PersFormer: 3D Lane Detection via Perspective Transformer and the OpenLane Benchmark
Li Chen^∗†, Chonghao Sima^∗, Yang Li^∗, Zehan Zheng, Jiajie Xu, Xiangwei Geng, Hongyang Li^†, Conghui He, Jianping Shi, Yu Qiao, Junchi Yan.

^∗ equal contributions.
^† corresponding authors
arXiV 2203.11089

This repository is the PyTorch implementation for PersFormer.

PersFormer is an end-to-end monocular 3D lane detector with a novel Transformer-based spatial feature transformation module. Our model generates BEV features by attending to related front-view local regions with camera parameters as a reference. It adopts a unified 2D/3D anchor design and an auxiliary task to detect 2D/3D lanes simultaneously, enhancing the feature consistency and sharing the benefits of multi-task learning.

• Changelog
• Get Started
  • Installation
  • Dataset
  • Training and evaluation
• Benchmark
• Visualization
• Citation
• Acknowledgements
• License

2022-5-9: We compared our method on ONCES_3DLanes Dataset, where PersFormer also outperforms other methods.
2022-4-12: We released the v1.0 code for PersFormer.

• To run PersFormer, make sure you are using a machine with at least one GPU.
• Please follow INSTALL.md to setup the environment.

• Please refer to OpenLane for downloading OpenLane Dataset.
• Please refer to Gen-LaneNet for downloading Apollo 3D Lane Synthetic Dataset.

• Please follow TRAIN_VAL.md to train and evaluate the model.

• 3D Lane Detection Results (F-Score) in OpenLane.

• 2D Lane Detection Results (F-Score) in OpenLane. Note that the baseline of 2D branch in PersFormer is LaneATT.

• 3D Lane Detection Results in ONCE_3DLanes.

Following are the visualization results of PersFormer on OpenLane dataset and Apollo dataset.

• OpenLane visualization results
  
  
• Apollo 3D Synthetic visualization results
  
  

Please use the following citation if you find our repo or our paper PersFormer useful:

@article{chen2022persformer,
  title={PersFormer: 3D Lane Detection via Perspective Transformer and the OpenLane Benchmark},
  author={Chen, Li and Sima, Chonghao and Li, Yang and Zheng, Zehan and Xu, Jiajie and Geng, Xiangwei and Li, Hongyang and He, Conghui and Shi, Jianping and Qiao, Yu and Yan, Junchi},
  journal={arXiv preprint arXiv:2203.11089},
  year={2022}
}  

We would like to acknowledge the great support from SenseBee labelling team at SenseTime Research, and the fruitful discussions and comments for this project from Zhiqi Li, Yuenan Hou, Yu Liu. We thank for the code implementation from Gen-LaneNet, LaneATT and Deformable DETR.

All code within this repository is under Apache License 2.0.