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.