  We propose a novel FCN able to work with omnidirectional images that outputs
accurate probability maps representing the main structure of indoor scenes,
which is able to generalize on different data. Our approach handles occlusions
and recovers complex shaped rooms more faithful to the actual shape of the real
scenes. We outperform the state of the art not only in accuracy of the 3D
models but also in speed.
