Large-scale Structure-from-Motion systems typically spend major computational
effort on pairwise image matching and geometric verification in order to
discover connected components in unordered image collections. In recent years,
the research community has spent significant effort on improving the efficiency
of this stage. In this work, we propose a learning-based approach, the PAirwise
Image Geometry Encoding (PAIGE), to efficiently identify image pairs with scene
overlap without the need to perform exhaustive putative matching and geometric
verification. PAIGE achieves state-of-the-art performance and integrates well
into existing Structure-from-Motion pipelines.