Merging the Unmatchable: Stitching Visually Disconnected SfM Models

Andrea Cohen, Torsten Sattler, Marc Pollefeys; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2015, pp. 2129-2137

Recent advances in Structure-from-Motion not only enable the reconstruction of large scale scenes, but are also able to detect ambiguous structures caused by repeating elements that might result in incorrect reconstructions. Yet, it is not always possible to fully reconstruct a scene. The images required to merge different sub-models might be missing or it might be impossible to acquire such images in the first place due to occlusions or the structure of the scene. The problem of aligning multiple reconstructions that do not have visual overlap is impossible to solve in general. An important variant of this problem is the case in which individual sides of a building can be reconstructed but not joined due to the missing visual overlap. In this paper, we present a combinatorial approach for solving this variant by automatically stitching multiple sides of a building together. Our approach exploits symmetries and semantic information to reason about the possible geometric relations between the individual models. We show that our approach is able to reconstruct complete building models where traditional SfM ends up with disconnected building sides.