Abstract: A recurring issue in data-driven feature extraction systems is the combinatorics of search in hypothesis space. Brute force attempts at feature generation, where all possible combinations of edges are evaluated, leads to exponential growth in the size of the search space. In monocular building extraction systems, this difficulty is encountered in creating plausible building model hypotheses from raw edge segments extracted from aerial imagery. This work presents constraints on intermediate feature generation, based on vanishing point geometry derived from a photogrammetric camera model, to significantly reduce the search space. Qualitative and quantitative results are presented in the context of PIVOT, a fully automated monocular building extraction system. !16
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