Update README.md

README.md

@@ -12,8 +12,14 @@ Please check the usage on [KTH-RPL/DeFlow](https://github.com/KTH-RPL/DeFlow) or
 - [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).
 
-DeFlow Result Video Preview:
-https://github.com/KTH-RPL/DeFlow/assets/35365764/9b265d56-06a9-4300-899c-96047a0da505
+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
+* [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> -->
@@ -22,8 +28,8 @@ You can try following methods in our code without any effort to make your own be
 - [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](tools/zerof2ours.py).
-- [ ] [NSFP](https://arxiv.org/abs/2111.01253): NeurIPS 2021, faster 3x than original version because of [our CUDA speed up](assets/cuda/README.md), same (slightly better) performance. 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/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