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 this https URL

我们提出了一种新的自动二维楼层平面重建系统，该系统利用我们的 SALVe 算法进行学习验证器。通过使用我们稀疏定位的 360° 全景图，以及从中推测出的语义特征（窗户，门和开口），我们的系统构建了房间的拓扑结构，并通过 HorizonNet 算法推断出楼层平面。经过定性和定量验证以及消融研究，我们的系统在完整性方面表现超过了现有的 SfM 系统 200％以上，而不牺牲准确性。