  We propose a new system for automatic 2D floorplan reconstruction that is
enabled by SALVe, our novel pairwise learned alignment verifier. The inputs to
our system are sparsely located 360$^\circ$ panoramas, whose semantic features
(windows, doors, and openings) are inferred and used to hypothesize pairwise
room adjacency or overlap. SALVe initializes a pose graph, which is
subsequently optimized using GTSAM. Once the room poses are computed, room
layouts are inferred using HorizonNet, and the floorplan is constructed by
stitching the most confident layout boundaries. We validate our system
qualitatively and quantitatively as well as through ablation studies, showing
that it outperforms state-of-the-art SfM systems in completeness by over 200%,
without sacrificing accuracy. Our results point to the significance of our
work: poses of 81% of panoramas are localized in the first 2 connected
components (CCs), and 89% in the first 3 CCs. Code and models are publicly
available at https://github.com/zillow/salve.
