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 defined in which we explicitly model the effects of occlusion regions on the stereo solution. We develop a dynamic programming algorithm that finds matches and occlusions simultaneously. We show that while some cost must be assigned to unmatched pixels, our algorithm's occlusion-cost sensitivity and algorithmic complexity can be significantly 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.