OpenPCDet is a clear, simple, self-contained open source project for LiDAR-based 3D object detection.
It is also the official code release of [PointRCNN], [Part-A^2 net] and [PV-RCNN].
[2020-10-7] Added Traffic Dataset with strong results. [2020-08-10] NEW: Bugfixed: The provided NuScenes models have been updated to fix the loading bugs. Please redownload it if you need to use the pretrained NuScenes models.
[2020-07-30] NEW: OpenPCDet v0.3.0 is released with the following features:
- The Point-based and Anchor-Free models (
PointRCNN,PartA2-Free) are supported now. - The NuScenes dataset is supported with strong baseline results (
SECOND-MultiHead (CBGS)andPointPillar-MultiHead). - High efficiency than last version, support
PyTorch 1.1~1.5andspconv 1.0~1.2simultaneously.
[2020-07-17] Add simple visualization codes and a quick demo to test with custom data.
[2020-06-24] OpenPCDet v0.2.0 is released with pretty new structures to support more models and datasets.
[2020-03-16] OpenPCDet v0.1.0 is released.
Note that we have upgrated PCDet from v0.1 to v0.2 with pretty new structures to support various datasets and models.
OpenPCDet is a general PyTorch-based codebase for 3D object detection from point cloud.
It currently supports multiple state-of-the-art 3D object detection methods with highly refactored codes for both one-stage and two-stage 3D detection frameworks.
Based on OpenPCDet toolbox, we win the Waymo Open Dataset challenge in 3D Detection,
3D Tracking, Domain Adaptation
three tracks among all LiDAR-only methods, and the Waymo related models will be released to OpenPCDet soon.
We are actively updating this repo currently, and more datasets and models will be supported soon. Contributions are also welcomed.
- Data-Model separation with unified point cloud coordinate for easily extending to custom datasets:
-
Unified 3D box definition: (x, y, z, dx, dy, dz, heading).
-
Flexible and clear model structure to easily support various 3D detection models:
- Support various models within one framework as:
- Support both one-stage and two-stage 3D object detection frameworks
- Support distributed training & testing with multiple GPUs and multiple machines
- Support multiple heads on different scales to detect different classes
- Support stacked version set abstraction to encode various number of points in different scenes
- Support Adaptive Training Sample Selection (ATSS) for target assignment
- Support RoI-aware point cloud pooling & RoI-grid point cloud pooling
- Support GPU version 3D IoU calculation and rotated NMS
Selected supported methods are shown in the below table. The results are the 3D detection performance of moderate difficulty on the val set of KITTI dataset.
- All models are trained with 8 GTX 1080Ti GPUs and are available for download.
- The training time is measured with 8 TITAN XP GPUs and PyTorch 1.5.
| training time | Car | Pedestrian | Cyclist | download | |
|---|---|---|---|---|---|
| PointPillar | ~1.2 hours | 77.28 | 52.29 | 62.68 | model-18M |
| SECOND | ~1.7 hours | 78.62 | 52.98 | 67.15 | model-20M |
| PointRCNN | ~3 hours | 78.70 | 54.41 | 72.11 | model-16M |
| PointRCNN-IoU | ~3 hours | 78.75 | 58.32 | 71.34 | model-16M |
| Part-A^2-Free | ~3.8 hours | 78.72 | 65.99 | 74.29 | model-226M |
| Part-A^2-Anchor | ~4.3 hours | 79.40 | 60.05 | 69.90 | model-244M |
| PV-RCNN | ~5 hours | 83.61 | 57.90 | 70.47 | model-50M |
All models are trained with 8 GTX 1080Ti GPUs and are available for download.
| mATE | mASE | mAOE | mAVE | mAAE | mAP | NDS | download | |
|---|---|---|---|---|---|---|---|---|
| PointPillar-MultiHead | 33.87 | 26.00 | 32.07 | 28.74 | 20.15 | 44.63 | 58.23 | model-23M |
| SECOND-MultiHead (CBGS) | 31.15 | 25.51 | 26.64 | 26.26 | 20.46 | 50.59 | 62.29 | model-35M |
The PointPillar method is trained on the Traffic dataset, and the results are are the 3D detection on the val set. The model is trained with 1 TITAN XP GPU.
| training time | Car | Pedestrian | Truck | Model | |
|---|---|---|---|---|---|
| PointPillar | ~8hours | 76.57 | 32.42 | 82.62 | model |
More datasets are on the way.
Please refer to INSTALL.md for the installation of OpenPCDet.
Please refer to DEMO.md for a quick demo to test with a pretrained model and visualize the predicted results on your custom data or the original KITTI data.
Please refer to GETTING_STARTED.md to learn more usage about this project.
OpenPCDet is released under the Apache 2.0 license.
OpenPCDet is an open source project for LiDAR-based 3D scene perception that supports multiple
LiDAR-based perception models as shown above. Some parts of PCDet are learned from the official released codes of the above supported methods.
We would like to thank for their proposed methods and the official implementation.
We hope that this repo could serve as a strong and flexible codebase to benefit the research community by speeding up the process of reimplementing previous works and/or developing new methods.
If you find this project useful in your research, please consider cite:
@inproceedings{shi2020pv,
title={Pv-rcnn: Point-voxel feature set abstraction for 3d object detection},
author={Shi, Shaoshuai and Guo, Chaoxu and Jiang, Li and Wang, Zhe and Shi, Jianping and Wang, Xiaogang and Li, Hongsheng},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={10529--10538},
year={2020}
}
@article{shi2020points,
title={From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network},
author={Shi, Shaoshuai and Wang, Zhe and Shi, Jianping and Wang, Xiaogang and Li, Hongsheng},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
year={2020},
publisher={IEEE}
}
@inproceedings{shi2019pointrcnn,
title={PointRCNN: 3d Object Progposal Generation and Detection from Point Cloud},
author={Shi, Shaoshuai and Wang, Xiaogang and Li, Hongsheng},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
pages={770--779},
year={2019}
}
This project is currently maintained by Shaoshuai Shi (@sshaoshuai) and Chaoxu Guo (@Gus-Guo).