Matching Many Identical Features of Planar Urban Facades Using Global Regularity

Abstract

Reasonable computation and accurate camera calibration require matching many interest points over long baselines. This is a difficult problem requiring better solutions than presently exist for urban scenes involving large buildings containing many windows since windows in a facade all have the same texture and, therefore, cannot be distinguished from one another based solely on appearance. Hence, the usual approach to feature detection and matching, such as use of SIFT, does not work in these scenes. A novel algorithm is introduced to provide correspondences for multiple repeating feature patterns seen under significant viewpoint changes. Most existing appearance-based algorithms cannot handle highly repetitive textures due to the match location ambiguity. However, the target structure provides a rich set of repeating features to be matched and tracked across multiple views, thus potentially improving camera estimation accuracy. The proposed method also exploits the geometric structure of regular grids of repeating features on planar surfaces.