Robust Collaborative 3D Object Detection in Presence of Pose Errors
Paper | Video ｜ Readme Chinese Ver. ｜ Readme English Ver.
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Dataset Support
- OPV2V
- V2X-Sim 2.0
- DAIR-V2X
- V2XSet
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SOTA collaborative perception method support
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Visualization support
- BEV visualization
- 3D visualization
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1-round/2-round communication support
- transform point cloud first (2-round communication)
- warp feature map (1-round communication, by default in this repo.)
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Pose error simulation support
Please visit the feishu docs CoAlign Installation Guide Chinese Ver. or English Ver. to learn how to install and run this repo.
Or you can refer to OpenCOOD data introduction and OpenCOOD installation guide to prepare data and install CoAlign. The installation is totally the same as OpenCOOD, except some dependent packages required by CoAlign.
Originally DAIR-V2X only annotates 3D boxes within the range of camera's view in vehicle-side. We supplement the missing 3D box annotations to enable the 360 degree detection. With fully complemented vehicle-side labels, we regenerate the cooperative labels for users, which follow the original cooperative label format.
| Original Annotations | Complemented Annotations |
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Download: Google Drive
Website: Website
@article{lu2022robust,
title={Robust Collaborative 3D Object Detection in Presence of Pose Errors},
author={Lu, Yifan and Li, Quanhao and Liu, Baoan and Dianati, Mehrdad and Feng, Chen and Chen, Siheng and Wang, Yanfeng},
journal={arXiv preprint arXiv:2211.07214},
year={2022}
}
This project is impossible without the code of OpenCOOD, g2opy and d3d!
Thanks again to @DerrickXuNu for the great code framework.