Spatial-Temporal Fusion for High Accuracy Depth Maps Using Dynamic MRFs
Abbreviated Journal Title
IEEE Trans. Pattern Anal. Mach. Intell.
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
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.
Ieee Transactions on Pattern Analysis and Machine Intelligence
"Spatial-Temporal Fusion for High Accuracy Depth Maps Using Dynamic MRFs" (2010). Faculty Bibliography 2010s. 1022.