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.

翻译：从非中心全景图中，可以通过几何推理恢复三维直线。然而，这些全景图对噪声的敏感性以及所需的复杂几何建模导致其鲜少被研究。本文提出了一种利用单张非中心全景图进行室内环境三维布局恢复的新方法。我们通过深度学习从非中心全景图中获取房间结构线的边界，并利用非中心投影系统的特性进行新的几何处理，以恢复缩放后的布局。我们针对曼哈顿环境（处理遮挡情况）以及亚特兰大环境，以统一方法解决了该问题。实验结果表明，该方法优于当前基于单张全景图进行三维布局恢复的先进方法。本研究是首个将深度学习与非中心全景图结合、并实现单张全景图布局尺度恢复的工作。