基于深度图像的手部姿态估计综述

Depth-based hand pose estimation has received increasing attention in the fields of human-computer interaction and virtual reality. A comprehensive survey and analysis of depth-based hand pose estimation of recent works are conducted. First, the definition and difficulties of this problem are explained, the widely used sensor and public datasets are also introduced. Then, the works of this field are divided into three categories, model-driven, data-driven, and hybrid method. The model-driven methods perform a model fitting between the model and the depth points. The data-driven methods learn a function, which maps the depth image to pose. The hybrid methods combine model-driven and data-driven to recovery the hand pose. In the course of narration, we focus on the solved problems and shortcomings to be solved. In the final, the works are compared in terms of accuracy, suitability, and robustness. The future research in this direction is also discussed.