Noah
49 lines
1.8 KiB
Markdown
49 lines
1.8 KiB
Markdown
# LidarGait++: Learning Local Features and Size Awareness from LiDAR Point Clouds for 3D Gait Recognition
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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.
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## Prepare dataset
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**SUSTech1K**:
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- Step 1. Apply for [SUSTech1K](https://lidargait.github.io/).
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**FreeGait** (Optional):
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- Step 1. Download [FreeGait](https://drive.google.com/drive/folders/1I9zOCmqUuBUcOmvO1cgZtUC6uSfmAq7h) first.
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- Then rearrange the folder structure like SUSTech1K/CASIA-B to fit OpenGait framework.
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```
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python datasets/FreeGait/rearrange_freegait.py --input_path yout_freegait_path
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```
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## Train
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To train on SUSTech1K, run
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```
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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
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```
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or train on FreeGait, run
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```
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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
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```
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## Citation
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```bibtex
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@inproceedings{shen2023lidargait,
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title={Lidargait: Benchmarking 3d gait recognition with point clouds},
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author={Shen, Chuanfu and Fan, Chao and Wu, Wei and Wang, Rui and Huang, George Q and Yu, Shiqi},
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booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
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pages={1054--1063},
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year={2023}
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}
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@inproceedings{shen2025lidargait++,
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title={LidarGait++: Learning Local Features and Size Awareness from LiDAR Point Clouds for 3D Gait Recognition},
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author={Shen, Chuanfu and Wang, Rui and Duan, Lixin and Yu, Shiqi},
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booktitle={Proceedings of the Computer Vision and Pattern Recognition Conference},
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pages={6627--6636},
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year={2025}
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}
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```
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