README.md

---
license: bsd-3-clause
language:
- en
---
# Scene Flow Models for Autonomous Driving Dataset

<p align="center">
    <a href="https://github.com/KTH-RPL/OpenSceneFlow">
    <picture>
    <img alt="opensceneflow" src="https://github.com/KTH-RPL/OpenSceneFlow/blob/main/assets/docs/logo.png?raw=true" width="600">
    </picture><br>
    </a>
</p>

💞 If you find [*OpenSceneFlow*](https://github.com/KTH-RPL/OpenSceneFlow) useful to your research, please cite [**our works** 📖](#cite-us) and give [a star 🌟](https://github.com/KTH-RPL/OpenSceneFlow) as encouragement. (੭ˊ꒳​ˋ)੭✧

OpenSceneFlow is a codebase for point cloud scene flow estimation. 
Please check the usage on [KTH-RPL/OpenSceneFlow](https://github.com/KTH-RPL/OpenSceneFlow).

<!-- - [DeFlow](https://arxiv.org/abs/2401.16122): Supervised learning scene flow, model included is trained on Argoverse 2.
- [SeFlow](https://arxiv.org/abs/2407.01702): **Self-Supervised** learning scene flow, model included is trained on Argoverse 2. Paper also reported Waymo result, the weight cannot be shared according to [Waymo Term](https://waymo.com/open/terms/). More detail discussion [issue 8](https://github.com/KTH-RPL/SeFlow/issues/8#issuecomment-2464224813).
- [SSF](https://arxiv.org/abs/2501.17821): Supervised learning long-range scene flow, model included is trained on Argoverse 2.
- [Flow4D](https://ieeexplore.ieee.org/document/10887254): Supervised learning 4D network scene flow, model included is trained on Argoverse 2. -->

The files we included and all test result reports can be found [v2 leaderboard](https://github.com/KTH-RPL/DeFlow/discussions/6) and [v1 leaderboard](https://github.com/KTH-RPL/DeFlow/discussions/2).
* [ModelName_best].ckpt: means the model evaluated in the public leaderboard page provided by authors or our retrained with the best parameters.
* [demo_data.zip](https://huggingface.co/kin-zhang/OpenSceneFlow/blob/main/demo_data.zip): 613Mb, a mini-dataset for user to quickly run train/val code. Check usage in [this section](https://github.com/KTH-RPL/SeFlow?tab=readme-ov-file#1-run--train).
* [waymo_map.tar.gz](https://huggingface.co/kin-zhang/OpenSceneFlow/blob/main/waymo_map.tar.gz): to successfully process waymo data with ground segmentation included to unified h5 file. Check usage in [this README](https://github.com/KTH-RPL/SeFlow/blob/main/dataprocess/README.md#waymo-dataset).


<details> <summary>🎁 <b>One repository, All methods!</b> </summary>
<!-- <br> -->
You can try following methods in our code without any effort to make your own benchmark.

- [x] [SSF](https://arxiv.org/abs/2501.17821) (Ours 🚀): ICRA 2025
- [x] [Flow4D](https://ieeexplore.ieee.org/document/10887254): RA-L 2025
- [x] [SeFlow](https://arxiv.org/abs/2407.01702) (Ours 🚀): ECCV 2024
- [x] [DeFlow](https://arxiv.org/abs/2401.16122) (Ours 🚀): ICRA 2024
- [x] [FastFlow3d](https://arxiv.org/abs/2103.01306): RA-L 2021
- [x] [ZeroFlow](https://arxiv.org/abs/2305.10424): ICLR 2024, their pre-trained weight can covert into our format easily through [the script](https://github.com/KTH-RPL/SeFlow/tools/zerof2ours.py).
- [ ] [NSFP](https://arxiv.org/abs/2111.01253): NeurIPS 2021, faster 3x than original version because of [our CUDA speed up](https://github.com/KTH-RPL/SeFlow/assets/cuda/README.md), same (slightly better) performance. Done coding, public after review.
- [ ] [FastNSF](https://arxiv.org/abs/2304.09121): ICCV 2023. Done coding, public after review.
- [ ] ... more on the way

</details>

## Cite Us

*OpenSceneFlow* is designed by [Qingwen Zhang](https://kin-zhang.github.io/) from DeFlow and SeFlow project. If you find it useful, please cite our works:

```bibtex
@inproceedings{zhang2024seflow,
  author={Zhang, Qingwen and Yang, Yi and Li, Peizheng and Andersson, Olov and Jensfelt, Patric},
  title={{SeFlow}: A Self-Supervised Scene Flow Method in Autonomous Driving},
  booktitle={European Conference on Computer Vision (ECCV)},
  year={2024},
  pages={353–369},
  organization={Springer},
  doi={10.1007/978-3-031-73232-4_20},
}
@inproceedings{zhang2024deflow,
  author={Zhang, Qingwen and Yang, Yi and Fang, Heng and Geng, Ruoyu and Jensfelt, Patric},
  booktitle={2024 IEEE International Conference on Robotics and Automation (ICRA)}, 
  title={{DeFlow}: Decoder of Scene Flow Network in Autonomous Driving}, 
  year={2024},
  pages={2105-2111},
  doi={10.1109/ICRA57147.2024.10610278}
}
@article{zhang2025himu,
    title={HiMo: High-Speed Objects Motion Compensation in Point Cloud},
    author={Zhang, Qingwen and Khoche, Ajinkya and Yang, Yi and Ling, Li and Sina, Sharif Mansouri and Andersson, Olov and Jensfelt, Patric},
    year={2025},
    journal={arXiv preprint arXiv:2503.00803},
}
```

And our excellent collaborators works as followings:

```bibtex
@article{kim2025flow4d,
  author={Kim, Jaeyeul and Woo, Jungwan and Shin, Ukcheol and Oh, Jean and Im, Sunghoon},
  journal={IEEE Robotics and Automation Letters}, 
  title={Flow4D: Leveraging 4D Voxel Network for LiDAR Scene Flow Estimation}, 
  year={2025},
  volume={10},
  number={4},
  pages={3462-3469},
  doi={10.1109/LRA.2025.3542327}
}
@article{khoche2025ssf,
  title={SSF: Sparse Long-Range Scene Flow for Autonomous Driving},
  author={Khoche, Ajinkya and Zhang, Qingwen and Sanchez, Laura Pereira and Asefaw, Aron and Mansouri, Sina Sharif and Jensfelt, Patric},
  journal={arXiv preprint arXiv:2501.17821},
  year={2025}
}
```

Feel free to contribute your method and add your bibtex here by pull request!