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.

翻译：我们提出了一种新型自动二维平面图重建系统，该系统由我们创新的成对学习对齐验证器SALVe实现。系统的输入是稀疏分布的360°全景图像，通过推断其语义特征（窗户、门及开口区域）来假设成对房间的相邻或重叠关系。SALVe初始化位姿图后，采用GTSAM进行优化求解。在计算获得房间位姿后，使用HorizonNet推断房间布局，并通过拼接置信度最高的布局边界构建完整平面图。我们通过定性定量分析及消融实验验证了系统性能，结果表明：在保持精度的前提下，该系统在完整性上超越当前最先进SfM系统达200%以上。研究数据凸显了本工作的重要意义：81%的全景图像位姿在前2个连通分量中完成定位，89%在前3个连通分量中实现定位。代码与模型已在https://github.com/zillow/salve公开。