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Robust spatiotemporal stereo for dynamic scenes

Stereo matching is a challenging problem, especially in the presence of noise or of weakly textured objects. Using temporal information in a binocular video sequence to increase the discriminability for matching has been introduced in the recent past, but all the proposed methods assume either constant disparity over time, or small object motions, which is not always true. We introduce a novel stereo algorithm that exploits temporal information by robustly aggregating a similarity statistic over time, in order to improve the matching accuracy for weak data, while preserving regions undergoing large motions without introducing artifacts.