  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.
