  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.
