(det_boxes, det_score, gt_boxes, result_stat, iou_thresh):
+    """
+    Calculate the true positive and false positive numbers of the current
+    frames.
+
+    Parameters
+    ----------
+    det_boxes : torch.Tensor
+        The detection bounding box, shape (N, 8, 3) or (N, 4, 2).
+    det_score :torch.Tensor
+        The confidence score for each preditect bounding box.
+    gt_boxes : torch.Tensor
+        The groundtruth bounding box.
+    result_stat: dict
+        A dictionary contains fp, tp and gt number.
+    iou_thresh : float
+        The iou thresh.
+    """
+    # fp, tp and gt in the current frame
+    fp = []
+    tp = []
+    gt = gt_boxes.shape[0]
+    if det_boxes is not None:
+        # convert bounding boxes to numpy array
+        det_boxes = common_utils.torch_tensor_to_numpy(det_boxes)
+        det_score = common_utils.torch_tensor_to_numpy(det_score)
+        gt_boxes = common_utils.torch_tensor_to_numpy(gt_boxes)
+
+        # sort the prediction bounding box by score
+        score_order_descend = np.argsort(-det_score)
+        det_polygon_list = list(common_utils.convert_format(det_boxes))
+        gt_polygon_list = list(common_utils.convert_format(gt_boxes))
+
+        # match prediction and gt bounding box
+        for i in range(score_order_descend.shape[0]):
+            det_polygon = det_polygon_list[score_order_descend[i]]
+            ious = common_utils.compute_iou(det_polygon, gt_polygon_list)
+
+            if len(gt_polygon_list) == 0 or np.max(ious) < iou_thresh:
+                fp.append(1)
+                tp.append(0)
+                continue
+
+            fp.append(0)
+            tp.append(1)
+
+            gt_index = np.argmax(ious)
+            gt_polygon_list.pop(gt_index)
+
+    result_stat[iou_thresh]['fp'] += fp
+    result_stat[iou_thresh]['tp'] += tp
+    result_stat[iou_thresh]['gt'] += gt
+
+
+def calculate_ap(result_stat, iou):
+    """
+    Calculate the average precision and recall, and save them into a txt.
+
+    Parameters
+    ----------
+    result_stat : dict
+        A dictionary contains fp, tp and gt number.
+    iou : float
+    """
+    iou_5 = result_stat[iou]
+
+    fp = iou_5['fp']
+    tp = iou_5['tp']
+    assert len(fp) == len(tp)
+
+    gt_total = iou_5['gt']
+
+    cumsum = 0
+    for idx, val in enumerate(fp):
+        fp[idx] += cumsum
+        cumsum += val
+
+    cumsum = 0
+    for idx, val in enumerate(tp):
+        tp[idx] += cumsum
+        cumsum += val
+
+    rec = tp[:]
+    for idx, val in enumerate(tp):
+        rec[idx] = float(tp[idx]) / gt_total
+
+    prec = tp[:]
+    for idx, val in enumerate(tp):
+        prec[idx] = float(tp[idx]) / (fp[idx] + tp[idx])
+
+    ap, mrec, mprec = voc_ap(rec[:], prec[:])
+
+    return ap, mrec, mprec
+
+
+def eval_final_results(result_stat, save_path):
+    dump_dict = {}
+
+    ap_30, mrec_30, mpre_30 = calculate_ap(result_stat, 0.30)
+    ap_50, mrec_50, mpre_50 = calculate_ap(result_stat, 0.50)
+    ap_70, mrec_70, mpre_70 = calculate_ap(result_stat, 0.70)
+
+    dump_dict.update({'ap30': ap_30,
+                      'ap_50': ap_50,
+                      'ap_70': ap_70,
+                      'mpre_50': mpre_50,
+                      'mrec_50': mrec_50,
+                      'mpre_70': mpre_70,
+                      'mrec_70': mrec_70,
+                      })
+    yaml_utils.save_yaml(dump_dict, os.path.join(save_path, 'eval.yaml'))
+
+    print('The Average Precision at IOU 0.3 is %.2f, '
+          'The Average Precision at IOU 0.5 is %.2f, '
+          'The Average Precision at IOU 0.7 is %.2f' % (ap_30, ap_50, ap_70))
diff --git a/opencood/opencood/utils/max_consensus.py b/opencood/opencood/utils/max_consensus.py
new file mode 100644
index 00000000..294afda5
--- /dev/null
+++ b/opencood/opencood/utils/max_consensus.py
@@ -0,0 +1,131 @@
+import numpy as np
+from sklearn.neighbors import NearestNeighbors
+
+
+def max_consunsus_hierarchical(pointsl, pointsr, loc_l, loc_r, resolution=None, radius=1, point_labels=None, label_weights=None, **kwargs):
+    max_err = kwargs['search_range']# np.array([1, 1, 6])
+    min_cons = kwargs['min_cons']
+    min_match_acc_points = kwargs['min_match_acc_points']
+    pointsl_out, pointsr_out, T, tf_local, cons, matched_pointsl, matched_pointsr = max_consensus2(pointsl, pointsr, -max_err, max_err,
+                                                                           resolution, radius, loc_l, loc_r,
+                                                                          point_labels=point_labels, label_weights=label_weights)
+
+    if matched_pointsl is not None and len(matched_pointsl) > min_match_acc_points:
+        T, tf = estimate_tf_2d(matched_pointsl, matched_pointsr, pointsl, pointsr_out)
+        tf_local = tf
+        tf_local[:2] = tf_local[:2] = tf_local[:2] - loc_r[0, :2] + loc_l[0, :2]
+        pointsr_homo = np.concatenate([pointsr, np.ones((len(pointsr), 1))], axis=1).T
+        pointsr_out = (T @ pointsr_homo).T
+    else:
+        return None, None, None
+
+    if cons < min_cons:
+        return None, None, None
+    return T, tf_local, pointsr_out
+
+
+def max_consensus2(pointsl, pointsr, xyr_min, xyr_max, resolotion, radius, loc_l=None, loc_r=None, point_labels=None, label_weights=None):
+    tf_matrices, tf_params, tf_params_local = construct_tfs(xyr_min, xyr_max, resolotion, loc_l, loc_r)
+    rotl, _, _ = construct_tfs(xyr_min[2:], xyr_max[2:], resolotion[2:])
+    pointr_homo = np.concatenate([pointsr, np.ones((len(pointsr), 1))], axis=1).T
+    # pointl_homo = np.concatenate([pointsl, np.ones((len(pointsl), 1))], axis=1).T
+    pointr_transformed = np.einsum('...ij, ...jk', tf_matrices, np.tile(pointr_homo,(len(tf_matrices), 1, 1))).transpose(0, 2, 1)
+    pointr_transformed_s = pointr_transformed.reshape(-1, 3)[:, :2]
+    cur_cons = 0
+    pointl_out = pointsl
+    pointr_out = pointsr
+    match_T, match_tf_local, matched_pointsl, matched_pointsr = None, None, None, None
+    # r1 = 0
+    for R in rotl[:, :2, :2]:
+        pointl_transformed = np.einsum('ij, jk', R, pointsl.T).T
+        nbrs = NearestNeighbors(n_neighbors=1, radius=radius, algorithm='auto').fit(pointl_transformed)
+        distances, indices = nbrs.kneighbors(pointr_transformed_s)
+        mask = (distances < radius)
+        lbll, lblr = point_labels
+        plus = (np.logical_and(lbll[indices] > 2, mask)).reshape(len(tf_matrices), len(pointsr))
+        mask = mask.reshape(len(tf_matrices), len(pointsr))
+        pointr_consensus = mask.sum(axis=1) + plus.sum(axis=1) * label_weights[-1]
+        best_match = np.argmax(pointr_consensus)
+        match_consensus = pointr_consensus[best_match]
+        if match_consensus > cur_cons:
+            pointr_out = pointr_transformed[best_match]
+            match_T = tf_matrices[best_match]
+            match_tf_local = tf_params_local[best_match]
+            accurate_points_mask = plus[best_match]
+            selected_indices = indices.reshape(len(tf_matrices), len(pointsr))[best_match][accurate_points_mask]
+            matched_pointsl = pointsl[selected_indices]
+            matched_pointsr = pointsr[accurate_points_mask]
+            # r1 = np.arctan2(R[1, 0], R[0, 0])
+            pointl_out = pointl_transformed
+            cur_cons = match_consensus
+    return pointl_out, pointr_out, match_T, match_tf_local, cur_cons, matched_pointsl, matched_pointsr
+
+
+def max_consensus1(pointsl, pointsr, xyr_min, xyr_max, resolotion, radius, loc_l=None, loc_r=None, point_labels=None, label_weights=None):
+    tf_matrices, tf_params, tf_params_local = construct_tfs(xyr_min, xyr_max, resolotion, loc_l, loc_r)
+    pointr_homo = np.concatenate([pointsr, np.ones((len(pointsr), 1))], axis=1).T
+    pointr_transformed = np.einsum('...ij, ...jk', tf_matrices, np.tile(pointr_homo,(len(tf_matrices), 1, 1))).transpose(0, 2, 1)
+    pointr_transformed_s = pointr_transformed.reshape(-1, 3)[:, :2]
+
+    nbrs = NearestNeighbors(n_neighbors=1, radius=radius, algorithm='auto').fit(pointsl)
+    distances, indices = nbrs.kneighbors(pointr_transformed_s)
+    mask = (distances < radius)
+    lbll, lblr = point_labels
+    plus = (np.logical_and(lbll[indices] > 2, mask)).reshape(len(tf_matrices), len(pointsr))
+    mask = mask.reshape(len(tf_matrices), len(pointsr))
+    pointr_consensus = mask.sum(axis=1) + plus.sum(axis=1) * label_weights[-1]
+    best_match = np.argmax(pointr_consensus)
+    match_consensus = pointr_consensus[best_match]
+    pointr_out = pointr_transformed[best_match]
+    match_tf = tf_params[best_match]
+    match_T = tf_matrices[best_match]
+    match_tf_local = tf_params_local[best_match]
+    accurate_points_mask = plus[best_match]
+    selected_indices = indices.reshape(len(tf_matrices), len(pointsr))[best_match][accurate_points_mask]
+    matched_pointsl = pointsl[selected_indices]
+    matched_pointsr = pointsr[accurate_points_mask]
+    return pointr_out, match_T, match_tf_local, match_consensus, matched_pointsl, matched_pointsr
+
+
+def construct_tfs(xyr_min, xyr_max, resolution, loc_l=None, loc_r=None):
+    input = [np.arange(xyr_min[i], xyr_max[i], resolution[i]) for i in range(len(xyr_min))]
+    grid = np.meshgrid(*input)
+    grid = [a.reshape(-1) for a in grid]
+    tf_parames_local = np.stack(grid, axis=1)
+    tf_parames_local[:, -1] = tf_parames_local[:, -1] / 180 * np.pi
+    tf_parames = np.copy(tf_parames_local)
+    if loc_r is not None:
+        tf_parames[:, :-1] = tf_parames_local[:, :2] + loc_r[:, :2] - loc_l[:, :2]
+    sina = np.sin(tf_parames[:, -1])
+    cosa = np.cos(tf_parames[:, -1])
+    zeros = np.zeros(len(tf_parames), dtype=sina.dtype)
+    ones = np.ones(len(tf_parames), dtype=sina.dtype)
+    x = tf_parames[:, 0] if len(xyr_min)>1 else zeros
+    y = tf_parames[:, 1] if len(xyr_min)>1 else zeros
+    tfs = np.array([[cosa, -sina, x],
+                    [sina, cosa, y],
+                    [zeros, zeros, ones]]).transpose(2, 0, 1)
+    return tfs, tf_parames, tf_parames_local
+
+
+def estimate_tf_2d(pointsr, pointsl, pointsl_all, pointsr_all):
+    # 1 reduce by the center of mass
+    l_mean = pointsl.mean(axis=0)
+    r_mean = pointsr.mean(axis=0)
+    l_reduced = pointsl - l_mean
+    r_reduced = pointsr - r_mean
+    # 2 compute the rotation
+    Sxx = (l_reduced[:, 0] * r_reduced[:, 0]).sum()
+    Syy = (l_reduced[:, 1] * r_reduced[:, 1]).sum()
+    Sxy = (l_reduced[:, 0] * r_reduced[:, 1]).sum()
+    Syx = (l_reduced[:, 1] * r_reduced[:, 0]).sum()
+    theta = np.arctan2(Sxy - Syx, Sxx + Syy)  # / np.pi * 180
+    sa = np.sin(theta)
+    ca = np.cos(theta)
+    T = np.array([[ca, -sa, 0],
+                  [sa, ca, 0],
+                  [0, 0, 1]])
+    t = r_mean.reshape(2, 1) - T[:2, :2] @ l_mean.reshape(2, 1)
+    # T = T.T
+    T[:2, 2:] = t
+    return T, np.array([*t.squeeze(), theta])
diff --git a/opencood/opencood/utils/pcd_utils.py b/opencood/opencood/utils/pcd_utils.py
new file mode 100644
index 00000000..875762f9
--- /dev/null
+++ b/opencood/opencood/utils/pcd_utils.py
@@ -0,0 +1,203 @@
+# -*- coding: utf-8 -*-
+# Author: Runsheng Xu <rxx3386@ucla.edu>, Hao Xiang <haxiang@g.ucla.edu>,
+# License: TDG-Attribution-NonCommercial-NoDistrib
+
+
+"""
+Utility functions related to point cloud
+"""
+
+import open3d as o3d
+import numpy as np
+
+
+def pcd_to_np(pcd_file):
+    """
+    Read  pcd and return numpy array.
+
+    Parameters
+    ----------
+    pcd_file : str
+        The pcd file that contains the point cloud.
+
+    Returns
+    -------
+    pcd : o3d.PointCloud
+        PointCloud object, used for visualization
+    pcd_np : np.ndarray
+        The lidar data in numpy format, shape:(n, 4)
+
+    """
+    pcd = o3d.io.read_point_cloud(pcd_file)
+
+    xyz = np.asarray(pcd.points)
+    # we save the intensity in the first channel
+    intensity = np.expand_dims(np.asarray(pcd.colors)[:, 0], -1)
+    pcd_np = np.hstack((xyz, intensity))
+
+    return np.asarray(pcd_np, dtype=np.float32)
+
+
+def mask_points_by_range(points, limit_range):
+    """
+    Remove the lidar points out of the boundary.
+
+    Parameters
+    ----------
+    points : np.ndarray
+        Lidar points under lidar sensor coordinate system.
+
+    limit_range : list
+        [x_min, y_min, z_min, x_max, y_max, z_max]
+
+    Returns
+    -------
+    points : np.ndarray
+        Filtered lidar points.
+    """
+
+    mask = (points[:, 0] > limit_range[0]) & (points[:, 0] < limit_range[3])\
+           & (points[:, 1] > limit_range[1]) & (
+                   points[:, 1] < limit_range[4]) \
+           & (points[:, 2] > limit_range[2]) & (
+                   points[:, 2] < limit_range[5])
+
+    points = points[mask]
+
+    return points
+
+
+def mask_ego_points(points):
+    """
+    Remove the lidar points of the ego vehicle itself.
+
+    Parameters
+    ----------
+    points : np.ndarray
+        Lidar points under lidar sensor coordinate system.
+
+    Returns
+    -------
+    points : np.ndarray
+        Filtered lidar points.
+    """
+    mask = (points[:, 0] >= -1.95) & (points[:, 0] <= 2.95) \
+           & (points[:, 1] >= -1.1) & (points[:, 1] <= 1.1)
+    points = points[np.logical_not(mask)]
+
+    return points
+
+
+def shuffle_points(points):
+    shuffle_idx = np.random.permutation(points.shape[0])
+    points = points[shuffle_idx]
+
+    return points
+
+
+def lidar_project(lidar_data, extrinsic):
+    """
+    Given the extrinsic matrix, project lidar data to another space.
+
+    Parameters
+    ----------
+    lidar_data : np.ndarray
+        Lidar data, shape: (n, 4)
+
+    extrinsic : np.ndarray
+        Extrinsic matrix, shape: (4, 4)
+
+    Returns
+    -------
+    projected_lidar : np.ndarray
+        Projected lida data, shape: (n, 4)
+    """
+
+    lidar_xyz = lidar_data[:, :3].T
+    # (3, n) -> (4, n), homogeneous transformation
+    lidar_xyz = np.r_[lidar_xyz, [np.ones(lidar_xyz.shape[1])]]
+    lidar_int = lidar_data[:, 3]
+
+    # transform to ego vehicle space, (3, n)
+    project_lidar_xyz = np.dot(extrinsic, lidar_xyz)[:3, :]
+    # (n, 3)
+    project_lidar_xyz = project_lidar_xyz.T
+    # concatenate the intensity with xyz, (n, 4)
+    projected_lidar = np.hstack((project_lidar_xyz,
+                                 np.expand_dims(lidar_int, -1)))
+
+    return projected_lidar
+
+
+def projected_lidar_stack(projected_lidar_list):
+    """
+    Stack all projected lidar together.
+
+    Parameters
+    ----------
+    projected_lidar_list : list
+        The list containing all projected lidar.
+
+    Returns
+    -------
+    stack_lidar : np.ndarray
+        Stack all projected lidar data together.
+    """
+    stack_lidar = []
+    for lidar_data in projected_lidar_list:
+        stack_lidar.append(lidar_data)
+
+    return np.vstack(stack_lidar)
+
+
+def downsample_lidar(pcd_np, num):
+    """
+    Downsample the lidar points to a certain number.
+
+    Parameters
+    ----------
+    pcd_np : np.ndarray
+        The lidar points, (n, 4).
+
+    num : int
+        The downsample target number.
+
+    Returns
+    -------
+    pcd_np : np.ndarray
+        The downsampled lidar points.
+    """
+    assert pcd_np.shape[0] >= num
+
+    selected_index = np.random.choice((pcd_np.shape[0]),
+                                      num,
+                                      replace=False)
+    pcd_np = pcd_np[selected_index]
+
+    return pcd_np
+
+
+def downsample_lidar_minimum(pcd_np_list):
+    """
+    Given a list of pcd, find the minimum number and downsample all
+    point clouds to the minimum number.
+
+    Parameters
+    ----------
+    pcd_np_list : list
+        A list of pcd numpy array(n, 4).
+    Returns
+    -------
+    pcd_np_list : list
+        Downsampled point clouds.
+    """
+    minimum = np.Inf
+
+    for i in range(len(pcd_np_list)):
+        num = pcd_np_list[i].shape[0]
+        minimum = num if minimum > num else minimum
+
+    for (i, pcd_np) in enumerate(pcd_np_list):
+        pcd_np_list[i] = downsample_lidar(pcd_np, minimum)
+
+    return pcd_np_list
diff --git a/opencood/opencood/utils/setup.py b/opencood/opencood/utils/setup.py
new file mode 100644
index 00000000..09eda12a
--- /dev/null
+++ b/opencood/opencood/utils/setup.py
@@ -0,0 +1,8 @@
+from distutils.core import setup
+from Cython.Build import cythonize
+import numpy
+setup(
+    name='box overlaps',
+    ext_modules=cythonize('opencood/utils/box_overlaps.pyx'),
+    include_dirs=[numpy.get_include()]
+)
\ No newline at end of file
diff --git a/opencood/opencood/utils/transformation_utils.py b/opencood/opencood/utils/transformation_utils.py
new file mode 100644
index 00000000..a3b8b7cf
--- /dev/null
+++ b/opencood/opencood/utils/transformation_utils.py
@@ -0,0 +1,108 @@
+# -*- coding: utf-8 -*-
+# Author: Runsheng Xu <rxx3386@ucla.edu>, Hao Xiang <haxiang@g.ucla.edu>,
+# License: TDG-Attribution-NonCommercial-NoDistrib
+
+
+"""
+Transformation utils
+"""
+
+import numpy as np
+
+
+def x_to_world(pose):
+    """
+    The transformation matrix from x-coordinate system to carla world system
+
+    Parameters
+    ----------
+    pose : list
+        [x, y, z, roll, yaw, pitch]
+
+    Returns
+    -------
+    matrix : np.ndarray
+        The transformation matrix.
+    """
+    x, y, z, roll, yaw, pitch = pose[:]
+
+    # used for rotation matrix
+    c_y = np.cos(np.radians(yaw))
+    s_y = np.sin(np.radians(yaw))
+    c_r = np.cos(np.radians(roll))
+    s_r = np.sin(np.radians(roll))
+    c_p = np.cos(np.radians(pitch))
+    s_p = np.sin(np.radians(pitch))
+
+    matrix = np.identity(4)
+    # translation matrix
+    matrix[0, 3] = x
+    matrix[1, 3] = y
+    matrix[2, 3] = z
+
+    # rotation matrix
+    matrix[0, 0] = c_p * c_y
+    matrix[0, 1] = c_y * s_p * s_r - s_y * c_r
+    matrix[0, 2] = -c_y * s_p * c_r - s_y * s_r
+    matrix[1, 0] = s_y * c_p
+    matrix[1, 1] = s_y * s_p * s_r + c_y * c_r
+    matrix[1, 2] = -s_y * s_p * c_r + c_y * s_r
+    matrix[2, 0] = s_p
+    matrix[2, 1] = -c_p * s_r
+    matrix[2, 2] = c_p * c_r
+
+    return matrix
+
+
+def x1_to_x2(x1, x2):
+    """
+    Transformation matrix from x1 to x2.
+
+    Parameters
+    ----------
+    x1 : list
+        The pose of x1 under world coordinates.
+    x2 : list
+        The pose of x2 under world coordinates.
+
+    Returns
+    -------
+    transformation_matrix : np.ndarray
+        The transformation matrix.
+
+    """
+    x1_to_world = x_to_world(x1)
+    x2_to_world = x_to_world(x2)
+    world_to_x2 = np.linalg.inv(x2_to_world)
+
+    transformation_matrix = np.dot(world_to_x2, x1_to_world)
+    return transformation_matrix
+
+
+def dist_to_continuous(p_dist, displacement_dist, res, downsample_rate):
+    """
+    Convert points discretized format to continuous space for BEV representation.
+    Parameters
+    ----------
+    p_dist : numpy.array
+        Points in discretized coorindates.
+
+    displacement_dist : numpy.array
+        Discretized coordinates of bottom left origin.
+
+    res : float
+        Discretization resolution.
+
+    downsample_rate : int
+        Dowmsamping rate.
+
+    Returns
+    -------
+    p_continuous : numpy.array
+        Points in continuous coorindates.
+
+    """
+    p_dist = np.copy(p_dist)
+    p_dist = p_dist + displacement_dist
+    p_continuous = p_dist * res * downsample_rate
+    return p_continuous
diff --git a/opencood/opencood/utils/visulizor.py b/opencood/opencood/utils/visulizor.py
new file mode 100644
index 00000000..1dfa0410
--- /dev/null
+++ b/opencood/opencood/utils/visulizor.py
@@ -0,0 +1,67 @@
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+def draw_box_plt(boxes_dec, ax, color=None, linewidth_scale=1.0):
+    """
+    draw boxes in a given plt ax
+    :param boxes_dec: (N, 5) or (N, 7) in metric
+    :param ax:
+    :return: ax with drawn boxes
+    """
+    if not len(boxes_dec)>0:
+        return ax
+    boxes_np= boxes_dec
+    if not isinstance(boxes_np, np.ndarray):
+        boxes_np = boxes_np.cpu().detach().numpy()
+    if boxes_np.shape[-1]>5:
+        boxes_np = boxes_np[:, [0, 1, 3, 4, 6]]
+    x = boxes_np[:, 0]
+    y = boxes_np[:, 1]
+    dx = boxes_np[:, 2]
+    dy = boxes_np[:, 3]
+
+    x1 = x - dx / 2
+    y1 = y - dy / 2
+    x2 = x + dx / 2
+    y2 = y + dy / 2
+    theta = boxes_np[:, 4:5]
+    # bl, fl, fr, br
+    corners = np.array([[x1, y1],[x1,y2], [x2,y2], [x2, y1]]).transpose(2, 0, 1)
+    new_x = (corners[:, :, 0] - x[:, None]) * np.cos(theta) + (corners[:, :, 1]
+              - y[:, None]) * (-np.sin(theta)) + x[:, None]
+    new_y = (corners[:, :, 0] - x[:, None]) * np.sin(theta) + (corners[:, :, 1]
+              - y[:, None]) * (np.cos(theta)) + y[:, None]
+    corners = np.stack([new_x, new_y], axis=2)
+    for corner in corners:
+        ax.plot(corner[[0,1,2,3,0], 0], corner[[0,1,2,3,0], 1], color=color, linewidth=0.5*linewidth_scale)
+        # draw front line (
+        ax.plot(corner[[2, 3], 0], corner[[2, 3], 1], color=color, linewidth=2*linewidth_scale)
+    return ax
+
+
+def draw_points_pred_gt_boxes_plt_2d(pc_range, points=None, boxes_pred=None, boxes_gt=None):
+    ax = plt.figure(figsize=(14, 4)).add_subplot(1, 1, 1)
+    ax.set_aspect('equal', 'box')
+    ax.set(xlim=(pc_range[0], pc_range[3]),
+           ylim=(pc_range[1], pc_range[4]))
+    if points is not None:
+        ax.plot(points[:, 0], points[:, 1], 'y.', markersize=0.3)
+    if (boxes_gt is not None) and len(boxes_gt)>0:
+        ax = draw_box_plt(boxes_gt, ax, color='green')
+    if (boxes_pred is not None) and len(boxes_pred)>0:
+        ax = draw_box_plt(boxes_pred, ax, color='red')
+    plt.xlabel('x')
+    plt.ylabel('y')
+
+    plt.show()
+    plt.close()
+
+
+def draw_points_boxes_plt_2d(ax, pc_range, points=None, boxes=None, color=None):
+    if points is not None:
+        ax.plot(points[:, 0], points[:, 1], '.', markersize=0.3, color=color)
+    if (boxes is not None) and len(boxes)>0:
+        ax = draw_box_plt(boxes, ax, color=color)
+
+    return ax
\ No newline at end of file
diff --git a/opencood/opencood/version.py b/opencood/opencood/version.py
new file mode 100644
index 00000000..e5aa9b5e
--- /dev/null
+++ b/opencood/opencood/version.py
@@ -0,0 +1,3 @@
+"""Specifies the current version number of OpenCOOD."""
+
+__version__ = "0.1.0"
diff --git a/opencood/opencood/visualization/__init__.py b/opencood/opencood/visualization/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/opencood/opencood/visualization/vis_data_sequence.py b/opencood/opencood/visualization/vis_data_sequence.py
new file mode 100644
index 00000000..a29a8bbc
--- /dev/null
+++ b/opencood/opencood/visualization/vis_data_sequence.py
@@ -0,0 +1,40 @@
+# -*- coding: utf-8 -*-
+# Author: Runsheng Xu <rxx3386@ucla.edu>
+# License: TDG-Attribution-NonCommercial-NoDistrib
+
+
+import os
+import argparse
+from torch.utils.data import DataLoader
+
+from opencood.hypes_yaml.yaml_utils import load_yaml
+from opencood.visualization import vis_utils
+from opencood.data_utils.datasets.early_fusion_vis_dataset import \
+    EarlyFusionVisDataset
+
+
+def vis_parser():
+    parser = argparse.ArgumentParser(description="data visualization")
+    parser.add_argument('--color_mode', type=str, default="intensity",
+                        help='lidar color rendering mode, e.g. intensity,'
+                             'z-value or constant.')
+    opt = parser.parse_args()
+    return opt
+
+
+if __name__ == '__main__':
+    current_path = os.path.dirname(os.path.realpath(__file__))
+    params = load_yaml(os.path.join(current_path,
+                                    '../hypes_yaml/visualization.yaml'))
+
+    opencda_dataset = EarlyFusionVisDataset(params, visualize=True,
+                                            train=False)
+    data_loader = DataLoader(opencda_dataset, batch_size=1, num_workers=8,
+                             collate_fn=opencda_dataset.collate_batch_train,
+                             shuffle=False,
+                             pin_memory=False)
+
+    opt = vis_parser()
+    vis_utils.visualize_sequence_dataloader(data_loader,
+                                            params['postprocess']['order'],
+                                            color_mode=opt.color_mode)
diff --git a/opencood/opencood/visualization/vis_utils.py b/opencood/opencood/visualization/vis_utils.py
new file mode 100644
index 00000000..8a93e411
--- /dev/null
+++ b/opencood/opencood/visualization/vis_utils.py
@@ -0,0 +1,672 @@
+# -*- coding: utf-8 -*-
+# Author: Runsheng Xu <rxx3386@ucla.edu>, Hao Xiang <haxiang@g.ucla.edu>,
+# License: TDG-Attribution-NonCommercial-NoDistrib
+
+import time
+
+import cv2
+import numpy as np
+import open3d as o3d
+import matplotlib
+import matplotlib.pyplot as plt
+
+from matplotlib import cm
+
+from opencood.utils import box_utils
+from opencood.utils import common_utils
+
+VIRIDIS = np.array(cm.get_cmap('plasma').colors)
+VID_RANGE = np.linspace(0.0, 1.0, VIRIDIS.shape[0])
+
+
+def bbx2linset(bbx_corner, order='hwl', color=(0, 1, 0)):
+    """
+    Convert the torch tensor bounding box to o3d lineset for visualization.
+
+    Parameters
+    ----------
+    bbx_corner : torch.Tensor
+        shape: (n, 8, 3).
+
+    order : str
+        The order of the bounding box if shape is (n, 7)
+
+    color : tuple
+        The bounding box color.
+
+    Returns
+    -------
+    line_set : list
+        The list containing linsets.
+    """
+    if not isinstance(bbx_corner, np.ndarray):
+        bbx_corner = common_utils.torch_tensor_to_numpy(bbx_corner)
+
+    if len(bbx_corner.shape) == 2:
+        bbx_corner = box_utils.boxes_to_corners_3d(bbx_corner,
+                                                   order)
+
+    # Our lines span from points 0 to 1, 1 to 2, 2 to 3, etc...
+    lines = [[0, 1], [1, 2], [2, 3], [0, 3],
+             [4, 5], [5, 6], [6, 7], [4, 7],
+             [0, 4], [1, 5], [2, 6], [3, 7]]
+
+    # Use the same color for all lines
+    colors = [list(color) for _ in range(len(lines))]
+    bbx_linset = []
+
+    for i in range(bbx_corner.shape[0]):
+        bbx = bbx_corner[i]
+        # o3d use right-hand coordinate
+        bbx[:, :1] = - bbx[:, :1]
+
+        line_set = o3d.geometry.LineSet()
+        line_set.points = o3d.utility.Vector3dVector(bbx)
+        line_set.lines = o3d.utility.Vector2iVector(lines)
+        line_set.colors = o3d.utility.Vector3dVector(colors)
+        bbx_linset.append(line_set)
+
+    return bbx_linset
+
+
+def bbx2oabb(bbx_corner, order='hwl', color=(0, 0, 1)):
+    """
+    Convert the torch tensor bounding box to o3d oabb for visualization.
+
+    Parameters
+    ----------
+    bbx_corner : torch.Tensor
+        shape: (n, 8, 3).
+
+    order : str
+        The order of the bounding box if shape is (n, 7)
+
+    color : tuple
+        The bounding box color.
+
+    Returns
+    -------
+    oabbs : list
+        The list containing all oriented bounding boxes.
+    """
+    if not isinstance(bbx_corner, np.ndarray):
+        bbx_corner = common_utils.torch_tensor_to_numpy(bbx_corner)
+
+    if len(bbx_corner.shape) == 2:
+        bbx_corner = box_utils.boxes_to_corners_3d(bbx_corner,
+                                                   order)
+    oabbs = []
+
+    for i in range(bbx_corner.shape[0]):
+        bbx = bbx_corner[i]
+        # o3d use right-hand coordinate
+        bbx[:, :1] = - bbx[:, :1]
+
+        tmp_pcd = o3d.geometry.PointCloud()
+        tmp_pcd.points = o3d.utility.Vector3dVector(bbx)
+
+        oabb = tmp_pcd.get_oriented_bounding_box()
+        oabb.color = color
+        oabbs.append(oabb)
+
+    return oabbs
+
+
+def bbx2aabb(bbx_center, order):
+    """
+    Convert the torch tensor bounding box to o3d aabb for visualization.
+
+    Parameters
+    ----------
+    bbx_center : torch.Tensor
+        shape: (n, 7).
+
+    order: str
+        hwl or lwh.
+
+    Returns
+    -------
+    aabbs : list
+        The list containing all o3d.aabb
+    """
+    if not isinstance(bbx_center, np.ndarray):
+        bbx_center = common_utils.torch_tensor_to_numpy(bbx_center)
+    bbx_corner = box_utils.boxes_to_corners_3d(bbx_center, order)
+
+    aabbs = []
+
+    for i in range(bbx_corner.shape[0]):
+        bbx = bbx_corner[i]
+        # o3d use right-hand coordinate
+        bbx[:, :1] = - bbx[:, :1]
+
+        tmp_pcd = o3d.geometry.PointCloud()
+        tmp_pcd.points = o3d.utility.Vector3dVector(bbx)
+
+        aabb = tmp_pcd.get_axis_aligned_bounding_box()
+        aabb.color = (0, 0, 1)
+        aabbs.append(aabb)
+
+    return aabbs
+
+
+def linset_assign_list(vis,
+                       lineset_list1,
+                       lineset_list2,
+                       update_mode='update'):
+    """
+    Associate two lists of lineset.
+
+    Parameters
+    ----------
+    vis : open3d.Visualizer
+    lineset_list1 : list
+    lineset_list2 : list
+    update_mode : str
+        Add or update the geometry.
+    """
+    for j in range(len(lineset_list1)):
+        index = j if j < len(lineset_list2) else -1
+        lineset_list1[j] = \
+            lineset_assign(lineset_list1[j],
+                                     lineset_list2[index])
+        if update_mode == 'add':
+            vis.add_geometry(lineset_list1[j])
+        else:
+            vis.update_geometry(lineset_list1[j])
+
+
+def lineset_assign(lineset1, lineset2):
+    """
+    Assign the attributes of lineset2 to lineset1.
+
+    Parameters
+    ----------
+    lineset1 : open3d.LineSet
+    lineset2 : open3d.LineSet
+
+    Returns
+    -------
+    The lineset1 object with 2's attributes.
+    """
+
+    lineset1.points = lineset2.points
+    lineset1.lines = lineset2.lines
+    lineset1.colors = lineset2.colors
+
+    return lineset1
+
+
+def color_encoding(intensity, mode='intensity'):
+    """
+    Encode the single-channel intensity to 3 channels rgb color.
+
+    Parameters
+    ----------
+    intensity : np.ndarray
+        Lidar intensity, shape (n,)
+
+    mode : str
+        The color rendering mode. intensity, z-value and constant are
+        supported.
+
+    Returns
+    -------
+    color : np.ndarray
+        Encoded Lidar color, shape (n, 3)
+    """
+    assert mode in ['intensity', 'z-value', 'constant']
+
+    if mode == 'intensity':
+        intensity_col = 1.0 - np.log(intensity) / np.log(np.exp(-0.004 * 100))
+        int_color = np.c_[
+            np.interp(intensity_col, VID_RANGE, VIRIDIS[:, 0]),
+            np.interp(intensity_col, VID_RANGE, VIRIDIS[:, 1]),
+            np.interp(intensity_col, VID_RANGE, VIRIDIS[:, 2])]
+
+    elif mode == 'z-value':
+        min_value = -1.5
+        max_value = 0.5
+        norm = matplotlib.colors.Normalize(vmin=min_value, vmax=max_value)
+        cmap = cm.jet
+        m = cm.ScalarMappable(norm=norm, cmap=cmap)
+
+        colors = m.to_rgba(intensity)
+        colors[:, [2, 1, 0, 3]] = colors[:, [0, 1, 2, 3]]
+        colors[:, 3] = 0.5
+        int_color = colors[:, :3]
+
+    elif mode == 'constant':
+        # regard all point cloud the same color
+        int_color = np.ones((intensity.shape[0], 3))
+        int_color[:, 0] *= 247 / 255
+        int_color[:, 1] *= 244 / 255
+        int_color[:, 2] *= 237 / 255
+
+    return int_color
+
+
+def visualize_single_sample_output_gt(pred_tensor,
+                                      gt_tensor,
+                                      pcd,
+                                      show_vis=True,
+                                      save_path='',
+                                      mode='constant'):
+    """
+    Visualize the prediction, groundtruth with point cloud together.
+
+    Parameters
+    ----------
+    pred_tensor : torch.Tensor
+        (N, 8, 3) prediction.
+
+    gt_tensor : torch.Tensor
+        (N, 8, 3) groundtruth bbx
+
+    pcd : torch.Tensor
+        PointCloud, (N, 4).
+
+    show_vis : bool
+        Whether to show visualization.
+
+    save_path : str
+        Save the visualization results to given path.
+
+    mode : str
+        Color rendering mode.
+    """
+
+    def custom_draw_geometry(pcd, pred, gt):
+        vis = o3d.visualization.Visualizer()
+        vis.create_window()
+
+        opt = vis.get_render_option()
+        opt.background_color = np.asarray([0, 0, 0])
+        opt.point_size = 1.0
+
+        vis.add_geometry(pcd)
+        for ele in pred:
+            vis.add_geometry(ele)
+        for ele in gt:
+            vis.add_geometry(ele)
+
+        vis.run()
+        vis.destroy_window()
+
+    if len(pcd.shape) == 3:
+        pcd = pcd[0]
+    origin_lidar = pcd
+    if not isinstance(pcd, np.ndarray):
+        origin_lidar = common_utils.torch_tensor_to_numpy(pcd)
+
+    origin_lidar_intcolor = \
+        color_encoding(origin_lidar[:, -1] if mode == 'intensity'
+                       else origin_lidar[:, 2], mode=mode)
+    # left -> right hand
+    origin_lidar[:, :1] = -origin_lidar[:, :1]
+
+    o3d_pcd = o3d.geometry.PointCloud()
+    o3d_pcd.points = o3d.utility.Vector3dVector(origin_lidar[:, :3])
+    o3d_pcd.colors = o3d.utility.Vector3dVector(origin_lidar_intcolor)
+
+    oabbs_pred = bbx2oabb(pred_tensor, color=(1, 0, 0))
+    oabbs_gt = bbx2oabb(gt_tensor, color=(0, 1, 0))
+
+    visualize_elements = [o3d_pcd] + oabbs_pred + oabbs_gt
+    if show_vis:
+        custom_draw_geometry(o3d_pcd, oabbs_pred, oabbs_gt)
+    if save_path:
+        save_o3d_visualization(visualize_elements, save_path)
+
+
+def visualize_single_sample_output_bev(pred_box, gt_box, pcd, dataset,
+                                       show_vis=True,
+                                       save_path=''):
+    """
+    Visualize the prediction, groundtruth with point cloud together in
+    a bev format.
+
+    Parameters
+    ----------
+    pred_box : torch.Tensor
+        (N, 4, 2) prediction.
+
+    gt_box : torch.Tensor
+        (N, 4, 2) groundtruth bbx
+
+    pcd : torch.Tensor
+        PointCloud, (N, 4).
+
+    show_vis : bool
+        Whether to show visualization.
+
+    save_path : str
+        Save the visualization results to given path.
+    """
+
+    if not isinstance(pcd, np.ndarray):
+        pcd = common_utils.torch_tensor_to_numpy(pcd)
+    if pred_box is not None and not isinstance(pred_box, np.ndarray):
+        pred_box = common_utils.torch_tensor_to_numpy(pred_box)
+    if gt_box is not None and not isinstance(gt_box, np.ndarray):
+        gt_box = common_utils.torch_tensor_to_numpy(gt_box)
+
+    ratio = dataset.params["preprocess"]["args"]["res"]
+    L1, W1, H1, L2, W2, H2 = dataset.params["preprocess"]["cav_lidar_range"]
+    bev_origin = np.array([L1, W1]).reshape(1, -1)
+    # (img_row, img_col)
+    bev_map = dataset.project_points_to_bev_map(pcd, ratio)
+    # (img_row, img_col, 3)
+    bev_map = \
+        np.repeat(bev_map[:, :, np.newaxis], 3, axis=-1).astype(np.float32)
+    bev_map = bev_map * 255
+
+    if pred_box is not None:
+        num_bbx = pred_box.shape[0]
+        for i in range(num_bbx):
+            bbx = pred_box[i]
+
+            bbx = ((bbx - bev_origin) / ratio).astype(int)
+            bbx = bbx[:, ::-1]
+            cv2.polylines(bev_map, [bbx], True, (0, 0, 255), 1)
+
+    if gt_box is not None and len(gt_box):
+        for i in range(gt_box.shape[0]):
+            bbx = gt_box[i][:4, :2]
+            bbx = (((bbx - bev_origin)) / ratio).astype(int)
+            bbx = bbx[:, ::-1]
+            cv2.polylines(bev_map, [bbx], True, (255, 0, 0), 1)
+
+    if show_vis:
+        plt.axis("off")
+        plt.imshow(bev_map)
+        plt.show()
+    if save_path:
+        plt.axis("off")
+        plt.imshow(bev_map)
+        plt.savefig(save_path)
+
+
+def visualize_single_sample_dataloader(batch_data,
+                                       o3d_pcd,
+                                       order,
+                                       key='origin_lidar',
+                                       visualize=False,
+                                       save_path='',
+                                       oabb=False,
+                                       mode='constant'):
+    """
+    Visualize a single frame of a single CAV for validation of data pipeline.
+
+    Parameters
+    ----------
+    o3d_pcd : o3d.PointCloud
+        Open3d PointCloud.
+
+    order : str
+        The bounding box order.
+
+    key : str
+        origin_lidar for late fusion and stacked_lidar for early fusion.
+
+    visualize : bool
+        Whether to visualize the sample.
+
+    batch_data : dict
+        The dictionary that contains current timestamp's data.
+
+    save_path : str
+        If set, save the visualization image to the path.
+
+    oabb : bool
+        If oriented bounding box is used.
+    """
+
+    origin_lidar = batch_data[key]
+    if not isinstance(origin_lidar, np.ndarray):
+        origin_lidar = common_utils.torch_tensor_to_numpy(origin_lidar)
+    # we only visualize the first cav for single sample
+    if len(origin_lidar.shape) > 2:
+        origin_lidar = origin_lidar[0]
+    origin_lidar_intcolor = \
+        color_encoding(origin_lidar[:, -1] if mode == 'intensity'
+                       else origin_lidar[:, 2], mode=mode)
+
+    # left -> right hand
+    origin_lidar[:, :1] = -origin_lidar[:, :1]
+
+    o3d_pcd.points = o3d.utility.Vector3dVector(origin_lidar[:, :3])
+    o3d_pcd.colors = o3d.utility.Vector3dVector(origin_lidar_intcolor)
+
+    object_bbx_center = batch_data['object_bbx_center']
+    object_bbx_mask = batch_data['object_bbx_mask']
+    object_bbx_center = object_bbx_center[object_bbx_mask == 1]
+
+    aabbs = bbx2linset(object_bbx_center, order) if not oabb else \
+        bbx2oabb(object_bbx_center, order)
+    visualize_elements = [o3d_pcd] + aabbs
+    if visualize:
+        o3d.visualization.draw_geometries(visualize_elements)
+
+    if save_path:
+        save_o3d_visualization(visualize_elements, save_path)
+
+    return o3d_pcd, aabbs
+
+
+def visualize_inference_sample_dataloader(pred_box_tensor,
+                                          gt_box_tensor,
+                                          origin_lidar,
+                                          o3d_pcd,
+                                          mode='constant'):
+    """
+    Visualize a frame during inference for video stream.
+
+    Parameters
+    ----------
+    pred_box_tensor : torch.Tensor
+        (N, 8, 3) prediction.
+
+    gt_box_tensor : torch.Tensor
+        (N, 8, 3) groundtruth bbx
+
+    origin_lidar : torch.Tensor
+        PointCloud, (N, 4).
+
+    o3d_pcd : open3d.PointCloud
+        Used to visualize the pcd.
+
+    mode : str
+        lidar point rendering mode.
+    """
+
+    if not isinstance(origin_lidar, np.ndarray):
+        origin_lidar = common_utils.torch_tensor_to_numpy(origin_lidar)
+    # we only visualize the first cav for single sample
+    if len(origin_lidar.shape) > 2:
+        origin_lidar = origin_lidar[0]
+    # this is for 2-stage origin lidar, it has different format
+    if origin_lidar.shape[1] > 4:
+        origin_lidar = origin_lidar[:, 1:]
+
+    origin_lidar_intcolor = \
+        color_encoding(origin_lidar[:, -1] if mode == 'intensity'
+                       else origin_lidar[:, 2], mode=mode)
+
+    if not isinstance(pred_box_tensor, np.ndarray):
+        pred_box_tensor = common_utils.torch_tensor_to_numpy(pred_box_tensor)
+    if not isinstance(gt_box_tensor, np.ndarray):
+        gt_box_tensor = common_utils.torch_tensor_to_numpy(gt_box_tensor)
+
+    # left -> right hand
+    origin_lidar[:, :1] = -origin_lidar[:, :1]
+
+    o3d_pcd.points = o3d.utility.Vector3dVector(origin_lidar[:, :3])
+    o3d_pcd.colors = o3d.utility.Vector3dVector(origin_lidar_intcolor)
+
+    gt_o3d_box = bbx2linset(gt_box_tensor, order='hwl', color=(0, 1, 0))
+    pred_o3d_box = bbx2linset(pred_box_tensor, color=(1, 0, 0))
+
+    return o3d_pcd, pred_o3d_box, gt_o3d_box
+
+
+def visualize_sequence_dataloader(dataloader, order, color_mode='constant'):
+    """
+    Visualize the batch data in animation.
+
+    Parameters
+    ----------
+    dataloader : torch.Dataloader
+        Pytorch dataloader
+
+    order : str
+        Bounding box order(N, 7).
+
+    color_mode : str
+        Color rendering mode.
+    """
+    vis = o3d.visualization.Visualizer()
+    vis.create_window()
+
+    vis.get_render_option().background_color = [0.05, 0.05, 0.05]
+    vis.get_render_option().point_size = 1.0
+    vis.get_render_option().show_coordinate_frame = True
+
+    # used to visualize lidar points
+    vis_pcd = o3d.geometry.PointCloud()
+    # used to visualize object bounding box, maximum 50
+    vis_aabbs = []
+    for _ in range(50):
+        vis_aabbs.append(o3d.geometry.LineSet())
+
+    while True:
+        for i_batch, sample_batched in enumerate(dataloader):
+            print(i_batch)
+            pcd, aabbs = \
+                visualize_single_sample_dataloader(sample_batched['ego'],
+                                                   vis_pcd,
+                                                   order,
+                                                   mode=color_mode)
+            if i_batch == 0:
+                vis.add_geometry(pcd)
+                for i in range(len(vis_aabbs)):
+                    index = i if i < len(aabbs) else -1
+                    vis_aabbs[i] = lineset_assign(vis_aabbs[i], aabbs[index])
+                    vis.add_geometry(vis_aabbs[i])
+
+            for i in range(len(vis_aabbs)):
+                index = i if i < len(aabbs) else -1
+                vis_aabbs[i] = lineset_assign(vis_aabbs[i], aabbs[index])
+                vis.update_geometry(vis_aabbs[i])
+
+            vis.update_geometry(pcd)
+            vis.poll_events()
+            vis.update_renderer()
+            time.sleep(0.001)
+
+    vis.destroy_window()
+
+
+def save_o3d_visualization(element, save_path):
+    """
+    Save the open3d drawing to folder.
+
+    Parameters
+    ----------
+    element : list
+        List of o3d.geometry objects.
+
+    save_path : str
+        The save path.
+    """
+    vis = o3d.visualization.Visualizer()
+    vis.create_window()
+    for i in range(len(element)):
+        vis.add_geometry(element[i])
+        vis.update_geometry(element[i])
+
+    vis.poll_events()
+    vis.update_renderer()
+
+    vis.capture_screen_image(save_path)
+    vis.destroy_window()
+
+
+def visualize_bev(batch_data):
+    bev_input = batch_data["processed_lidar"]["bev_input"]
+    label_map = batch_data["label_dict"]["label_map"]
+    if not isinstance(bev_input, np.ndarray):
+        bev_input = common_utils.torch_tensor_to_numpy(bev_input)
+
+    if not isinstance(label_map, np.ndarray):
+        label_map = label_map[0].numpy() if not label_map[0].is_cuda else \
+            label_map[0].cpu().detach().numpy()
+
+    if len(bev_input.shape) > 3:
+        bev_input = bev_input[0, ...]
+
+    plt.matshow(np.sum(bev_input, axis=0))
+    plt.axis("off")
+    plt.matshow(label_map[0, :, :])
+    plt.axis("off")
+    plt.show()
+
+
+def draw_box_plt(boxes_dec, ax, color=None, linewidth_scale=1.0):
+    """
+    draw boxes in a given plt ax
+    :param boxes_dec: (N, 5) or (N, 7) in metric
+    :param ax:
+    :return: ax with drawn boxes
+    """
+    if not len(boxes_dec)>0:
+        return ax
+    boxes_np= boxes_dec
+    if not isinstance(boxes_np, np.ndarray):
+        boxes_np = boxes_np.cpu().detach().numpy()
+    if boxes_np.shape[-1]>5:
+        boxes_np = boxes_np[:, [0, 1, 3, 4, 6]]
+    x = boxes_np[:, 0]
+    y = boxes_np[:, 1]
+    dx = boxes_np[:, 2]
+    dy = boxes_np[:, 3]
+
+    x1 = x - dx / 2
+    y1 = y - dy / 2
+    x2 = x + dx / 2
+    y2 = y + dy / 2
+    theta = boxes_np[:, 4:5]
+    # bl, fl, fr, br
+    corners = np.array([[x1, y1],[x1,y2], [x2,y2], [x2, y1]]).transpose(2, 0, 1)
+    new_x = (corners[:, :, 0] - x[:, None]) * np.cos(theta) + (corners[:, :, 1]
+              - y[:, None]) * (-np.sin(theta)) + x[:, None]
+    new_y = (corners[:, :, 0] - x[:, None]) * np.sin(theta) + (corners[:, :, 1]
+              - y[:, None]) * (np.cos(theta)) + y[:, None]
+    corners = np.stack([new_x, new_y], axis=2)
+    for corner in corners:
+        ax.plot(corner[[0,1,2,3,0], 0], corner[[0,1,2,3,0], 1], color=color, linewidth=0.5*linewidth_scale)
+        # draw front line (
+        ax.plot(corner[[2, 3], 0], corner[[2, 3], 1], color=color, linewidth=2*linewidth_scale)
+    return ax
+
+
+def draw_points_boxes_plt(pc_range, points=None, boxes_pred=None, boxes_gt=None, save_path=None,
+                          points_c='y.', bbox_gt_c='green', bbox_pred_c='red', return_ax=False, ax=None):
+    if ax is None:
+        ax = plt.figure(figsize=(15, 6)).add_subplot(1, 1, 1)
+        ax.set_aspect('equal', 'box')
+        ax.set(xlim=(pc_range[0], pc_range[3]),
+               ylim=(pc_range[1], pc_range[4]))
+    if points is not None:
+        ax.plot(points[:, 0], points[:, 1], points_c, markersize=0.1)
+    if (boxes_gt is not None) and len(boxes_gt)>0:
+        ax = draw_box_plt(boxes_gt, ax, color=bbox_gt_c)
+    if (boxes_pred is not None) and len(boxes_pred)>0:
+        ax = draw_box_plt(boxes_pred, ax, color=bbox_pred_c)
+    plt.xlabel('x')
+    plt.ylabel('y')
+
+    plt.savefig(save_path)
+    if return_ax:
+        return ax
+    plt.close()
\ No newline at end of file
diff --git a/opencood/requirements.txt b/opencood/requirements.txt
new file mode 100644
index 00000000..fa825378
--- /dev/null
+++ b/opencood/requirements.txt
@@ -0,0 +1,17 @@
+cmake
+easydict~=1.9
+numpy
+numba==0.49.0
+opencv-python~=4.5.1.48
+matplotlib~=3.3.3
+scipy~=1.5.4
+scikit-image
+tqdm
+PyYAML
+open3d
+cython
+tensorboardX
+shapely==2.0.0
+einops
+cumm
+timm
\ No newline at end of file
diff --git a/opencood/setup.py b/opencood/setup.py
new file mode 100644
index 00000000..e77ae752
--- /dev/null
+++ b/opencood/setup.py
@@ -0,0 +1,30 @@
+# -*- coding: utf-8 -*-
+# Author: Runsheng Xu <rxx3386@ucla.edu>
+# License: TDG-Attribution-NonCommercial-NoDistrib
+
+
+from os.path import dirname, realpath
+from setuptools import setup, find_packages, Distribution
+from opencood.version import __version__
+
+
+def _read_requirements_file():
+    """Return the elements in requirements.txt."""
+    req_file_path = '%s/requirements.txt' % dirname(realpath(__file__))
+    with open(req_file_path) as f:
+        return [line.strip() for line in f]
+
+
+setup(
+    name='OpenCOOD',
+    version=__version__,
+    packages=find_packages(),
+    url='https://github.com/ucla-mobility/OpenCDA.git',
+    license='MIT',
+    author='Runsheng Xu, Hao Xiang',
+    author_email='rxx3386@ucla.edu',
+    description='An opensource pytorch framework for autonomous driving '
+                'cooperative detection',
+    long_description=open("README.md").read(),
+    install_requires=_read_requirements_file(),
+)

From b2be23e2c02d3ec07698733ed64a560c000a0632 Mon Sep 17 00:00:00 2001
From: ju40268 <ju40268@gmail.com>
Date: Sat, 25 Feb 2023 13:28:36 -0800
Subject: [PATCH 11/14] add some comments

---
 opencda/core/common/cav_world.py                |  2 +-
 opencda/core/common/v2x_manager.py              | 15 ++++-----------
 opencda/core/common/vehicle_manager.py          |  2 +-
 .../sensing/perception/coperception_manager.py  |  9 +--------
 .../sensing/perception/perception_manager.py    |  3 ++-
 opencda/customize/ml_libs/opencood_manager.py   | 17 -----------------
 6 files changed, 9 insertions(+), 39 deletions(-)

diff --git a/opencda/core/common/cav_world.py b/opencda/core/common/cav_world.py
index 5314c92e..edc01a62 100644
--- a/opencda/core/common/cav_world.py
+++ b/opencda/core/common/cav_world.py
@@ -36,7 +36,7 @@ class CavWorld(object):
         The machine learning manager class.
     """
 
-    def __init__(self, apply_ml=False, apply_coperception=False, fusion_method='late'):
+    def __init__(self, apply_ml=False, apply_coperception=False, fusion_method='early'):
 
         self.vehicle_id_set = set()
         self._vehicle_manager_dict = {}
diff --git a/opencda/core/common/v2x_manager.py b/opencda/core/common/v2x_manager.py
index 5a42cbc0..8b8e6dff 100644
--- a/opencda/core/common/v2x_manager.py
+++ b/opencda/core/common/v2x_manager.py
@@ -93,7 +93,7 @@ def __init__(self, cav_world, config_yaml, vid):
         if 'lag' in config_yaml:
             self.lag = config_yaml['lag']
 
-    def update_info(self, ego_pos, ego_spd, ego_lidar, ego_image, ego_data):
+    def update_info(self, ego_pos, ego_spd, ego_lidar, ego_image):
         """
         Update all communication plugins with current localization info.
         """
@@ -101,7 +101,6 @@ def update_info(self, ego_pos, ego_spd, ego_lidar, ego_image, ego_data):
         self.ego_spd.append(ego_spd)
         self.ego_lidar.append(ego_lidar)
         self.ego_image.append(ego_image)
-        self.ego_data.append(ego_data)
         self.search()
 
         # the ego pos in platooning_plugin is used for self-localization,
@@ -169,13 +168,6 @@ def get_ego_rgb_image(self):
             self.ego_image[-1 - int(abs(self.lag))]
         return image
 
-    def get_ego_data(self):
-        if not self.ego_data:
-            return
-        data = self.ego_data[0] if len(self.ego_data) < self.lag else \
-            self.ego_data[-1 - int(abs(self.lag))]
-        return data
-
     def search(self):
         """
         Search the CAVs nearby.
@@ -200,6 +192,7 @@ def search(self):
                 }})
             else:
                 self.cav_nearby.pop(vm.vehicle.id, None)
+
     """
     -----------------------------------------------------------
                  Below is platooning related 
@@ -310,7 +303,7 @@ def get_platoon_manager(self):
             The ego vehicle's in team id.
         """
         return self.platooning_plugin.platooning_object, \
-               self.platooning_plugin.in_id
+            self.platooning_plugin.in_id
 
     def get_platoon_status(self):
         """
@@ -336,4 +329,4 @@ def get_platoon_front_rear(self):
             Rear vehicle of the ego vehicle in the platoon.
         """
         return self.platooning_plugin.front_vehicle, \
-               self.platooning_plugin.rear_vechile
+            self.platooning_plugin.rear_vechile
diff --git a/opencda/core/common/vehicle_manager.py b/opencda/core/common/vehicle_manager.py
index 9881be6d..fdd495ec 100644
--- a/opencda/core/common/vehicle_manager.py
+++ b/opencda/core/common/vehicle_manager.py
@@ -192,7 +192,7 @@ def update_info(self):
 
         # update ego position and speed to v2x manager,
         # and then v2x manager will search the nearby cavs
-        self.v2x_manager.update_info(ego_pos, ego_spd, ego_lidar, ego_image, self.v2x_manager)
+        self.v2x_manager.update_info(ego_pos, ego_spd, ego_lidar, ego_image)
 
         self.agent.update_information(ego_pos, ego_spd, objects)
         # pass position and speed info to controller
diff --git a/opencda/core/sensing/perception/coperception_manager.py b/opencda/core/sensing/perception/coperception_manager.py
index a9504467..321ab6ee 100644
--- a/opencda/core/sensing/perception/coperception_manager.py
+++ b/opencda/core/sensing/perception/coperception_manager.py
@@ -14,14 +14,7 @@ def communicate(self):
                 data.update({str(vid): vm})
         return data
 
-    def prepare_data(self,
-                     cav_id,
-                     camera,
-                     lidar,
-                     pos,
-                     agent,
-                     is_ego):
-
+    def prepare_data(self, cav_id, camera, lidar, pos, agent, is_ego):
         data = {cav_id: OrderedDict()}
         data[cav_id]['ego'] = is_ego
         data[cav_id]['time_delay'] = self.coperception_libs.time_delay
diff --git a/opencda/core/sensing/perception/perception_manager.py b/opencda/core/sensing/perception/perception_manager.py
index 47b48b59..4a0992e9 100644
--- a/opencda/core/sensing/perception/perception_manager.py
+++ b/opencda/core/sensing/perception/perception_manager.py
@@ -523,7 +523,8 @@ def coperception_mode(self, objects):
         if self.lidar.data is None:
             return objects
 
-        enable_communicate = False
+        # TODO: need to pass in enable_comm flag thru yaml
+        enable_communicate = True
         data = OrderedDict()
 
         ego_data = self.co_manager.prepare_data(
diff --git a/opencda/customize/ml_libs/opencood_manager.py b/opencda/customize/ml_libs/opencood_manager.py
index 1e8079c3..93652a4e 100644
--- a/opencda/customize/ml_libs/opencood_manager.py
+++ b/opencda/customize/ml_libs/opencood_manager.py
@@ -88,23 +88,6 @@ def inference(self, batch_data):
 
         return pred_box_tensor, pred_score, gt_box_tensor
 
-    def evaluate_average_precision(self):
-        for iou, iou_dict in self.result_stat.items():
-            if iou_dict['gt'] == 0:
-                print("skip this evaluation as the gt is divided by 0.")
-                return
-        ap_30, mrec_30, mpre_30 = calculate_ap(self.result_stat, 0.30)
-        ap_50, mrec_50, mpre_50 = calculate_ap(self.result_stat, 0.50)
-        ap_70, mrec_70, mpre_70 = calculate_ap(self.result_stat, 0.70)
-        print('The Average Precision at IOU 0.3 is %.2f, '
-              'The Average Precision at IOU 0.5 is %.2f, '
-              'The Average Precision at IOU 0.7 is %.2f' % (ap_30, ap_50, ap_70))
-        self.result_stat = {
-            0.3: {'tp': [], 'fp': [], 'gt': 0},
-            0.5: {'tp': [], 'fp': [], 'gt': 0},
-            0.7: {'tp': [], 'fp': [], 'gt': 0}
-        }
-
     def evaluate_final_average_precision(self):
         ap_30, mrec_30, mpre_30 = calculate_ap(self.result_stat, 0.30)
         ap_50, mrec_50, mpre_50 = calculate_ap(self.result_stat, 0.50)

From 52a341c502dd3b3e17f538642cb172c9bd8f573a Mon Sep 17 00:00:00 2001
From: ju40268 <ju40268@gmail.com>
Date: Sun, 26 Mar 2023 12:54:02 -0700
Subject: [PATCH 12/14] make the parameters passed in from the yaml files, turn
 off the coperception prediction on nearby cav (smallest id as the ego id),
 make plot gt, enable_communicate configurable

---
 opencda/core/common/cav_world.py              | 13 ++++++-
 .../sensing/perception/coperception_libs.py   | 29 ++++++--------
 .../perception/coperception_manager.py        | 15 ++++++--
 .../core/sensing/perception/o3d_lidar_libs.py | 19 ++++++----
 .../sensing/perception/perception_manager.py  | 38 ++++++++++++++-----
 opencda/customize/ml_libs/opencood_manager.py | 24 ++++++------
 .../config_yaml/v2xp_online_carla.yaml        | 33 +++++++++++-----
 opencda/scenario_testing/v2xp_online_carla.py |  2 +-
 8 files changed, 111 insertions(+), 62 deletions(-)

diff --git a/opencda/core/common/cav_world.py b/opencda/core/common/cav_world.py
index edc01a62..caae2d36 100644
--- a/opencda/core/common/cav_world.py
+++ b/opencda/core/common/cav_world.py
@@ -36,18 +36,21 @@ class CavWorld(object):
         The machine learning manager class.
     """
 
-    def __init__(self, apply_ml=False, apply_coperception=False, fusion_method='early'):
+    def __init__(self, apply_ml=False,
+                 apply_coperception=False,
+                 coperception_params=None):
 
         self.vehicle_id_set = set()
         self._vehicle_manager_dict = {}
         self._platooning_dict = {}
         self._rsu_manager_dict = {}
         self.ml_manager = None
+        self.ego_id = None
 
         if apply_ml and apply_coperception:
             ml_manager = getattr(importlib.import_module(
                 "opencda.customize.ml_libs.opencood_manager"), 'OpenCOODManager')
-            self.ml_manager = ml_manager(fusion_method)
+            self.ml_manager = ml_manager(coperception_params)
         elif apply_ml:
             # we import in this way so the user don't need to install ml
             # packages unless they require to
@@ -58,6 +61,12 @@ def __init__(self, apply_ml=False, apply_coperception=False, fusion_method='earl
         # this is used only when co-simulation activated.
         self.sumo2carla_ids = {}
 
+    def update_global_ego_id(self):
+        """
+        Return the smallest id as the ego_id
+        """
+        self.ego_id = min(self.vehicle_id_set)
+
     def update_vehicle_manager(self, vehicle_manager):
         """
         Update created CAV manager to the world.
diff --git a/opencda/core/sensing/perception/coperception_libs.py b/opencda/core/sensing/perception/coperception_libs.py
index d92c5c18..48b8f2e9 100644
--- a/opencda/core/sensing/perception/coperception_libs.py
+++ b/opencda/core/sensing/perception/coperception_libs.py
@@ -65,14 +65,14 @@ def dist_to_ego(actor):
         return data
 
     @staticmethod
-    def load_transformation_matrix(is_ego, data):
+    def load_transformation_matrix(ego_data, cav_data):
         """
         TODO: needs revision to reflect the cur/delay params
         """
-        cur_params = data
-        delay_params = data
-        cur_ego_params = data
-        delay_ego_params = data
+        cur_params = cav_data
+        delay_params = cav_data
+        cur_ego_params = ego_data
+        delay_ego_params = ego_data
 
         delay_cav_lidar_pose = delay_params['lidar_pose']
         delay_ego_lidar_pose = delay_ego_params["lidar_pose"]
@@ -89,12 +89,8 @@ def load_transformation_matrix(is_ego, data):
                                                     self.xyz_noise_std,
                                                     self.ryp_noise_std)
         """
-        if is_ego:
-            transformation_matrix = x1_to_x2(delay_cav_lidar_pose, cur_ego_lidar_pose)
-            spatial_correction_matrix = np.eye(4)
-        else:
-            transformation_matrix = x1_to_x2(delay_cav_lidar_pose, delay_ego_lidar_pose)
-            spatial_correction_matrix = x1_to_x2(delay_ego_lidar_pose, cur_ego_lidar_pose)
+        transformation_matrix = x1_to_x2(delay_cav_lidar_pose, delay_ego_lidar_pose)
+        spatial_correction_matrix = x1_to_x2(delay_ego_lidar_pose, cur_ego_lidar_pose)
         gt_transformation_matrix = x1_to_x2(cur_cav_lidar_pose, cur_ego_lidar_pose)
         return {
             'transformation_matrix': transformation_matrix,
@@ -148,17 +144,16 @@ def load_camera_data(self, lidar, rgb_camera):
             data.update({'camera%d' % i: camera_param})
         return data
 
-    def load_ego_data(self):
+    def load_ego_data(self, localizer):
         data = {
             'predicted_ego_pos': [],
             'true_ego_pos': [],
             'ego_speed': 0.0
         }
-        if not self.localizer:
+        if not localizer:
             return data
-
-        predicted_ego_pos = self.localizer.get_ego_pos()
-        true_ego_pos = self.localizer.vehicle.get_transform() \
+        predicted_ego_pos = localizer.get_ego_pos()
+        true_ego_pos = localizer.vehicle.get_transform() \
             if hasattr(self.localizer, 'vehicle') \
             else self.localizer.true_ego_pos
         data = {
@@ -178,7 +173,7 @@ def load_ego_data(self):
                 true_ego_pos.rotation.yaw,
                 true_ego_pos.rotation.pitch
             ],
-            'ego_speed': float(self.localizer.get_ego_spd())
+            'ego_speed': float(localizer.get_ego_spd())
         }
         return data
 
diff --git a/opencda/core/sensing/perception/coperception_manager.py b/opencda/core/sensing/perception/coperception_manager.py
index 321ab6ee..87e4c93b 100644
--- a/opencda/core/sensing/perception/coperception_manager.py
+++ b/opencda/core/sensing/perception/coperception_manager.py
@@ -6,6 +6,7 @@ def __init__(self, vid, v2x_manager, coperception_libs):
         self.vid = vid
         self.v2x_manager = v2x_manager
         self.coperception_libs = coperception_libs
+        self.ego_data_dict = None
 
     def communicate(self):
         data = {}
@@ -14,13 +15,18 @@ def communicate(self):
                 data.update({str(vid): vm})
         return data
 
-    def prepare_data(self, cav_id, camera, lidar, pos, agent, is_ego):
+    def calculate_transformation(self, cav_id, cav_data):
+        t_matrix = self.coperception_libs.load_transformation_matrix(self.ego_data_dict, cav_data[cav_id]['params'])
+        cav_data[cav_id]['params'].update(t_matrix)
+        return cav_data
+
+    def prepare_data(self, cav_id, camera, lidar, pos, localizer, agent, is_ego, ego_params=None):
         data = {cav_id: OrderedDict()}
         data[cav_id]['ego'] = is_ego
         data[cav_id]['time_delay'] = self.coperception_libs.time_delay
         data[cav_id]['params'] = {}
         camera_data = self.coperception_libs.load_camera_data(lidar, camera)
-        ego_data = self.coperception_libs.load_ego_data()
+        ego_data = self.coperception_libs.load_ego_data(localizer)
         plan_trajectory_data = self.coperception_libs.load_plan_trajectory(agent)
         lidar_pose_data = self.coperception_libs.load_cur_lidar_pose(lidar)
         vehicles = self.coperception_libs.load_vehicles(cav_id, pos, lidar)
@@ -29,7 +35,8 @@ def prepare_data(self, cav_id, camera, lidar, pos, agent, is_ego):
         data[cav_id]['params'].update(ego_data)
         data[cav_id]['params'].update(lidar_pose_data)
         data[cav_id]['params'].update(vehicles)
-        t_matrix = self.coperception_libs.load_transformation_matrix(data[cav_id]['ego'], data[cav_id]['params'])
-        data[cav_id]['params'].update(t_matrix)
         data[cav_id].update({'lidar_np': lidar.data})
+        # get base_data_dict
+        if is_ego:
+            self.ego_data_dict = data[cav_id]['params']
         return data
diff --git a/opencda/core/sensing/perception/o3d_lidar_libs.py b/opencda/core/sensing/perception/o3d_lidar_libs.py
index cee6f21a..0573ffeb 100644
--- a/opencda/core/sensing/perception/o3d_lidar_libs.py
+++ b/opencda/core/sensing/perception/o3d_lidar_libs.py
@@ -160,17 +160,18 @@ def o3d_visualizer_show(vis, count, point_cloud, objects):
             vis.remove_geometry(aabb)
 
 
-def o3d_visualizer_show_coperception(vis, count, point_cloud, predict_bbx_tensor, gt_box_tensor, objects):
+def o3d_visualizer_show_coperception(vis, count, point_cloud, predict_bbx_tensor, gt_box_tensor, show_gt, objects):
     if count == 2:
         vis.add_geometry(point_cloud)
     if predict_bbx_tensor is not None:
         oabbs_pred = bbx2oabb(predict_bbx_tensor, color=(1, 0, 0))
         for p in oabbs_pred:
             vis.add_geometry(p)
-    if gt_box_tensor is not None:
-        oabbs_gt = bbx2oabb(gt_box_tensor, color=(0, 1, 0))
-        for g in oabbs_gt:
-            vis.add_geometry(g)
+    if show_gt:
+        if gt_box_tensor is not None:
+            oabbs_gt = bbx2oabb(gt_box_tensor, color=(0, 1, 0))
+            for g in oabbs_gt:
+                vis.add_geometry(g)
 
     vis.update_geometry(point_cloud)
 
@@ -197,9 +198,11 @@ def o3d_visualizer_show_coperception(vis, count, point_cloud, predict_bbx_tensor
         # remove the prediction bbx drawing
         for p in oabbs_pred:
             vis.remove_geometry(p)
-    if gt_box_tensor is not None:
-        for g in oabbs_gt:
-            vis.remove_geometry(g)
+
+    if show_gt:
+        if gt_box_tensor is not None:
+            for g in oabbs_gt:
+                vis.remove_geometry(g)
 
 
 def o3d_camera_lidar_fusion(objects,
diff --git a/opencda/core/sensing/perception/perception_manager.py b/opencda/core/sensing/perception/perception_manager.py
index 4a0992e9..092e2961 100644
--- a/opencda/core/sensing/perception/perception_manager.py
+++ b/opencda/core/sensing/perception/perception_manager.py
@@ -390,8 +390,12 @@ def __init__(self, v2x_manager, localization_manager, behavior_agent, vehicle,
         self.lidar_visualize = config_yaml['lidar_visualize']
         self.global_position = config_yaml['global_position'] \
             if 'global_position' in config_yaml else None
-        self.cooperative = config_yaml['cooperative'] \
-            if 'cooperative' in config_yaml else False
+        self.coperception = config_yaml['coperception'] \
+            if 'coperception' in config_yaml else False
+        self.enable_communicate = config_yaml['enable_communicate'] \
+            if 'enable_communicate' in config_yaml else False
+        self.enable_show_gt = config_yaml['enable_show_gt'] \
+            if 'enable_show_gt' in config_yaml else False
         self.v2x_manager = v2x_manager
         self.localization_manager = localization_manager
         self.behavior_agent = behavior_agent
@@ -508,7 +512,7 @@ def detect(self, ego_pos):
         if not self.activate:
             objects = self.deactivate_mode(objects)
         else:
-            if self.cooperative:
+            if self.coperception:
                 objects = self.coperception_mode(objects)
             else:
                 objects = self.activate_mode(objects)
@@ -520,11 +524,16 @@ def coperception_mode(self, objects):
         """
         Use OpenCOOD to detect objects
         """
+        self.cav_world.update_global_ego_id()
+        ego_id = self.cav_world.ego_id
+
+        if self.id != ego_id:
+            objects = self.deactivate_mode(objects)
+            return objects
+
         if self.lidar.data is None:
             return objects
 
-        # TODO: need to pass in enable_comm flag thru yaml
-        enable_communicate = True
         data = OrderedDict()
 
         ego_data = self.co_manager.prepare_data(
@@ -532,12 +541,17 @@ def coperception_mode(self, objects):
             camera=self.rgb_camera,
             lidar=self.lidar,
             pos=self.ego_pos,
+            localizer=self.localization_manager,
             agent=self.behavior_agent,
             is_ego=True
         )
+        ego_data = self.co_manager.calculate_transformation(
+            cav_id=self.id,
+            cav_data=ego_data,
+        )
         data.update(ego_data)
 
-        if enable_communicate:
+        if self.enable_communicate:
             nearby_objects = self.co_manager.communicate()
             for vid, nearby_data_dict in nearby_objects.items():
                 nearby_vm = nearby_data_dict['vehicle_manager']
@@ -547,20 +561,25 @@ def coperception_mode(self, objects):
                     camera=nearby_v2x_manager.get_ego_rgb_image(),
                     lidar=nearby_v2x_manager.get_ego_lidar(),
                     pos=nearby_v2x_manager.get_ego_pos(),
+                    localizer=nearby_vm.localizer,
                     agent=nearby_vm.agent,
                     is_ego=False
                 )
+                nearby_data = self.co_manager.calculate_transformation(
+                    cav_id=vid,
+                    cav_data=nearby_data,
+                )
                 data.update(nearby_data)
+
         # inference
         reformat_data_dict = self.ml_manager.opencood_dataset.get_item_test(data)
         output_dict = self.ml_manager.opencood_dataset.collate_batch_test(
             [reformat_data_dict])  # should have batch size dim
         batch_data = self.ml_manager.to_device(output_dict)
         predict_box_tensor, predict_score, gt_box_tensor = self.ml_manager.inference(batch_data)
-        # self.ml_manager.show_vis(pred_box_tensor, gt_box_tensor, batch_data)
-        # self.ml_manager.evaluate_final_average_precision()
+        # self.ml_manager.show_vis(pred_box_tensor, gt_box_tensor, batch_data) show predict results frame by frame
 
-        # plot
+        # plot the opencood inference results
         if self.lidar_visualize:
             while self.lidar.data is None:
                 continue
@@ -571,6 +590,7 @@ def coperception_mode(self, objects):
                 self.lidar.o3d_pointcloud,
                 predict_box_tensor,
                 gt_box_tensor,
+                self.enable_show_gt,
                 objects)
         objects = self.retrieve_traffic_lights(objects)
         self.objects = objects
diff --git a/opencda/customize/ml_libs/opencood_manager.py b/opencda/customize/ml_libs/opencood_manager.py
index 93652a4e..549a62da 100644
--- a/opencda/customize/ml_libs/opencood_manager.py
+++ b/opencda/customize/ml_libs/opencood_manager.py
@@ -12,12 +12,6 @@
 from opencood.data_utils.datasets.early_fusion_dataset import EarlyFusionDataset
 from opencood.data_utils.datasets.intermediate_fusion_dataset import IntermediateFusionDataset
 
-model_mapping = {
-    'early': 'opencood/logs/pointpillar_early_fusion',
-    'late': 'opencood/logs/pointpillar_late_fusion',
-    'intermediate': 'opencood/logs/pointpillar_attentive_fusion',
-}
-
 DATASET_DICT = {
     'late': LateFusionDataset,
     'early': EarlyFusionDataset,
@@ -26,16 +20,20 @@
 
 
 class OpenCOODManager(object):
-    def __init__(self, fusion_method):
-        assert fusion_method in ['late', 'early', 'intermediate']
+    def __init__(self, coperception_params):
+        fusion_method = coperception_params['fusion_method']
+        models = coperception_params['models']
+        assert fusion_method in models, f'Fusion method should be within one of the models supported, it is provided ' \
+                                        f'with {fusion_method} '
+
         self.fusion_method = fusion_method
-        self.opt = argparse.Namespace(model_dir=model_mapping[fusion_method])
+        self.opt = argparse.Namespace(model_dir=models[fusion_method])
         hypes = yaml_utils.load_yaml(None, self.opt)
         self.model = train_utils.create_model(hypes)
         if torch.cuda.is_available():
             self.model.cuda()
         self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-        saved_path = model_mapping[fusion_method]
+        saved_path = models[fusion_method]
         _, model = train_utils.load_saved_model(saved_path, self.model)
         model.eval()
 
@@ -61,7 +59,7 @@ def inference(self, batch_data):
                 inference_utils.inference_early_fusion(batch_data,
                                                        self.model,
                                                        self.opencood_dataset)
-        elif self.fusion_method == 'intermediate':
+        elif self.fusion_method.startswith('intermediate'): # intermediate would be different models
             pred_box_tensor, pred_score, gt_box_tensor = \
                 inference_utils.inference_intermediate_fusion(batch_data,
                                                               self.model,
@@ -89,10 +87,12 @@ def inference(self, batch_data):
         return pred_box_tensor, pred_score, gt_box_tensor
 
     def evaluate_final_average_precision(self):
+        print('Evaluate final average precision results:')
+        print(f'  - Fusion method: {self.fusion_method}')
         ap_30, mrec_30, mpre_30 = calculate_ap(self.result_stat, 0.30)
         ap_50, mrec_50, mpre_50 = calculate_ap(self.result_stat, 0.50)
         ap_70, mrec_70, mpre_70 = calculate_ap(self.result_stat, 0.70)
-        print('The Average Precision at IOU 0.3 is %.2f, '
+        print('  - The Average Precision at IOU 0.3 is %.2f, '
               'The Average Precision at IOU 0.5 is %.2f, '
               'The Average Precision at IOU 0.7 is %.2f' % (ap_30, ap_50, ap_70))
 
diff --git a/opencda/scenario_testing/config_yaml/v2xp_online_carla.yaml b/opencda/scenario_testing/config_yaml/v2xp_online_carla.yaml
index 031f77ee..4e840327 100644
--- a/opencda/scenario_testing/config_yaml/v2xp_online_carla.yaml
+++ b/opencda/scenario_testing/config_yaml/v2xp_online_carla.yaml
@@ -40,20 +40,22 @@ rsu_base: &rsu_base
   sensing:
     perception:
       activate: true # when not activated, objects positions will be retrieved from server directly
-      cooperative: true
+      coperception: true
+      enable_communicate: true
+      enable_show_gt: false
       camera_visualize: 1 # how many camera images need to be visualized. 0 means no visualization for camera
       camera_num: 4 # how many cameras are mounted on the vehicle. Maximum 3(frontal, left and right cameras)
       lidar_visualize: true # whether to visualize lidar points using open3d
       lidar: # lidar sensor configuration, check CARLA sensor reference for more details
-        channels: 32
+        channels: 64
         range: 120
-        points_per_second: 1000000
+        points_per_second: 1300000
         rotation_frequency: 20 # the simulation is 20 fps
         upper_fov: 2
         lower_fov: -25
-        dropoff_general_rate: 0.3
+        dropoff_general_rate: 0.1
         dropoff_intensity_limit: 0.7
-        dropoff_zero_intensity: 0.4
+        dropoff_zero_intensity: 0.15
         noise_stddev: 0.02
     localization:
       activate: false # when not activated, ego position will be retrieved from server directly
@@ -68,12 +70,14 @@ vehicle_base: &vehicle_base
   sensing: &base_sensing
     perception: &base_perception
       activate: true # when not activated, objects positions will be retrieved from server directly
-      cooperative: true # cooperative perception
+      coperception: true # cooperative perception
+      enable_communicate: true
+      enable_show_gt: false
       camera_visualize: 1 # how many camera images need to be visualized. 0 means no visualization for camera
       camera_num: 4 # how many cameras are mounted on the vehicle. Maximum 3(frontal, left and right cameras)
       lidar_visualize: true # whether to visualize lidar points using open3d
       lidar: # lidar sensor configuration, check CARLA sensor reference for more details
-        channels: 32
+        channels: 64
         range: 120
         points_per_second: 1000000
         rotation_frequency: 20 # the simulation is 20 fps
@@ -85,7 +89,7 @@ vehicle_base: &vehicle_base
         noise_stddev: 0.02
     localization: &base_localize
       activate: false # when not activated, ego position will be retrieved from server directly
-      cooperative: false # cooperative perception
+      coperception: false # cooperative perception
       dt: *delta # used for kalman filter
       gnss: # gnss sensor configuration
         noise_alt_stddev: 0.005
@@ -198,4 +202,15 @@ scenario:
         local_planner:
           <<: *base_local_planner
           debug_trajectory: false
-          debug: false
\ No newline at end of file
+          debug: false
+
+# these are parameters supporting cooperative perception
+# models are pending to be added with all the models supported in the intermediate fusion
+coperception:
+  models:
+    early: 'opencood/logs/pointpillar_early_fusion'
+    intermediate_v2xvit: 'opencood/logs/pointpillar_v2xvit_fusion'
+    intermediate_attentive: 'opencood/logs/pointpillar_attentive_fusion'
+    intermediate_cobevt: 'opencood/logs/pointpillar_cobevt_fusion'
+    late: 'opencood/logs/pointpillar_late_fusion'
+  fusion_method: 'intermediate_v2xvit'
diff --git a/opencda/scenario_testing/v2xp_online_carla.py b/opencda/scenario_testing/v2xp_online_carla.py
index 96ddea2d..a503c11e 100644
--- a/opencda/scenario_testing/v2xp_online_carla.py
+++ b/opencda/scenario_testing/v2xp_online_carla.py
@@ -23,7 +23,7 @@ def run_scenario(opt, config_yaml):
         # create CAV world
         cav_world = CavWorld(apply_ml=opt.apply_ml,
                              apply_coperception=True,
-                             fusion_method='early')
+                             coperception_params=scenario_params['coperception'])
 
         # create scenario manager
         scenario_manager = sim_api.ScenarioManager(scenario_params,

From 922dbf58fad0be18b3285babba46dacceaf355c9 Mon Sep 17 00:00:00 2001
From: ju40268 <ju40268@gmail.com>
Date: Tue, 28 Mar 2023 19:19:24 -0700
Subject: [PATCH 13/14] make the GT range match with 100, and larger the
 communication range, disable bicycle

---
 .../config_yaml/v2xp_online_carla.yaml                 | 10 +++++-----
 opencood/opencood/data_utils/datasets/__init__.py      |  2 +-
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/opencda/scenario_testing/config_yaml/v2xp_online_carla.yaml b/opencda/scenario_testing/config_yaml/v2xp_online_carla.yaml
index 4e840327..dfcd8363 100644
--- a/opencda/scenario_testing/config_yaml/v2xp_online_carla.yaml
+++ b/opencda/scenario_testing/config_yaml/v2xp_online_carla.yaml
@@ -32,8 +32,8 @@ blueprint:
     car: 0.5
     truck: 0.1
     bus: 0.1
-    bicycle: 0.1
-    motorcycle: 0.1
+#    bicycle: 0.1
+#    motorcycle: 0.1
 
 # Define the basic parameters of the rsu
 rsu_base: &rsu_base
@@ -145,7 +145,7 @@ vehicle_base: &vehicle_base
       max_steering: 0.3
   v2x: &base_v2x # communication related
     enabled: true
-    communication_range: 35
+    communication_range: 70
     loc_noise: 0.0
     yaw_noise: 0.0
     speed_noise: 0.0
@@ -184,7 +184,7 @@ scenario:
       destination: [67.12, 150.2, 1.0]
       v2x:
         <<: *base_v2x
-        communication_range: 45
+        communication_range: 70
       behavior:
         <<: *base_behavior
         local_planner:
@@ -196,7 +196,7 @@ scenario:
       destination: [-2.43, 263, 0.3]
       v2x:
         <<: *base_v2x
-        communication_range: 45
+        communication_range: 70
       behavior:
         <<: *base_behavior
         local_planner:
diff --git a/opencood/opencood/data_utils/datasets/__init__.py b/opencood/opencood/data_utils/datasets/__init__.py
index 1ec93be5..0b13f5f2 100644
--- a/opencood/opencood/data_utils/datasets/__init__.py
+++ b/opencood/opencood/data_utils/datasets/__init__.py
@@ -15,7 +15,7 @@
 }
 
 # the final range for evaluation
-GT_RANGE = [-140, -40, -3, 140, 40, 1]
+GT_RANGE = [-100, -40, -3, 100, 40, 1]
 # The communication range for cavs
 COM_RANGE = 70
 

From d82ad40935652978bece296d2b64e51f250090ac Mon Sep 17 00:00:00 2001
From: ju40268 <ju40268@gmail.com>
Date: Thu, 30 Mar 2023 18:36:08 -0700
Subject: [PATCH 14/14] bump the range for lidar ground truth, skip the first
 60 frames and count only even frames

---
 .../sensing/perception/coperception_libs.py   |  2 +-
 .../sensing/perception/perception_manager.py  |  6 ++-
 opencda/customize/ml_libs/opencood_manager.py | 41 ++++++++++---------
 3 files changed, 27 insertions(+), 22 deletions(-)

diff --git a/opencda/core/sensing/perception/coperception_libs.py b/opencda/core/sensing/perception/coperception_libs.py
index 48b8f2e9..2a8cd8e0 100644
--- a/opencda/core/sensing/perception/coperception_libs.py
+++ b/opencda/core/sensing/perception/coperception_libs.py
@@ -49,7 +49,7 @@ def dist_to_ego(actor):
 
         world = self.carla_world
         vehicle_list = world.get_actors().filter("*vehicle*")
-        vehicle_list = [v for v in vehicle_list if dist_to_ego(v) < 50 and v.id != ego_id]
+        vehicle_list = [v for v in vehicle_list if dist_to_ego(v) < 120 and v.id != ego_id]
         vehicle_dict = {}
         if lidar:
             for v in vehicle_list:
diff --git a/opencda/core/sensing/perception/perception_manager.py b/opencda/core/sensing/perception/perception_manager.py
index 092e2961..1d502476 100644
--- a/opencda/core/sensing/perception/perception_manager.py
+++ b/opencda/core/sensing/perception/perception_manager.py
@@ -695,12 +695,16 @@ def deactivate_mode(self, objects):
         world = self.carla_world
 
         vehicle_list = world.get_actors().filter("*vehicle*")
-        thresh = 50 if not self.data_dump else 120
+        if self.coperception:
+            thresh = 120
+        else:
+            thresh = 50 if not self.data_dump else 120
 
         vehicle_list = [v for v in vehicle_list if self.dist(v) < thresh and
                         v.id != self.id]
 
         # use semantic lidar to filter out vehicles out of the range
+        # TODO: self.coperception?
         if self.data_dump:
             vehicle_list = self.filter_vehicle_out_sensor(vehicle_list)
 
diff --git a/opencda/customize/ml_libs/opencood_manager.py b/opencda/customize/ml_libs/opencood_manager.py
index 549a62da..7026b407 100644
--- a/opencda/customize/ml_libs/opencood_manager.py
+++ b/opencda/customize/ml_libs/opencood_manager.py
@@ -25,7 +25,7 @@ def __init__(self, coperception_params):
         models = coperception_params['models']
         assert fusion_method in models, f'Fusion method should be within one of the models supported, it is provided ' \
                                         f'with {fusion_method} '
-
+        self.counter = 0
         self.fusion_method = fusion_method
         self.opt = argparse.Namespace(model_dir=models[fusion_method])
         hypes = yaml_utils.load_yaml(None, self.opt)
@@ -43,7 +43,6 @@ def __init__(self, coperception_params):
         self.result_stat = {0.3: {'tp': [], 'fp': [], 'gt': 0},
                             0.5: {'tp': [], 'fp': [], 'gt': 0},
                             0.7: {'tp': [], 'fp': [], 'gt': 0}}
-        self.vis_counter = 0
 
     def to_device(self, data):
         return train_utils.to_device(data, self.device)
@@ -68,22 +67,25 @@ def inference(self, batch_data):
             raise NotImplementedError('Only early, late and intermediate'
                                       'fusion is supported.')
 
-        eval_utils.caluclate_tp_fp(pred_box_tensor,
-                                   pred_score,
-                                   gt_box_tensor,
-                                   self.result_stat,
-                                   0.3)
-        eval_utils.caluclate_tp_fp(pred_box_tensor,
-                                   pred_score,
-                                   gt_box_tensor,
-                                   self.result_stat,
-                                   0.5)
-        eval_utils.caluclate_tp_fp(pred_box_tensor,
-                                   pred_score,
-                                   gt_box_tensor,
-                                   self.result_stat,
-                                   0.7)
-
+        # skip the first 60 ticks for calculating the average precision
+        if self.counter > 60 and self.counter % 2 == 0:
+            print(f"Aggregating the current stats into final results: {self.counter}")
+            eval_utils.caluclate_tp_fp(pred_box_tensor,
+                                       pred_score,
+                                       gt_box_tensor,
+                                       self.result_stat,
+                                       0.3)
+            eval_utils.caluclate_tp_fp(pred_box_tensor,
+                                       pred_score,
+                                       gt_box_tensor,
+                                       self.result_stat,
+                                       0.5)
+            eval_utils.caluclate_tp_fp(pred_box_tensor,
+                                       pred_score,
+                                       gt_box_tensor,
+                                       self.result_stat,
+                                       0.7)
+        self.counter += 1
         return pred_box_tensor, pred_score, gt_box_tensor
 
     def evaluate_final_average_precision(self):
@@ -100,7 +102,7 @@ def show_vis(self, pred_box_tensor, gt_box_tensor, batch_data):
         vis_save_path = os.path.join(self.opt.model_dir, 'vis')
         if not os.path.exists(vis_save_path):
             os.makedirs(vis_save_path)
-        vis_save_path = os.path.join(vis_save_path, '%05d.png' % self.vis_counter)
+        vis_save_path = os.path.join(vis_save_path, '%05d.png' % self.counter)
 
         vis_utils.visualize_single_sample_output_gt(pred_box_tensor,
                                                     gt_box_tensor,
@@ -109,4 +111,3 @@ def show_vis(self, pred_box_tensor, gt_box_tensor, batch_data):
                                                     True,
                                                     vis_save_path,
                                                     mode='constant')
-        self.vis_counter += 1