Evaluation of Stereo Matching Costs on Close Range, Aerial and Satellite Images

Abstract

In the last years, most dense stereo matching methods use evaluation on the Middlebury stereo vision benchmark datasets. Most recent stereo algorithms were designed to perform well on these close range stereo datasets with relatively small baselines and good radiometric behaviour. In this paper, different matching costs on the Semi-Global Matching algorithm are evaluated and compared using the common Middlebury datasets, aerial and satellite datasets with ground truth. The experimental results show that the performance of dense stereo methods for datasets with larger baselines and stronger radiometric changes relies on even more robust matching costs. In addition, a novel matching cost based on mutual information and Census is introduced showing the most robust performance on close range, aerial and satellite data.