REGATTE / SST

Codes for “Fully Sparse 3D Object Detection” & “Embracing Single Stride 3D Object Detector with Sparse Transformer”

🔥 We release the code of CTRL, the first open-sourced LiDAR-based auto-labeling system. See ctrl_instruction.

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This repo contains official implementations of our series of work in LiDAR-based 3D object detection:

• Embracing Single Stride 3D Object Detector with Sparse Transformer (CVPR 2022).
• Fully Sparse 3D Object Detection (NeurIPS 2022).
• Super Sparse 3D Object Detection (TPAMI 2023).
• Once Detected, Never Lost: Surpassing Human Performance in Offline LiDAR based 3D Object Detection (ICCV 2023).

Users could follow the instructions in docs to use this repo.

NEWS

• [23-06-21] 🔥 The code of FSD++ (TPAMI version of FSD) is released.
• [23-06-19] 🔥 The code of CTRL is released.
• [23-03-21] The Argoverse 2 model of FSD is released. See instructions.
• [22-09-19] The code of FSD is released here.
• [22-09-15] FSD is accepted at NeurIPS 2022.
• [22-03-02] SST is accepted at CVPR 2022.
• [21-12-10] The code of SST is released.

Citation

Please consider citing our work as follows if it is helpful.

Since FSD is accidentally excluded in Google Scholar search results, if possible, please kindly consider citing the journal version of FSD as well.

@inproceedings{fan2022embracing,
  title={{Embracing Single Stride 3D Object Detector with Sparse Transformer}},
  author={Fan, Lue and Pang, Ziqi and Zhang, Tianyuan and Wang, Yu-Xiong and Zhao, Hang and Wang, Feng and Wang, Naiyan and Zhang, Zhaoxiang},
  booktitle={CVPR},
  year={2022}
}

@inproceedings{fan2022fully,
  title={{Fully Sparse 3D Object Detection}},
  author={Fan, Lue and Wang, Feng and Wang, Naiyan and Zhang, Zhaoxiang},
  booktitle={NeurIPS},
  year={2022}
}

@article{fan2023super,
  title={Super Sparse 3D Object Detection},
  author={Fan, Lue and Yang, Yuxue and Wang, Feng and Wang, Naiyan and Zhang, Zhaoxiang},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2023}
}

@inproceedings{fan2023once,
  title={Once Detected, Never Lost: Surpassing Human Performance in Offline LiDAR based 3D Object Detection},
  author={Fan, Lue and Yang, Yuxue and Mao, Yiming and Wang, Feng and Chen, Yuntao and Wang, Naiyan and Zhang, Zhaoxiang},
  booktitle={ICCV},
  year={2023}
}

Acknowledgments

This project is based on the following codebases.

• MMDetection3D
• LiDAR-RCNN

Thank the authors of CenterPoint for providing their detailed results.