Estimating disparity and occlusions in stereo video sequences

We propose an algorithm for estimating disparity and occlusion in stereo video sequences. The algorithm defines a prior on sequences of disparity maps using a 3D Markov random field, and approximately computes the MAP estimate for the disparity sequence using loopy belief propagation. In contrast to previous work on temporal stereo, the algorithm (i) correctly models half-occlusions - scene points visible in one camera but not the other - and (ii) enforces the so-called "monotonicity constraint" on the boundary of half-occluded regions. The algorithm is also able to exploit temporal coherence more appropriately than many previous approaches to temporal stereo, by employing additional states in the Markov random field. These additional states permit rudimentary motion estimation to be performed as part of the belief propagation, thus improving the quality of temporal inference. Parameters of the algorithm are learned from the ground truth disparities of a real stereo sequence. Qualitative results are shown on real sequences, including comparisons with competing approaches, and the performance of the algorithm is assessed quantitatively using the ground truth data.