Title

Spatial-Temporal Fusion for High Accuracy Depth Maps Using Dynamic MRFs

Authors

Authors

J. J. Zhu; L. Wang; J. Z. Gao;R. G. Yang

Comments

Authors: contact us about adding a copy of your work at STARS@ucf.edu

Abbreviated Journal Title

IEEE Trans. Pattern Anal. Mach. Intell.

Keywords

Stereo; MRFs; time-of-flight sensor; data fusion; global optimization; MARKOV RANDOM-FIELDS; BELIEF PROPAGATION; GRAPH CUTS; STEREO; VISION; Computer Science, Artificial Intelligence; Engineering, Electrical &; Electronic

Abstract

Time-of-flight range sensors and passive stereo have complimentary characteristics in nature. To fuse them to get high accuracy depth maps varying over time, we extend traditional spatial MRFs to dynamic MRFs with temporal coherence. This new model allows both the spatial and the temporal relationship to be propagated in local neighbors. By efficiently finding a maximum of the posterior probability using Loopy Belief Propagation, we show that our approach leads to improved accuracy and robustness of depth estimates for dynamic scenes.

Journal Title

Ieee Transactions on Pattern Analysis and Machine Intelligence

Volume

32

Issue/Number

5

Publication Date

1-1-2010

Document Type

Article

Language

English

First Page

899

Last Page

909

WOS Identifier

WOS:000275569300010

ISSN

0162-8828

Share

COinS