Performance Evaluation of Stereo Matching Algorithms in the Lack of Visual Features

In this paper, we evaluate three different subcategories of image matching algorithms. We consider hierarchical matching, wavelet-based localized correlation and multiresolution subregioning. The importance of this evaluation stems from the fact that these algorithms are all somehow based on a multiresolution scheme, but exhibit different performances when dealing with featureless image pairs, noisy image pairs, or when tuned to different parameters, e. g. the number of resolution levels and the size of the correlation size. We also consider the use of different correlation functions. A data set has been built using random dots stereograms, with a full range of disparities and a controlled amount of noise. The algorithms performances are benchmarked in terms of accuracy and global coherence of the disparity maps.