Abstract: This paper presents a hierarchical cost aggregation-based fast stereo image matching method based on Webers law and guided filtering. Weber local descriptors for each color channel are firstly extracted from stereo pairs, and raw matching costs between the images are initialized by the descriptors. The matching costs are enhanced with guided filtering to extract the subsets of disparity candidates. Joint spatial discrete sampling and adaptive support weight are utilized to implement hierarchical cost aggregation on the candidate subsets. Then initial disparities from the subsets are selected fast and optimally. Modified bilateral filtering and symmetric warping-based post-processing are sequentially exploited in disparity refining to improve effectively ambiguous regions of initial disparity maps. The experimental results indicate that this proposed technique can obtain piecewise smooth, accurate and dense disparity map while eliminating effectively matching ambiguity. Being concise, fast and high efficiency, and it is robust to illumination change.