A Robust Stereo Matching Method for Low Texture Stereo Images

Computing disparity images for stereo pairs of low texture images is a challenging task because matching costs inside low texture areas of the stereo pairs are almost similar. This problem can not be solved straightforwardly by increasing the size of aggregation windows or by using global optimization methods, e.g. dynamic programming, because those approaches will smooth depth discontinued boundaries as well. Based on the assumption that disparities of pixels in homogeneous regions are similar, this paper proposes a new method that is able to robustly perform stereo matching for low texture stereo images. The proposed method utilizes the edge maps computed from the stereo pairs to guide the cost aggregation process in stereo matching. By using edge maps, the proposed method can achieve the effect of using different shapes and sizes of aggregation windows. Moreover, the computational complexity of the proposed method is independent from the window size, similar to the moving average aggregation method. Experimental results from both of an artificial and a real stereo image sequence demonstrate that the proposed method can produce a larger number of and a better accuracy of reliable disparities for low texture stereo images than the moving average method.