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Many applications in computer vision and computer graphics require dense correspondences between images of multi-view video streams. Most state-of-the-art algorithms estimate correspondences by considering pairs of images. However, in multi-view videos, several images capture nearly the same scene. In this article we show that this redundancy can be exploited to estimate more robust and consistent correspondence fields. We use the multi-video data structure to establish a confidence measuredoi:10.1016/j.imavis.2012.06.011 fatcat:firfrrlt7zedxgt7mu5ko2wkfu