LidarGait++: Learning Local Features and Size Awareness from LiDAR Point Clouds for 3D Gait Recognition

This paper has been accepted by CVPR 2025.

Prepare dataset

SUSTech1K:

• Step 1. Apply for SUSTech1K.

FreeGait (Optional):

• Step 1. Download FreeGait 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

@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}
}