![]() The ultimate objective of encoding human body is to extract the various joints of a predefined skeleton in a simplified manner. Human behavior recognition, as a fundamental research problem, is an extremely significant component and extensively studied research subject in computer vision. The size measurement of human body based on the depth camera is a safe and non-contact fast measurement method, which overcome the challenges of high cost and bulky electronic scanners. The health monitoring system using the depth information can check the diseased parts of the human body to facilitate the guidance of rehabilitation training. The 3D segmentation technology of human body is the most critical technology in applications such as digital clothing and computer animation. For example, 3D human reconstruction is the process of recovering a 3D human surface model by finding the accurate correspondence between frames. Many human-centered tasks based on depth camera had been investigated in the last few years, as shown in Figure 1. The depth camera can provide the ranging information from a single depth image or a point cloud for a variety of applications, such as gaming, three-dimensional (3D) reconstruction and object recognition. Finally, we conclude the challenges involved and problems to be solved in future researches. Moreover, this review helps further understand the pertinent applications in many frontier research directions. The widely used datasets in the field are summarized, and quantitative comparisons are provided for the representative methods. Especially, the significant works are highlighted with a detailed introduction to analyze their characteristics and limitations. The existing works are divided into three categories based on their working principles, including template-based method, feature-based method and machine learning-based method. In this review, we summarize the recent development on the point cloud-based pose estimation of the human body. Although many depth-based researches have been classified and generalized in previous review or survey papers, the point cloud-based pose estimation of human body is still difficult due to the disorder and rotation invariance of the point cloud. Joint estimation of the human body is suitable for many fields such as human–computer interaction, autonomous driving, video analysis and virtual reality.
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