3D hand pose estimation: methods, datasets, and challenges

3D hand pose estimation is an important task in the computer vision community due to its vast applications, including but not limited to, human computer interaction, virtual reality and augmented reality, sign language recognition, medical image analysis. The challenges for this task lie in high degree of freedom of a human hand, self-occlusions, different hand shapes, ambiguities among different fingers. The obstacles faced by the research communities are lack of proper methods and limitation in the current datasets. In view of this, I investigate in this thesis in three aspects: methods, datasets, and challenges. More specifically, the contributions of this thesis are: (1) Proposed a large-scale hand pose dataset, collected using a novel capture method, the dataset is known as the BigHand2.2M dataset; (2) Hosted a depth-based 3D hand pose challenge that attracted the top research groups across the world to evaluate …