  We present a method for temporally consistent motion segmentation from RGB-D
videos assuming a piecewise rigid motion model. We formulate global energies
over entire RGB-D sequences in terms of the segmentation of each frame into a
number of objects, and the rigid motion of each object through the sequence. We
develop a novel initialization procedure that clusters feature tracks obtained
from the RGB data by leveraging the depth information. We minimize the energy
using a coordinate descent approach that includes novel techniques to assemble
object motion hypotheses. A main benefit of our approach is that it enables us
to fuse consistently labeled object segments from all RGB-D frames of an input
sequence into individual 3D object reconstructions.
