A survey of hand pose estimation and hand datasets

Hand pose estimation aims to detect and reconstruct 3D hand skeletons or mesh models from information media such as RGB images, depth maps, point clouds, and voxels. The relevant technologies are mainly applied in fields like humancomputer interaction, motion analysis, augmented reality, and virtual reality. In recent years, with the rapid development of deep learning technology and the widespread application of depth cameras, hand pose estimation has attracted more and more attention. The current state-of-the-art has achieved good performance, but there are still great challenges when facing hand-object interactions, two-handed interactions, and occlusions in outdoor situations. This paper will classify and introduce the characteristics and advantages and disadvantages of various hand pose estimation algorithms according to the type of information media, collect the main datasets and benchmarks used in hand pose estimation, and finally discuss the main challenges and prospects in the field of hand pose estimation.