kin-zhang/OpenSceneFlow

💞 If you find OpenSceneFlow useful to your research, please cite our works 📖 and give a star 🌟 as encouragement. (੭ˊ꒳​ˋ)੭✧

OpenSceneFlow is a codebase for point cloud scene flow estimation. Please check the usage on KTH-RPL/OpenSceneFlow. Here we upload our demo data and checkpoint for the community.

🎁 One repository, All methods!

You can try following methods in our OpenSceneFlow without any effort to make your own benchmark.

Officially:

• TeFlow (Ours 🚀): CVPR 2026
• DeltaFlow (Ours 🚀): NeurIPS 2025, spotlight
• HiMo (SeFlow++) (Ours 🚀): T-RO 2025
• VoteFlow: CVPR 2025
• SSF (Ours 🚀): ICRA 2025
• Flow4D: RA-L 2025
• SeFlow (Ours 🚀): ECCV 2024
• DeFlow (Ours 🚀): ICRA 2024

Reoriginse to our codebase:

• FastFlow3D: RA-L 2021, a basic backbone model.
• ZeroFlow: ICLR 2024, their pre-trained weight can covert into our format easily through the script.
• NSFP: NeurIPS 2021, faster 3x than original version because of our CUDA speed up, same (slightly better) performance.
• FastNSF: ICCV 2023. SSL Optimization-based.
• ICP-Flow: CVPR 2024. SSL Optimization-based.
• Floxels: CVPR 2025. SSL optimization-based. coding now but not yet ready for release as lower performance than reported. check branch code for more details.
• EulerFlow: ICLR 2025. SSL optimization-based. In my plan, haven't coding yet.

Notes

The tree of uploaded files:

• [ModelName_best].ckpt: means the model evaluated in the public leaderboard page provided by authors or our retrained with the best parameters.
• demo-data-v2.zip: 1.2GB, a mini-dataset for user to quickly run train/val code. Check usage in this section.
• waymo_map.tar.gz: to successfully process waymo data with ground segmentation included to unified h5 file. Check usage in this README.
• demo_data.zip: 1st version (will deprecated later) 613Mb, a mini-dataset for user to quickly run train/val code. Check usage in this section.

Cite Us

OpenSceneFlow is designed by Qingwen Zhang from DeFlow and SeFlow project. If you find it useful, please cite our works:

@inproceedings{zhang2026teflow,
  title = {{TeFlow}: Enabling Multi-frame Supervision for Self-Supervised Feed-forward Scene Flow Estimation},
  author={Zhang, Qingwen and Jiang, Chenhan and Zhu, Xiaomeng and Miao, Yunqi and Zhang, Yushan and Andersson, Olov and Jensfelt, Patric},
  year = {2026},
  booktitle = {Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
  pages = {},
}
@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{zhang2025himo,
  title={{HiMo}: High-Speed Objects Motion Compensation in Point Cloud},
  author={Zhang, Qingwen and Khoche, Ajinkya and Yang, Yi and Ling, Li and Mansouri, Sina Sharif and Andersson, Olov and Jensfelt, Patric},
  journal={IEEE Transactions on Robotics}, 
  year={2025},
  volume={41},
  pages={5896-5911},
  doi={10.1109/TRO.2025.3619042}
}
@inproceedings{zhang2025deltaflow,
  title={{DeltaFlow}: An Efficient Multi-frame Scene Flow Estimation Method},
  author={Zhang, Qingwen and Zhu, Xiaomeng and Zhang, Yushan and Cai, Yixi and Andersson, Olov and Jensfelt, Patric},
  booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems},
  year={2025},
  url={https://openreview.net/forum?id=T9qNDtvAJX}
}

And our excellent collaborators works contributed to this codebase also:

@article{khoche2026dogflow,
  author={Khoche, Ajinkya and Zhang, Qingwen and Cai, Yixi and Mansouri, Sina Sharif and Jensfelt, Patric},
  journal = {IEEE Robotics and Automation Letters},
  title = {{DoGFlow}: Self-Supervised LiDAR Scene Flow via Cross-Modal Doppler Guidance},
  year = {2026},
  volume = {11},
  number = {3},
  pages = {3836-3843},
  doi = {10.1109/LRA.2026.3662592},
}
@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}
}
@inproceedings{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},
  booktitle={2025 IEEE International Conference on Robotics and Automation (ICRA)}, 
  year={2025},
  pages={6394-6400},
  doi={10.1109/ICRA55743.2025.11128770}
}
@inproceedings{lin2025voteflow,
  title={VoteFlow: Enforcing Local Rigidity in Self-Supervised Scene Flow},
  author={Lin, Yancong and Wang, Shiming and Nan, Liangliang and Kooij, Julian and Caesar, Holger},
  booktitle={CVPR},
  year={2025},
}

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