  In this paper, we present a new approach to estimate the layout of a room
from its single image. While recent approaches for this task use robust
features learnt from data, they resort to optimization for detecting the final
layout. In addition to using learnt robust features, our approach learns an
additional ranking function to estimate the final layout instead of using
optimization. To learn this ranking function, we propose a framework to train a
CNN using max-margin structure cost. Also, while most approaches aim at
detecting cuboidal layouts, our approach detects non-cuboidal layouts for which
we explicitly estimates layout complexity parameters. We use these parameters
to propose layout candidates in a novel way. Our approach shows
state-of-the-art results on standard datasets with mostly cuboidal layouts and
also performs well on a dataset containing rooms with non-cuboidal layouts.
