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3D Convolutional Neural Networks for Efficient and Robust Hand Pose Estimation From Single Depth Images

CVPR 2017 · 3D vision › Human pose estimation · approach: Depth-based hand pose regression · 311 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
use 3D CNN with projective D-TSDF and 3D data augmentationsingle-image hand pose estimation · MSRA datasetproportion of good frames: about 10%, 12%, 25% and 30% higherHelped
use 3D CNN with projective D-TSDF and 3D data augmentationsingle-image hand pose estimation · NYU datasetproportion of good frames: about 10% moreHelped
use 3D CNN with projective D-TSDF and 3D data augmentationsingle-image hand pose estimation · NYU datasetproportion of joints within error threshold: about 10% moreHelped
replace a 32×32×32 volume with a 16×16×16 volume (ablation)3D CNN · MSRA datasetpose estimation accuracyHurt
replace a 32×32×32 volume with a 64×64×64 volume (ablation)3D CNN · MSRA datasetpose estimation accuracyNo clear effect
use projective TSDF instead of projective D-TSDF (ablation)3D CNN · MSRA datasetpose estimation accuracyHurt
use accurate TSDF instead of projective D-TSDF (ablation)3D CNN · MSRA datasetpose estimation accuracyHurt
add 3D data augmentation (ablation)3D CNN with projective D-TSDF · MSRA datasetpose estimation accuracyHelped

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