  The problem of 3D layout recovery in indoor scenes has been a core research
topic for over a decade. However, there are still several major challenges that
remain unsolved. Among the most relevant ones, a major part of the
state-of-the-art methods make implicit or explicit assumptions on the scenes --
e.g. box-shaped or Manhattan layouts. Also, current methods are computationally
expensive and not suitable for real-time applications like robot navigation and
AR/VR. In this work we present CFL (Corners for Layout), the first end-to-end
model for 3D layout recovery on 360 images. Our experimental results show that
we outperform the state of the art relaxing assumptions about the scene and at
a lower cost. We also show that our model generalizes better to camera position
variations than conventional approaches by using EquiConvs, a type of
convolution applied directly on the sphere projection and hence invariant to
the equirectangular distortions.
  CFL Webpage: https://cfernandezlab.github.io/CFL/
