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LidarGait: Benchmarking 3D Gait Recognition With Point Clouds
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use range-view depth projections with convolutional features instead of point-wise models | LidarGait · SUSTech1K valid + test | Rank-1 accuracy: 64.83 -> 86.77 | Helped |
| use LiDAR range-view depth input instead of LiDAR silhouettes without depth (ablation) | LidarGait · SUSTech1K | Rank-1 accuracy: 64.70% to 86.77% | Helped |
| use bird's-eye-view projection instead of range-view projection (ablation) | LidarGait · SUSTech1K | Rank-1 accuracy: 86.77 -> 26.33 | Hurt |
| use right-side-view projection instead of range-view projection (ablation) | LidarGait · SUSTech1K | Rank-1 accuracy: 86.77 -> 62.67 | Hurt |
| add right-side-view features with frame-level concatenation (ablation) | LidarGait · SUSTech1K | Rank-1 accuracy: +0.73% | Helped |
| add bird's-eye-view features to range-view and right-side-view features (ablation) | MV-LidarGait · SUSTech1K | Rank-1 accuracy: 87.50 -> 87.47 | No clear effect |
| fuse views by frame-level concatenation rather than other fusion strategies (ablation) | MV-LidarGait · SUSTech1K | Rank-1 accuracy | Helped |
Closest papers by meaning
- LidarGait++: Learning Local Features and Size Awareness from LiDAR Point Clouds for 3D Gait Recognition (CVPR 2025 · similarity 0.94)
- SkeletonGait: Gait Recognition Using Skeleton Maps (AAAI 2024 · similarity 0.86)
- BiggerGait: Unlocking Gait Recognition with Layer-wise Representations from Large Vision Models (NeurIPS 2025 · similarity 0.85)
- BigGait: Learning Gait Representation You Want by Large Vision Models (CVPR 2024 · similarity 0.85)
- Vocabulary-Guided Gait Recognition (NeurIPS 2025 · similarity 0.84)
- Cross-Covariate Gait Recognition: A Benchmark (AAAI 2024 · similarity 0.84)
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