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.

翻译：本文提出了一种利用非中心采集系统进行室内环境三维布局恢复的新方法。通过非中心全景图，无需几何或尺度假设，即可独立恢复完整的、带比例的三维线段。然而，该类全景图对噪声敏感且几何建模复杂，导致其研究较少。我们的新流程旨在通过神经网络提取室内环境结构线的边界，并利用非中心投影系统的特性，通过新的几何处理恢复带比例的三维布局。实验结果表明，在非中心投影系统中，我们在布局重建和线提取方面改进了现有最优方法。我们完全解决了曼哈顿和亚特兰大环境中的问题，处理了遮挡，并无需额外测量即可恢复房间的度量尺度。据作者所知，我们的方法是首个在非中心全景图上使用深度学习，并从单张全景图中恢复带比例布局的工作。