Update README.md

README.md

@@ -5,18 +5,26 @@ language:
 ---
 # Scene Flow Models for Autonomous Driving Dataset
 
-<!-- Provide a quick summary of what the model is/does. -->
+<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>
 
-Please check the usage on [KTH-RPL/DeFlow](https://github.com/KTH-RPL/DeFlow) or [KTH-RPL/SeFlow](https://github.com/KTH-RPL/SeFlow).
+💞 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✧
 
-- [DeFlow](https://github.com/KTH-RPL/DeFlow): Supervised learning scene flow, model included is trained on Argoverse 2
-- [SeFlow](https://github.com/KTH-RPL/SeFlow): **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).
+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).
-* [seflow_best.ckpt](https://huggingface.co/kin-zhang/OpenSceneFlow/blob/main/seflow_best.ckpt): trained on Argoverse 2 sensor set with self-supervised way.
-* [deflow_best.ckpt](https://huggingface.co/kin-zhang/OpenSceneFlow/blob/main/deflow_best.ckpt): trained on Argoverse 2 sensor set with ground truth using deflowLoss function.
-* [fastflow3d_best.ckpt](https://huggingface.co/kin-zhang/OpenSceneFlow/blob/main/fastflow3d_best.ckpt): trained on Argoverse 2 sensor set with ground truth using ff3dLoss function. 
-* ... more models on the way
+* [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).
 
@@ -25,30 +33,23 @@ The files we included and all test result reports can be found [v2 leaderboard](
 <!-- <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.
-<!-- - [ ] [Flow4D](https://arxiv.org/abs/2407.07995): 1st supervise network in the new leaderboard. Done coding, public after review. -->
 - [ ] ... more on the way
 
 </details>
 
-## Citation
+## Cite Us
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+*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:
 
-```
-@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}
-}
+```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},
@@ -58,4 +59,41 @@ You can try following methods in our code without any effort to make your own be
   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!