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

    Authors

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

    Comments

    Authors: contact us about adding a copy of your work at [email protected]

    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
     

    Accessibility Statement

    This item was created or digitized prior to April 24, 2027, or is a reproduction of legacy media created before that date. It is preserved in its original, unmodified state specifically for research, reference, or historical recordkeeping. In accordance with the ADA Title II Final Rule, the University Libraries provides accessible versions of archival materials upon request. To request an accommodation for this item, please submit an accessibility request form.