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Point-to-Point Regression PointNet for 3D Hand Pose Estimation

ECCV 2018 • Liuhao Ge • Zhou Ren • Junsong Yuan

Convolutional Neural Networks (CNNs)-based methods for 3D hand pose estimation with depth cameras usually take 2D depth images as input and directly regress holistic 3D hand pose. Different from these methods, our proposed Point-to-Point Regression PointNet directly takes the 3D point cloud as input and outputs point-wise estimations, i.e., heat-maps and unit vector fields on the point cloud, representing the closeness and direction from every point in the point cloud to the hand joint... (read more)

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METHOD TYPE
PointNet
3D Representations
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