Stereo matching using iterative reliable disparity map expansion in the color–spatial–disparity space

Abstract

In this paper, we propose a new stereo matching algorithm using an iterated graph cuts and mean shift filtering technique. Our algorithm estimates the disparity map progressively through the following two steps. In the first step, with a previously estimated RDM (reliable disparity map) that consists of sparse ground control points, an updated dense disparity map is constructed through a RDM constrained energy minimization framework that can cope with occlusion. The graph cuts technique is employed for the solution of the proposed energy model. In the second step, more accurate and denser RDM is estimated through the disparity crosschecking technique and the mean shift filtering in the CSD (color–spatial–disparity) space. The proposed algorithm expands the reliable disparities in RDM repeatedly through the above two steps until it converges. Experimental results on the standard data set demonstrate that the proposed algorithm achieves comparable performance to the state-of-the-arts, and gives excellent results especially in the areas such as the disparity discontinuous boundaries and occluded regions, where the conventional methods usually suffer.