Scene Flow Models for Autonomous Driving Dataset

opensceneflow

πŸ’ž If you find OpenSceneFlow useful to your research, please cite our works πŸ“– and give a star 🌟 as encouragement. (ΰ©­ΛŠκ’³β€‹Λ‹)੭✧

OpenSceneFlow is a codebase for point cloud scene flow estimation. Please check the usage on KTH-RPL/OpenSceneFlow.

The files we included and all test result reports can be found v2 leaderboard and v1 leaderboard.

  • [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: 613Mb, 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.
🎁 One repository, All methods! You can try following methods in our code without any effort to make your own benchmark.
  • SSF (Ours πŸš€): ICRA 2025
  • Flow4D: RA-L 2025
  • SeFlow (Ours πŸš€): ECCV 2024
  • DeFlow (Ours πŸš€): ICRA 2024
  • FastFlow3d: RA-L 2021
  • 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. Done coding, public after review.
  • FastNSF: ICCV 2023. Done coding, public after review.
  • ... more on the way

Cite Us

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

@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:

@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!

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