Institute of Modern Optics, Nankai University, Tianjin 300350, China.
Angle AI (Tianjin) Technology Company Ltd., Tianjin 300450, China.
Sensors (Basel). 2021 Mar 1;21(5):1684. doi: 10.3390/s21051684.
Joint estimation of the human body is suitable for many fields such as human-computer interaction, autonomous driving, video analysis and virtual reality. 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. In this review, we summarize the recent development on the point cloud-based pose estimation of the human body. 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. Especially, the significant works are highlighted with a detailed introduction to analyze their characteristics and limitations. The widely used datasets in the field are summarized, and quantitative comparisons are provided for the representative methods. Moreover, this review helps further understand the pertinent applications in many frontier research directions. Finally, we conclude the challenges involved and problems to be solved in future researches.
人体联合估计适用于人机交互、自动驾驶、视频分析和虚拟现实等多个领域。尽管之前的综述或调查论文已经对许多基于深度的研究进行了分类和概括，但由于点云的无序性和旋转不变性，基于点云的人体姿态估计仍然具有挑战性。在这篇综述中，我们总结了基于点云的人体姿态估计的最新进展。现有的工作根据其工作原理分为三类，包括基于模板的方法、基于特征的方法和基于机器学习的方法。特别是，我们重点介绍了重要的工作，详细分析了它们的特点和局限性。我们总结了该领域广泛使用的数据集，并为代表性方法提供了定量比较。此外，本综述有助于进一步了解许多前沿研究方向中的相关应用。最后，我们总结了未来研究中涉及的挑战和需要解决的问题。
