Abstract

Large-scale Structure-from-Motion systems typically spend major computational effort on pairwise image matching and geometric verifification in order to discover connected components in large-scale, unordered image collections. In recent years, the research community has spent signifificant effort on improving the effificiency of this stage. In this paper, we present a comprehensive overview of various state-of-the-art methods, evaluating and analyzing their performance. Based on the insights of this evaluation, we propose a learning-based approach, the PAirwise Image Geometry Encoding (PAIGE), to effificiently identify image pairs with scene overlap without the need to perform exhaustive putative matching and geometric verifification. PAIGE achieves state-of-the-art performance and integrates well into existing Structure-from-Motion pipelines