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}
+}
+```
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+data_cfg:
+  dataset_name: FreeGait
+  dataset_root: your_path
+  dataset_partition: ./datasets/FreeGait/FreeGait.json
+  num_workers: 1 
+  data_in_use: [false, false, true, false, false]
+  remove_no_gallery: false # Remove probe if no gallery for it
+  test_dataset_name: FreeGait
+
+
+evaluator_cfg:
+  enable_float16: false #true #false #true
+  restore_ckpt_strict: true
+  restore_hint: 80000
+  save_name: lidargaitv2
+  eval_func: evaluate_FreeGait 
+  sampler:
+    batch_shuffle: false
+    points_in_use:  # For point-based gait recognition using point clouds
+      pointcloud_index: 0 
+      points_num: 256 #312 #256 #2048
+    batch_size: 8 
+    frames_num_fixed: 10 # fixed frames number for training
+    frames_skip_num: 0
+    #sample_type: fixed_ordered
+    sample_type: all_ordered # all indicates whole sequence used to test, while ordered means input sequence by its natural order; Other options:   fixed_unordered
+    frames_all_limit: 720 # limit the number of sampled frames to prevent out of memory
+  metric: euc # cos
+  transform:
+      - type: PointCloudsTransform
+        xyz_only: true
+        scale_aware: true
+
+loss_cfg:
+  - loss_term_weight: 1.0
+    margin: 0.2
+    type: TripletLoss
+    log_prefix: triplet
+    lazy: False
+  - loss_term_weight: 0.1
+    scale: 31 #16
+    type: CrossEntropyLoss
+    log_prefix: softmax
+    log_accuracy: true
+
+   
+model_cfg:
+  model: LidarGaitPlusPlus
+  pool: PPP_HAP 
+  sampling: knn
+  channel: 32 
+  npoints: [256, 192, 128]
+  nsample: 16
+  scale_aware: true 
+  normalize_dp: true 
+  SeparateFCs:
+    in_channels: 256 
+    out_channels: 256 
+    parts_num: 31 
+  SeparateBNNecks:
+    class_num: 500
+    in_channels: 256 
+    parts_num: 31 
+  scale:
+    - 1
+    - 2
+    - 4
+    - 8
+    - 16
+
+
+optimizer_cfg:
+  lr: 0.1
+  momentum: 0.9
+  solver: SGD
+  weight_decay: 0.0005
+
+scheduler_cfg:
+  T_max: 80000
+  eta_min: 0.0001
+  scheduler: CosineAnnealingLR
+
+
+trainer_cfg:
+  enable_float16: false #true # half_percesion float for memory reduction and speedup
+  fix_BN: false
+  with_test: true 
+  log_iter: 100
+  restore_ckpt_strict: true
+  restore_hint: 0
+  save_iter: 5000
+  save_name: lidargaitv2
+  sync_BN: true
+  total_iter: 80000
+  sampler:
+    batch_shuffle: true
+    batch_size:
+      - 32 # TripletSampler, batch_size[0] indicates Number of Identity
+      - 4 #                 batch_size[1] indicates Samples sequqnce for each Identity
+    frames_num_fixed: 10 # fixed frames number for training
+    sample_type: fixed_unordered # fixed control input frames number, unordered for controlling order of input tensor; Other options: unfixed_ordered or all_ordered
+    type: TripletSampler
+    points_in_use:
+      pointcloud_index: 0
+      points_num: 256
+  transform:
+      - type: PointCloudsTransform
+        xyz_only: true
+        scale_aware: true
+        scale_prob: 0.2 
+        flip_prob: 0.1 
@@ -0,0 +1,110 @@
+data_cfg:
+  dataset_name: SUSTech1K
+  dataset_root: your_path # download from https://lidargait.github.io/ if you don't have dataset
+  dataset_partition: ./datasets/SUSTech1K/SUSTech1K.json
+  num_workers: 4
+  data_in_use: [true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false]
+  remove_no_gallery: false # Remove probe if no gallery for it
+  test_dataset_name: SUSTech1K
+
+evaluator_cfg:
+  enable_float16: true
+  restore_ckpt_strict: true
+  restore_hint: 40000
+  save_name: lidargaitv2
+  eval_func: evaluate_indoor_dataset 
+  sampler:
+    batch_shuffle: false
+    points_in_use: # For point-based gait recognition using point clouds
+      pointcloud_index: 0 
+      points_num: 1024 #2048
+    batch_size: 4 
+    frames_num_fixed: 10 # fixed frames number for training
+    frames_skip_num: 0
+    #sample_type: fixed_ordered
+    sample_type: all_ordered # all indicates whole sequence used to test, while ordered means input sequence by its natural order; Other options:   fixed_unordered
+    frames_all_limit: 720 # limit the number of sampled frames to prevent out of memory
+  metric: euc # cos
+  transform:
+      - type: PointCloudsTransform
+        xyz_only: true
+        scale_aware: true
+
+loss_cfg:
+  - loss_term_weight: 1.0
+    margin: 0.2
+    type: TripletLoss
+    log_prefix: triplet
+    lazy: False
+  - loss_term_weight: 0.1
+    scale: 31
+    type: CrossEntropyLoss
+    log_prefix: softmax
+    log_accuracy: true
+
+   
+model_cfg:
+  model: LidarGaitPlusPlus
+  pool: PPP_HAP 
+  sampling: knn
+  channel: 16 
+  npoints: [512, 256, 128]
+  nsample: 32
+  scale_aware: true 
+  normalize_dp: true 
+  SeparateFCs:
+    in_channels: 256 
+    out_channels: 256 
+    parts_num: 31
+  SeparateBNNecks:
+    class_num: 250
+    in_channels: 256 
+    parts_num: 31 
+  scale:
+    - 1
+    - 2
+    - 4
+    - 8
+    - 16
+
+
+optimizer_cfg:
+  lr: 0.1
+  momentum: 0.9
+  solver: SGD
+  weight_decay: 0.0005
+
+scheduler_cfg:
+  T_max: 40000
+  eta_min: 0.0001
+  scheduler: CosineAnnealingLR
+
+
+trainer_cfg:
+  enable_float16: false #true # half_percesion float for memory reduction and speedup
+  fix_BN: false
+  with_test: true 
+  log_iter: 100
+  restore_ckpt_strict: true
+  restore_hint: 0
+  save_iter: 5000
+  save_name: lidargaitv2
+  sync_BN: true
+  total_iter: 40000
+  sampler:
+    batch_shuffle: true
+    batch_size:
+      - 32 # TripletSampler, batch_size[0] indicates Number of Identity
+      - 4 #                 batch_size[1] indicates Samples sequqnce for each Identity
+    frames_num_fixed: 10 # fixed frames number for training
+    sample_type: fixed_unordered # fixed control input frames number, unordered for controlling order of input tensor; Other options: unfixed_ordered or all_ordered
+    type: TripletSampler
+    points_in_use:
+      pointcloud_index: 0
+      points_num: 1024
+  transform:
+      - type: PointCloudsTransform
+        xyz_only: true
+        scale_aware: true
+        scale_prob: 1
+        flip_prob: 0.15