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https://github.com/tusen-ai/SST
Code for a series of work in LiDAR perception, including SST (CVPR 22), FSD (NeurIPS 22), FSD++ (TPAMI 23), FSDv2, and CTRL (ICCV 23, oral).
https://github.com/tusen-ai/SST
3d-object-detection autonomous-driving pytorch
Last synced: 7 days ago
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Code for a series of work in LiDAR perception, including SST (CVPR 22), FSD (NeurIPS 22), FSD++ (TPAMI 23), FSDv2, and CTRL (ICCV 23, oral).
- Host: GitHub
- URL: https://github.com/tusen-ai/SST
- Owner: tusen-ai
- License: apache-2.0
- Created: 2021-12-10T09:28:06.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2024-05-21T13:40:08.000Z (6 months ago)
- Last Synced: 2024-07-31T05:06:38.552Z (3 months ago)
- Topics: 3d-object-detection, autonomous-driving, pytorch
- Language: Python
- Homepage:
- Size: 4.62 MB
- Stars: 768
- Watchers: 15
- Forks: 98
- Open Issues: 34
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- Awesome-BEV-Perception - project
README
### 🔥 We release the code of CTRL, the first open-sourced LiDAR-based auto-labeling system. See [ctrl_instruction](https://github.com/tusen-ai/SST/blob/main/docs/CTRL_instructions.md).
### 🔥 We release FSDv2. Better performance, easier use! Support Waymo, nuScenes, and Argoverse 2. See [fsdv2_instruction](https://github.com/tusen-ai/SST/blob/main/docs/fsdv2_instructions.md).
---
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](https://arxiv.org/abs/2112.06375) (CVPR 2022).
- [Fully Sparse 3D Object Detection](http://arxiv.org/abs/2207.10035) (NeurIPS 2022).
- [Super Sparse 3D Object Detection](http://arxiv.org/abs/2301.02562) (TPAMI 2023).
- [Once Detected, Never Lost: Surpassing Human Performance in Offline LiDAR based 3D Object Detection](https://arxiv.org/abs/2304.12315) (ICCV 2023, Oral).
- [FSD V2: Improving Fully Sparse 3D Object Detection with Virtual Voxels](https://arxiv.org/abs/2308.03755).
Users could follow the instructions in [docs](https://github.com/tusen-ai/SST/blob/main/docs) to use this repo.
**NEWS**
- [23-08-08] The code of FSDv2 is merged into this repo.
- [23-07-14] CTRL is aceepted at ICCV 2023.
- [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](https://github.com/tusen-ai/SST/blob/main/instructions.md).
- [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++ (TPAMI version) is accidentally excluded in Google Scholar search results, if possible, please kindly use the following bibtex**.
```
@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}
}
```
```
@article{fan2023fsdv2,
title={FSD V2: Improving Fully Sparse 3D Object Detection with Virtual Voxels},
author={Fan, Lue and Wang, Feng and Wang, Naiyan and Zhang, Zhaoxiang},
journal={arXiv preprint arXiv:2308.03755},
year={2023}
}
```
## Acknowledgments
This project is based on the following codebases.
* [MMDetection3D](https://github.com/open-mmlab/mmdetection3d)
* [LiDAR-RCNN](https://github.com/TuSimple/LiDAR_RCNN)
Thank the authors of CenterPoint for providing their detailed results.