Date of Original Version



Technical Report

Rights Management

All Rights Reserved

Abstract or Description

Abstract: "This paper presents a stereo matching method which uses multiple stereo pairs with various baselines to obtain precise depth estimates without suffering from ambiguity. In stereo processing, a short baseline means that the estimated depth will be less precise due to narrow triangulation. For more precise depth estimation, a longer baseline is desired. With a longer baseline, however, a larger disparity range must be searched to find a match. As a result, matching is more difficult and there is a greater possibility of a false match. So there is a trade-off between precision and accuracy in matching.The stereo matching method presented in this paper uses multiple stereo pairs with different baselines generated by a lateral displacement of a camera. Matching is performed simply by computing the sum of squared-difference (SSD) values. The SSD functions for individual stereo pairs are represented with respect to the inverse depth (rather than the disparity, as is usually done), and then are simply added to produce the sum of SSDs. This resulting function is called the SSSD-in-inverse-depth. We show that the SSSD-in-inverse-depth function exhibits a unique and clear minimum at the correct matching position even when the underlying intensity patterns of the scene include ambiguities or repetitive patterns.An advantage of this method is that we can eliminate false matches and increase precision without any search or sequential filtering. This paper first defines a stereo algorithm based on the SSSD-in-inverse-depth and presents a mathematical analysis to show how the algorithm can remove ambiguity and increase precision. Then, a few experimental results with real stereo images are presented to demonstrate the effectiveness of the algorithm."