Richard Szeliski and Daniel Scharstein
Stereo matching is one of the most active research areas in computer vision. While a large number of algorithms for stereo correspondence have been developed, rela-tively little work has been done on characterizing their performance. In this paper, we present a taxonomy of dense, two-frame stereo methods designed to assess the differ-ent components and design decisions made in individual stereo algorithms. Using this taxonomy, we compare existing stereo methods and present experiments evaluating the performance of many different variants. In order to establish a common software plat-form and a collection of data sets for easy evaluation, we have designed a stand-alone, flexible C++ implementation that enables the evaluation of individual components and that can be easily extended to include new algorithms. We have also produced several new multi-frame stereo data sets with ground truth, and are making both the code and data sets available on the Web.