The problem of stereo vision has been of increasing interest to the computer vision community over the past decade. This paper presents a new computational framework for matching a pair of stereo images arising from viewing the same object from two different positions. In contrast to previous work, this approach formulates the matching problem as detection of a “bright”, coherent disparity surface in a 3D image called the spatio-disparity space (SDS) image. The SDS images represents the goodness of each and every possible match.
A nonlinear filter is proposed for enhancing the disparity surface in the SDS image and for suppressing the noise. This filter is used to construct a hyperpyramid representation of the SDS image. Then the disparity surface is detected using a coarse-to-fine control structure. The proposed method is robust to photometric and geometric distortions in the stereo images, and has a number of computational advantages. It produces good results for complex scenes.