Dynamic depth recovery using belief propagation

In this paper, we study the dynamic stereo problem, i.e. to recover the shape of dynamic scene from multiple synchronized image sequences. To incorporate both spatial and temporal information for depth recovery, we propose a statistical framework that uses pixel process model to encode temporal coherence, and Markov random fields (MRFs) for spatial coherence. In this framework, the dynamic depth recovery problem is finally formulated as an optimization problem, and is optimized by using the belief propagation algorithm. Experimental results with the real dynamic scenes illustrate our method´s ability of robust shape recovery.