A Stereo Matching Algorithm with an Adaptive Window: Theory and Experiment

Abstract

An iterative stereo matching algorithm is presented which selects a window adaptively for each pixel. The selected window is optimal in the sense that it produces the disparity estimate having the least uncertainty after evaluating both the intensity and the disparity variations within a window. The algorithm employs a statistical model that represents uncertainty of disparity of points over the window; the uncertainty is assumed to increase with the distance of the point from the center point. The algorithm is completely local and does not include any global optimization. Also, the algorithm does not use any post-processing smoothing, but smooth surfaces are recovered as smooth while sharp disparity edges are retained. Experimental results have demonstrated a clear advantage of this algorithm over algorithms with a fixed-size window, for both synthetic and real images.