A 3D-point-cloud feature for human-pose estimation

Abstract

Estimating human poses is an important step towards developing robots that can understand human motions and improving their cognitive capabilities. This paper presents a geometric feature for estimating human poses from a 3D point cloud input. The proposed feature can be considered as an extension of the idea of visual features, such as color/edge, of color/grayscale images, and it contains the geometric structure of the point cloud. It is derived by arranging the 3D points into a tree structure, which preserves the global and local properties of the 3D points. Shown experimentally, the tree structure (spatial ordering) is particularly important for estimating human poses (i.e., articulated objects). The 3D orientation (pan, tilt and yaw angles) and shape features are then extracted from each node in the tree to describe the geometric distribution of the 3D points. The proposed feature has been evaluated on a benchmark dataset and compared with two existing geometric features. Experimental results show that the proposed feature has the lowest overall error in human-pose estimation.