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Research map / 3D vision / Human pose estimation

V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation From a Single Depth Map

CVPR 2018 · 3D vision › Human pose estimation · approach: Depth-based hand pose regression · 403 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Replace direct coordinate regression with per-voxel likelihood estimation using 2D depth input (ablation)Pose estimation network · NYU Hand Pose Datasetaverage 3D distance error (mm): 18.85 -> 13.01Helped
Replace 2D depth map input with a 3D voxelized grid for coordinate regression (ablation)Pose estimation network · NYU Hand Pose Datasetaverage 3D distance error (mm): 18.85 -> 16.78Helped
Use voxelized input and per-voxel likelihood output instead of coordinate regression (ablation)V2V-PoseNet · NYU Hand Pose Datasetaverage 3D distance error (mm): 16.78 -> 10.37Helped
Add localization refinement (ablation)V2V-PoseNet · NYU Hand Pose Datasetaverage 3D distance error (mm): 11.14 -> 9.22Helped
Add epoch ensemble (ablation)V2V-PoseNet with localization refinement · NYU Hand Pose Datasetaverage 3D distance error (mm): 9.22 -> 8.42Helped
Use V2V-PoseNet instead of Pose-REN3D hand pose estimation · ICVL Hand Posture Datasetmean error (mm): 6.79 -> 6.28Helped
Use V2V-PoseNet instead of Pose-REN3D hand pose estimation · NYU Hand Pose Datasetmean error (mm): 11.81 -> 8.42Helped
Use V2V-PoseNet instead of Pose-REN3D hand pose estimation · MSRA Hand Pose Datasetmean error (mm): 8.65 -> 7.49Helped
Use V2V-PoseNet instead of REN-9x6x63D human pose estimation · ITOP front-viewmAP (Mean): 84.9 -> 88.74Helped
Use V2V-PoseNet instead of REN-9x6x63D human pose estimation · ITOP top-viewmAP (Mean): 75.5 -> 83.44Helped

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