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Neural Voting Field for Camera-Space 3D Hand Pose Estimation

CVPR 2023 · 3D vision › Human pose estimation · approach: Depth-based hand pose regression · 10 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use unified 3D dense point-wise voting instead of holistic regressionCamera-space hand pose estimation · FreiHANDCS-MJE: 54.5 -> 47.2Helped
Use unified 3D dense point-wise voting instead of 2D dense pixel-wise votingCamera-space hand pose estimation · FreiHANDCS-MJE: 53.2 -> 47.2Helped
Use hand scale (ablation)CS-NVF · FreiHANDCS-MJE: 47.2 -> 42.4Helped
Pre-train on Comp (ablation)CS-NVF · FreiHANDCS-MJE: 47.2 -> 44.6Helped
Use both hand scale and Comp pre-training (ablation)CS-NVF · FreiHANDCS-MJE: 42.4 -> 39.3Helped
Use 50% of near-surface points with highest voting weight (ablation)CS-NVF · FreiHANDCS-MJE: 40.2 -> 39.3Helped
Increase voting fraction from 50% to 75% (ablation)CS-NVF · FreiHANDCS-MJE: 39.3 -> 39.5Hurt
Increase clamping distance from 5 mm to 10 mm (ablation)CS-NVF · FreiHANDCS-MJE: 39.3 -> 40.0Hurt
Increase KNN from 1024 to 2048 (ablation)CS-NVF · FreiHANDCS-MJE: 39.3 -> 39.3No clear effect
Increase sampling step size from 16 mm to 32 mm (ablation)CS-NVF · FreiHANDCS-MJE: 39.3 -> 42.4Hurt
Increase sampling step size from 16 mm to 32 mm (ablation)CS-NVF · FreiHANDFPS: 34 -> 56Helped
Use NVF for root-relative pose estimation rather than the strongest listed baselineRoot-relative hand pose estimation · HO3DMJE: 24.0 -> 21.8Helped

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