lidargaitv2 open-source

2025-06-11 14:43:19 +08:00
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+# LidarGait++: Learning Local Features and Size Awareness from LiDAR Point Clouds for 3D Gait Recognition
+
+This [paper](https://openaccess.thecvf.com/content/CVPR2025/papers/Shen_LidarGait_Learning_Local_Features_and_Size_Awareness_from_LiDAR_Point_CVPR_2025_paper.pdf) has been accepted by CVPR 2025.
+
+
+
+## Prepare dataset
+**SUSTech1K**: 
+- Step 1. Apply for [SUSTech1K](https://lidargait.github.io/).
+
+**FreeGait** (Optional): 
+
+- Step 1. Download [FreeGait](https://drive.google.com/drive/folders/1I9zOCmqUuBUcOmvO1cgZtUC6uSfmAq7h) first. 
+
+- Then rearrange the folder structure like SUSTech1K/CASIA-B to fit OpenGait framework.
+    ```
+        python datasets/FreeGait/rearrange_freegait.py --input_path yout_freegait_path
+    ```
+
+## Train
+To train on SUSTech1K, run
+```
+CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=4 opengait/main.py --cfgs ./configs/lidargaitv2/lidargaitv2_sustech1k.yaml --phase train
+```
+or train on FreeGait, run
+```
+CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=4 opengait/main.py --cfgs ./configs/lidargaitv2/lidargaitv2_freegait.yaml --phase train
+```
+
+## Citation
+
+```bibtex
+@inproceedings{shen2023lidargait,
+  title={Lidargait: Benchmarking 3d gait recognition with point clouds},
+  author={Shen, Chuanfu and Fan, Chao and Wu, Wei and Wang, Rui and Huang, George Q and Yu, Shiqi},
+  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
+  pages={1054--1063},
+  year={2023}
+}
+
+@inproceedings{shen2025lidargait++,
+  title={LidarGait++: Learning Local Features and Size Awareness from LiDAR Point Clouds for 3D Gait Recognition},
+  author={Shen, Chuanfu and Wang, Rui and Duan, Lixin and Yu, Shiqi},
+  booktitle={Proceedings of the Computer Vision and Pattern Recognition Conference},
+  pages={6627--6636},
+  year={2025}
+}
+```