3D motion segmentation of articulated rigid bodies based on RGB-D data

This paper addresses the problem of motion segmentation of articulated rigid bodies from a single-view RGB-D data sequence. Current methods either perform dense motion segmentation, and consequently are very computational demanding, or rely on sparse 2D feature points, which may not be sufficient to represent the entire scene. In this paper, we advocate the use of 3D semi-dense motion segmentation which also bridges some limitations of standard 2D methods ( e.g . background removal). We cast the 3D motion segmentation problem into a subspace clustering problem, adding an adaptive spectral clustering that estimates the number of object rigid parts. The resultant method has few parameters to adjust, takes less time than the temporal length of the scene and requires no post-processing.