Lei Yang · Kaicheng Yu · Tao Tang · Jun Li · Kun Yuan · Li Wang · Xinyu Zhang · Peng Chen

BEVHeight is a new vision-based 3D object detector specially designed for roadside scenario. BEVHeight surpasses BEVDepth base- line by a margin of 4.85% and 4.43% on DAIR-V2X-I and Rope3D benchmarks under the traditional clean settings, and by 26.88% on robust settings where external camera parameters changes. We hope our work can shed light on studying more effective feature representation on roadside perception.

• [2023/03/15] Both arXiv and codebase are released!
• [2023/02/27] BEVHeight got accepted to CVPR 2023!

• Release the pretrained models
• Support train and test on a custom dataset

• Installation
• Prepare Dataset

Train BEVHeight with 8 GPUs

python [EXP_PATH] --amp_backend native -b 8 --gpus 8

Eval BEVHeight with 8 GPUs

python [EXP_PATH] --ckpt_path [CKPT_PATH] -e -b 8 --gpus 8

• DAIR-V2X-I Dataset

This project is not possible without the following codebases.

• BEVDepth
• DAIR-V2X
• pypcd

If you use BEVHeight in your research, please cite our work by using the following BibTeX entry:

@inproceedings{yang2023bevheight,
    title={BEVHeight: A Robust Framework for Vision-based Roadside 3D Object Detection},
    author={Yang, Lei and Yu, Kaicheng and Tang, Tao and Li, Jun and Yuan, Kun and Wang, Li and Zhang, Xinyu and Chen, Peng},
    booktitle={IEEE/CVF Conf.~on Computer Vision and Pattern Recognition (CVPR)},
    month = mar,
    year={2023}
}