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This paper addresses the 3D point cloud reconstruction and 3D pose estimationof the human hand from a single RGB image. To that end, we present a novelpipeline for local and global point cloud reconstruction using a 3D handtemplate while learning a latent representation for pose estimation. Todemonstrate our method, we introduce a new multi-view hand posture dataset toobtain complete 3D point clouds of the hand in the real world. Experiments onour newly proposed dataset and four public benchmarks demonstrate the model'sstrengths. Our method outperforms competitors in 3D pose estimation whilereconstructing realistic-looking complete 3D hand point clouds.

Local and Global Point Cloud Reconstruction for 3D Hand Pose Estimation