In this work we present a novel approach for 3D layout recovery of indoor
environments using a non-central acquisition system. From a non-central
panorama, full and scaled 3D lines can be independently recovered by geometry
reasoning without geometric nor scale assumptions. However, their sensitivity
to noise and complex geometric modeling has led these panoramas being little
investigated. Our new pipeline aims to extract the boundaries of the structural
lines of an indoor environment with a neural network and exploit the properties
of non-central projection systems in a new geometrical processing to recover an
scaled 3D layout. The results of our experiments show that we improve
state-of-the-art methods for layout reconstruction and line extraction in
non-central projection systems. We completely solve the problem in Manhattan
and Atlanta environments, handling occlusions and retrieving the metric scale
of the room without extra measurements. As far as the authors knowledge goes,
our approach is the first work using deep learning on non-central panoramas and
recovering scaled layouts from single panoramas.

使用非中心采集系统的新方法，结合神经网络提取室内环境的结构线，并通过新的几何处理方法恢复放缩的三维布局。通过实验证明，该方法改进了非中心投影系统中布局重建和线条提取的方法，可以解决曼哈顿和亚特兰大环境中的问题，处理遮挡并恢复房间的度量尺度，而无需额外测量。据作者所知，这是首个在非中心全景图上使用深度学习从单个全景图中恢复放缩布局的工作。

来自非中心全景图的亚特兰大缩放布局

Atlanta Scaled layouts from non-central panoramas

The problem of 3D layout recovery in indoor scenes has been a core research
topic for over a decade. However, there are still several major challenges that
remain unsolved. Among the most relevant ones, a major part of the
state-of-the-art methods make implicit or explicit assumptions on the scenes --
e.g. box-shaped or Manhattan layouts. Also, current methods are computationally
expensive and not suitable for real-time applications like robot navigation and
AR/VR. In this work we present CFL (Corners for Layout), the first end-to-end
model for 3D layout recovery on 360 images. Our experimental results show that
we outperform the state of the art relaxing assumptions about the scene and at
a lower cost. We also show that our model generalizes better to camera position
variations than conventional approaches by using EquiConvs, a type of
convolution applied directly on the sphere projection and hence invariant to
the equirectangular distortions.
CFL Webpage: this https URL

本文提出了一种基于 360 度图片的端到端模型 CFL（Corners for Layout），用于恢复室内场景的三维布局，并通过使用 EquiConvs（一种直接应用于球体投影的卷积），放宽场景假设和降低成本，优于目前的现有方法，并且对相机位置变化具有良好的泛化性能。

基于端到端的 360 度图像布局恢复的角点检测

Corners for Layout: End-to-End Layout Recovery from 360 Images

In this paper, we propose a novel procedure for 3D layout recovery of indoor
scenes from single 360 degrees panoramic images. With such images, all scene is
seen at once, allowing to recover closed geometries. Our method combines
strategically the accuracy provided by geometric reasoning (lines and vanishing
points) with the higher level of data abstraction and pattern recognition
achieved by deep learning techniques (edge and normal maps). Thus, we extract
structural corners from which we generate layout hypotheses of the room
assuming Manhattan world. The best layout model is selected, achieving good
performance on both simple rooms (box-type) and complex shaped rooms (with more
than four walls). Experiments of the proposed approach are conducted within two
public datasets, SUN360 and Stanford (2D-3D-S) demonstrating the advantages of
estimating layouts by combining geometry and deep learning and the
effectiveness of our proposal with respect to the state of the art.

该论文提出了一种从单个全景图像中恢复室内场景的三维布局恢复新方法，该方法结合几何推理和深度学习技术，从而提取结构角并生成房间的布局模型，并在 SUN360 和 Stanford 公共数据集上进行实验。