Temporally Consistent Motion Segmentation from RGB-D Video
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Description
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.
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.
Date of Publication
2016-08
Publication Type
Report
Language(s)
en
Additional Credits
Publisher
Cornell University Library
Access(Rights)
open.access