Abstract

The homography between pairs of images are typically com- puted from the correspondence of keypoints, which are established by using image descriptors. When these descriptors are not reliable, either because of repetitive patterns or large amounts of clutter, additional priors need to be considered. The Blind PnP algorithm makes use of geometric priors to guide the search for matches while computing cam- era pose. Inspired by this, we propose a novel approach for homography estimation that combines geometric priors with appearance priors of am- biguous descriptors. More specifically, for each point we retain its best candidates according to appearance. We then prune the set of poten- tial matches by iteratively shrinking the regions of the image that are consistent with the geometric prior. We can then successfully compute homographies between pairs of images containing highly repetitive pat- terns and even under oblique viewing conditions.