Stereo Matching Using Iterated Graph Cuts and Mean Shift Filtering

Abstract

In this paper, we propose a new stereo matching algorithm using an iterated graph cuts and mean shift filtering technique. Our algorithm consists of following two steps. In the first step, given an estimated sparse RDM (Reliable Disparity Map), we obtain an updated dense disparity map through a new constrained energy minimization framework that can cope with occlusion. The graph cuts technique is employed for the solution of the proposed stereo model. In the second step, we re-estimate the RDM from the disparity map obtained in the first step. In order to obtain accurate reliable disparities, the crosschecking technique followed by the mean shift filtering in the color-disparity space is introduced. The proposed algorithm expands the 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 good results especially in the areas such as the disparity discontinuous boundaries and occluded regions, where the conventional methods usually suffer.