BEV-LaneDet: a Simple and Effective 3D Lane Detection Baseline

Introduction

BEV-LaneDet is an efficient and robust monocular 3D lane detection system. First, we introduce the Virtual Camera, which unifies the intrinsic/extrinsic parameters of cameras mounted on different vehicles to ensure the consistency of the spatial relationship between cameras. It can effectively promote the learning process due to the unified visual space. Secondly, we propose a simple but efficient 3D lane representation called Key-Points Representation. This module is more suitable for representing the complicated and diverse 3D lane structures. Finally, we present a lightweight and chip-friendly spatial transformation module called Spatial Transformation Pyramid to transform multi-scale front view features into BEV features. Experimental results demonstrate that our work outperforms the state-of-the-art approaches in terms of F-Score, being 10.6% higher on the OpenLane dataset and 5.9% higher on the Apollo 3D synthetic dataset, with a speed of 185 FPS. Our paper has been accepted by cvpr2023 arxiv.

• Get Started
• Benchmark
• Visualization

Get Started

Installation

• To run our code, make sure you are using a machine with at least one GPU.
• Setup the enviroment

pip install -r requirements.txt

Training and evaluation on OpenLane

• Please refer to OpenLane for downloading OpenLane Dataset. For example: download OpenLane dataset to /dataset/openlane

• How to train:

  1. Please modify the configuration in the /tools/openlane_config.py
  2. Execute the following code:

cd tools
python3 train_openlane.py

• How to evaluation:
  1. Please modify the configuration in the /tools/val_openlane.py
  2. Execute the following code:

cd tools
python val_openlane.py

Training and evaluation on Apollo 3D Lane Synthetic

• Please refer to Apollo 3D Lane Synthetic for downloading Apollo 3D Lane Synthetic Dataset. For example: download OpenLane dataset to /dataset/apollo

• How to train:

  1. Please modify the configuration in the /tools/apollo_config.py
  2. Execute the following code:

cd tools
python3 train_apollo.py

• How to evaluation:
  1. Please modify the configuration in the /tools/val_apollo.py
  2. Execute the following code:

cd tools
python val_apollo.py

Benchmark

Results of different models on OpenLane dataset

Method	F-Score	X error near	X error far	Z error near	Z error far	GFLOPs	TensorRT	PyTorch
Gen-LaneNet	29.7	0.309	0.877	0.16	0.75	34	–	54FPS
PersFormer	47.8	0.322	0.778	0.213	0.681	143	–	21FPS
Ours	58.4	0.309	0.659	0.244	0.631	53	185FPS	102FPS

Results of different models on Apollo 3D Lane Synthetic (Balanced Scence)

Method	F-Score	X error near	X error far	Z error near	Z error far
3D-LaneNet	86.4	0.068	0.477	0.015	0.202
Gen-LaneNet	88.1	0.061	0.486	0.012	0.214
CLGO	91.9	0.061	0.361	0.029	0.25
PersFormer	92.9	0.054	0.356	0.01	0.234
Ours	98.7	0.016	0.242	0.02	0.216

Virtual Camera

CPU implementation is here: Virutal Camera on CPU

Hardware	Single-thread	Multi-thread
Apple M1	1.5ms	0.5ms
Intel Xeon Platinum 8163 @ 2.5 GHz	5.5ms	1.2ms
Nv V100	-	TODO

Visualization

OpenLane

Full-length (10 mins) video of OpenLane is here: Video or you can find in https://www.youtube.com/watch?v=Mqh0N2cOctM

Apollo 3D Lane Synthetic

You can watch video of Apollo 3D Lane Synthetic in https://www.youtube.com/watch?v=WC36c4wO_QM