Abstract
Matching and aligning architectural imagery is an important step for many applications but can be a difficult task due to repetitive elements often present in buildings. Many keypoint descriptor and matching methods will fail to produce distinctive descriptors for each region of man-made structures, which causes ambiguity when attempting to match areas between images. In this paper, we outline a technique for reducing the search space for matching by taking a two-step approach, aligning pairs one dimension at a time and by abstracting images that originally contain many repetitive elements into a set of distinct, representative patches. We also present a simple, but very effective method for computing the intra-image saliency for a single image that allows us to directly identify unique areas in an image without machine learning. We use this information to find distinctive keypoint matches across image pairs. We show that our pipeline is able to overcome many of the pitfalls encountered when using traditional keypoint and regional matching techniques on commonly encountered images of urban scenes.
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