An Adjustment-Free Stereo Matching Algorithm
Ruihua Ma, Monique Thonnat, Marc-Antoine Berthod · 1993
Matching is recognized as the most difficult step in stereo vision. Many algorithms have been proposed but none of them is robust enough. The primary reason for this is that one of the most useful cues, namely shape similarity, is not appropriately exploited. Making full use of shape similarity for disambiguation requires determining an appropriate criterion and an adequate implementation. We show that a disparity gradient (DG) limit as small as 0.2 can be used to ensure shape similarity and permits the majority of scene surfaces to be correctly handled. This DG limit is a compromise between the quantity of matches and the quality of matching. The implementation of a DG limit is made effective by explicitly taking into account uncertainty in image point positions. A very effcient voting scheme is proposed. Many tests have been performed, mostly on complex real scenes, but none of the parameters has been adjusted. The results are satisfactory both in terms of the quantity of matches and the correctness of these in normal circumstances. 1