Accurate Image-Guided Stereo Matching With Efficient Matching Cost and Disparity Refinement

Abstract

Stereo matching is a challenging problem, and high-accuracy stereo matching is still required in various computer vision applications, e.g., 3-D scanning, autonomous navigation, and 3-D reconstruction. Therefore, we present a novel image-guided stereo matching algorithm, which employs the efficient combined matching cost and multistep disparity refinement, to improve the accuracy of existing local stereo matching algorithms. Different from all the other methods, we introduce a guidance image for the whole algorithm. This filter-based guidance image is generated by extracting the enhanced information from the raw stereo image. The combined matching cost consists of the novel double-RGB gradient, the improved lightweight census transform, and the image color. This cost measurement is robust against image noise and textureless regions in computing the matching cost. Furthermore, a new systemic multistep refinement process, which includes outlier classification, four-direction propagation, leftmost propagation, and an exponential step filter, is proposed to remove the outliers in the raw disparity map. Experiments on the Middlebury benchmark demonstrate our algorithm's superior performance that it ranks first among the 158 submitted algorithms. Moreover, the proposed method is also robust on the 30 Middlebury data sets and the real-world Karlsruhe Institute of Technology and Toyota Technological Institute benchmark.