Disparity-Space Images and Large Occlusion Stereo

A new method for solving the stereo matching problem in the presence of large occlusion is presented. A data structure | the disparity space image | is deened in which we explicitly model the eeects of occlusion regions on the stereo solution. We develop a dynamic programming algorithm that nds matches and occlusions simultaneously. We show that while some cost must be assigned to unmatched pixels, our algo-rithm's occlusion-cost sensitivity and algorithmic complexity can be signiicantly reduced when highly-reliable matches, or ground control points, are incorporated into the matching process. The use of ground control points eliminates both the need for biasing the process towards a smooth solution and the task of selecting critical prior probabilities describing image formation.