Research map / 3D vision / Human pose estimation
3D Convolutional Neural Networks for Efficient and Robust Hand Pose Estimation From Single Depth Images
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use 3D CNN with projective D-TSDF and 3D data augmentation | single-image hand pose estimation · MSRA dataset | proportion of good frames: about 10%, 12%, 25% and 30% higher | Helped |
| use 3D CNN with projective D-TSDF and 3D data augmentation | single-image hand pose estimation · NYU dataset | proportion of good frames: about 10% more | Helped |
| use 3D CNN with projective D-TSDF and 3D data augmentation | single-image hand pose estimation · NYU dataset | proportion of joints within error threshold: about 10% more | Helped |
| replace a 32×32×32 volume with a 16×16×16 volume (ablation) | 3D CNN · MSRA dataset | pose estimation accuracy | Hurt |
| replace a 32×32×32 volume with a 64×64×64 volume (ablation) | 3D CNN · MSRA dataset | pose estimation accuracy | No clear effect |
| use projective TSDF instead of projective D-TSDF (ablation) | 3D CNN · MSRA dataset | pose estimation accuracy | Hurt |
| use accurate TSDF instead of projective D-TSDF (ablation) | 3D CNN · MSRA dataset | pose estimation accuracy | Hurt |
| add 3D data augmentation (ablation) | 3D CNN with projective D-TSDF · MSRA dataset | pose estimation accuracy | Helped |
Closest papers by meaning
- Hand PointNet: 3D Hand Pose Estimation Using Point Sets (CVPR 2018 · similarity 0.93)
- Depth-Based 3D Hand Pose Estimation: From Current Achievements to Future Goals (CVPR 2018 · similarity 0.93)
- 3D Hand Shape and Pose Estimation From a Single RGB Image (CVPR 2019 · similarity 0.93)
- DeepHand: Robust Hand Pose Estimation by Completing a Matrix Imputed With Deep Features (CVPR 2016 · similarity 0.93)
- Robust 3D Hand Pose Estimation in Single Depth Images: From Single-View CNN to Multi-View CNNs (CVPR 2016 · similarity 0.92)
- Reconstructing Hands in 3D with Transformers (CVPR 2024 · similarity 0.88)
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