From a non-central panorama, 3D lines can be recovered by geometric
reasoning. However, their sensitivity to noise and the complex geometric
modeling required has led these panoramas being very little investigated. In
this work we present a novel approach for 3D layout recovery of indoor
environments using single non-central panoramas. We obtain the boundaries of
the structural lines of the room from a non-central panorama using deep
learning and exploit the properties of non-central projection systems in a new
geometrical processing to recover the scaled layout. We solve the problem for
Manhattan environments, handling occlusions, and also for Atlanta environments
in an unified method. The experiments performed improve the state-of-the-art
methods for 3D layout recovery from a single panorama. Our approach is the
first work using deep learning with non-central panoramas and recovering the
scale of single panorama layouts.

使用单个非中心全景图，通过几何推理从室内环境中恢复 3D 布局，利用深度学习提取结构线的边界并利用非中心投影系统的特性进行新的几何处理以恢复比例尺，解决了曼哈顿环境和亚特兰大环境的遮挡问题，改进了现有的单幅全景图的 3D 布局恢复方法，是第一个使用深度学习进行非中心全景图及其布局比例尺恢复的研究。