  This paper addresses the 3D point cloud reconstruction and 3D pose estimation
of the human hand from a single RGB image. To that end, we present a novel
pipeline for local and global point cloud reconstruction using a 3D hand
template while learning a latent representation for pose estimation. To
demonstrate our method, we introduce a new multi-view hand posture dataset to
obtain complete 3D point clouds of the hand in the real world. Experiments on
our newly proposed dataset and four public benchmarks demonstrate the model's
strengths. Our method outperforms competitors in 3D pose estimation while
reconstructing realistic-looking complete 3D hand point clouds.
