Graph-cut-based stereo matching using image segmentation with symmetrical treatment of occlusions

This paper describes a dense stereo matching algorithm for epipolar rectified images. The method applies colour segmentation on the reference image. Our basic assumptions are that disparity varies smoothly inside a segment, while disparity boundaries coincide with the segment borders. The use of these assumptions makes the algorithm capable of handling large untextured regions, estimating precise depth boundaries and propagating disparity information to occluded regions, which are challenging tasks for conventional stereo methods. We model disparity inside a segment by a planar equation. Initial disparity segments are clustered to form a set of disparity layers, which are planar surfaces that are likely to occur in the scene. Assignments of segments to disparity layers are then derived by minimization of a global cost function. This cost function is based on the observation that occlusions cannot be dealt with in the domain of segments. Therefore, we propose a novel cost function that is defined on two levels, one representing the segments and the other corresponding to pixels. The basic idea is that a pixel has to be assigned to the same disparity layer as its segment, but can as well be occluded. The cost function is then effectively minimized via graph-cuts. In the experimental results, we show that our method produces good-quality results, especially in regions of low texture and close to disparity boundaries. Results obtained for the Middlebury test set indicate that the proposed method is able to compete with the best-performing state-of-the-art algorithms.