Stereo matching using gradient similarity and locally adaptive support-weight

Due to the similarities between neighbouring pixels as well as the intensity-value differences between corresponding pixels, classical matching measures based on intensity similarity produce slightly imprecise results. In this study, a gradient similarity-matching measure was implemented in a state-of-the-art local stereo-matching method (an adaptive support-weight algorithm). The new matching measure improved the precision of the results over the classical measures. Using the Middlebury stereo benchmark, when high accuracy was required in the disparity results our algorithm consistently outperformed other adaptive support-weight algorithms using different similarity measures, and it was the best local area-based method compared to the permanent Middlebury table entries.

► Gradient-based stereo matching produces accurate disparity maps. ► Gradient-based stereo matching is very robust against real image conditions. ► New matching measures can further improve the performance of stereo algorithms.