Temporally Consistent Motion Segmentation from RGB-D Video

Bertholet, Peter-Immanuel; Zwicker, Matthias (2016). Temporally Consistent Motion Segmentation from RGB-D Video Cornell University Library

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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.

Item Type:

Report (Report)

Division/Institute:

08 Faculty of Science > Institute of Computer Science (INF) > Computer Graphics Group (CGG)
08 Faculty of Science > Institute of Computer Science (INF)

UniBE Contributor:

Bertholet, Peter-Immanuel, Zwicker, Matthias

Subjects:

000 Computer science, knowledge & systems
500 Science > 510 Mathematics

Publisher:

Cornell University Library

Language:

English

Submitter:

Matthias Zwicker

Date Deposited:

22 Aug 2016 10:58

Last Modified:

05 Dec 2022 14:57

ArXiv ID:

1608.04642

BORIS DOI:

10.7892/boris.86116

URI:

https://boris.unibe.ch/id/eprint/86116

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