Fast Adaptive Support-Weight Stereo Matching Algorithm

Abstract

Adaptive support-weight (ASW) stereo matching algorithm is widely used in the field of three-dimensional (3D) reconstruction owing to its relatively high matching accuracy. However, since all the weight coefficients need to be calculated in the whole disparity range for each pixel, the algorithm is extremely time-consuming. To solve this problem, a fast ASW algorithm is proposed using twice aggregation. First, a novel weight coefficient which adapts cosine function to satisfy the weight distribution discipline is proposed to accomplish the first cost aggregation. Then, the disparity range is divided into several sub-ranges and local optimal disparities are selected from each of them. For each pixel, only the ASW at the location of local optimal disparities is calculated, and thus, the complexity of the algorithm is greatly reduced. Experimental results show that the proposed algorithm can reduce the amount of calculation by 70% and improve the matching accuracy by 6% for the 15 images on Middlebury Website on average.