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。

SALVe: 稀疏全景图的平面图重建的语义对齐验证

SALVe: Semantic Alignment Verification for Floorplan Reconstruction from  Sparse Panoramas

We present a new approach to the problem of estimating the 3D room layout
from a single panoramic image. We represent room layout as three 1D vectors
that encode, at each image column, the boundary positions of floor-wall and
ceiling-wall, and the existence of wall-wall boundary. The proposed network,
HorizonNet, trained for predicting 1D layout, outperforms previous
state-of-the-art approaches. The designed post-processing procedure for
recovering 3D room layouts from 1D predictions can automatically infer the room
shape with low computation cost - it takes less than 20ms for a panorama image
while prior works might need dozens of seconds. We also propose Pano Stretch
Data Augmentation, which can diversify panorama data and be applied to other
panorama-related learning tasks. Due to the limited data available for
non-cuboid layout, we relabel 65 general layout from the current dataset for
finetuning. Our approach shows good performance on general layouts by
qualitative results and cross-validation.

本文提出了 HorizonNet 网络作为一种新方法来从单个全景图像中估计三维房间布局，其表示房间布局为三个一维向量，并提出了恢复三维房间布局的后期处理过程和全景拉伸数据增强的方法。在有限的数据情况下，对当前数据集中的 65 个常规布局进行微调，其在常规布局上具有良好的性能表现。