Update README.md

README.md

@@ -2,8 +2,12 @@
 license: bsd-3-clause
 language:
 - en
+tags:
+- scene-flow
+- point-cloud
+- codebase
+- 3d-vision
 ---
-# Scene Flow Models for Autonomous Driving Dataset
 
 <p align="center">
     <a href="https://github.com/KTH-RPL/OpenSceneFlow">
@@ -15,36 +19,43 @@ language:
 
 💞 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).
+[*OpenSceneFlow*](https://github.com/KTH-RPL/OpenSceneFlow) is a codebase for point cloud scene flow estimation. 
+Please check the usage on [KTH-RPL/OpenSceneFlow](https://github.com/KTH-RPL/OpenSceneFlow). 
+Here we upload our demo data and checkpoint for the community.
 
-<!-- - [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).
+## 🎁 One repository, All methods!
 
+You can try following methods in [our OpenSceneFlow](https://github.com/KTH-RPL/OpenSceneFlow) without any effort to make your own benchmark.
 
-<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.
-
+Officially:
+- [x] [HiMo (SeFlow++)](https://arxiv.org/abs/2503.00803): T-RO 2025
+- [x] [VoteFlow](https://arxiv.org/abs/2503.22328): CVPR 2025
 - [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
+
+<details> <summary> Reoriginse to our codebase:</summary>
+  
 - [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.
+- [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/OpenSceneFlow/tools/zerof2ours.py).
+- [x] [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/OpenSceneFlow/assets/cuda/README.md), same (slightly better) performance. Done coding, public after review.
+- [x] [FastNSF](https://arxiv.org/abs/2304.09121): ICCV 2023. Done coding, public after review.
 - [ ] ... more on the way
 
 </details>
 
+## 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](https://huggingface.co/kin-zhang/OpenSceneFlow/blob/main/demo_data.zip): 1.2GB, 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).
+* [demo_data.zip](https://huggingface.co/kin-zhang/OpenSceneFlow/blob/main/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](https://github.com/KTH-RPL/OpenSceneFlow?tab=readme-ov-file#1-run--train).
+
+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).
+
 ## 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:
@@ -67,8 +78,8 @@ You can try following methods in our code without any effort to make your own be
   pages={2105-2111},
   doi={10.1109/ICRA57147.2024.10610278}
 }
-@article{zhang2025himu,
-    title={HiMo: High-Speed Objects Motion Compensation in Point Cloud},
+@article{zhang2025himo,
+    title={HiMo: High-Speed Objects Motion Compensation in Point Clouds},
     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},
@@ -78,6 +89,12 @@ You can try following methods in our code without any effort to make your own be
 And our excellent collaborators works as followings:
 
 ```bibtex
+@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},
+}
 @article{kim2025flow4d,
   author={Kim, Jaeyeul and Woo, Jungwan and Shin, Ukcheol and Oh, Jean and Im, Sunghoon},
   journal={IEEE Robotics and Automation Letters},