Robust Stereo Matching Under Radiometric Variations Based On Cumulative Distributions Of Gradients

We propose a robust stereo matching algorithm for images captured under varying radiometric conditions, such as exposure and lighting variations, based on the cumulative distributions of gradients. The gradient operator extracts local changes in pixel values, which are less sensitive to radiometric variations than the original pixel values. Moreover, the cumulative distribution function (CDF) of gradient vectors reflects the ranks of edge strength levels, and corresponding pixels in stereo images tend to have similar ranks regardless of radiometric conditions. Therefore, we design the matching cost function based on the dissimilarity of gradient CDF values. However, since multiple pixels in an image may have the same gradient CDF value, we further constrain the correspondence matching by checking the dissimilarity of gradient orientations. Finally, to estimate an accurate disparity at each pixel, we adaptively aggregate matching costs using the color similarity and the geometric proximity of neighboring pixels. Experimental results demonstrate that the proposed algorithm provides more accurate disparities than conventional algorithms, especially under varying lighting conditions.