  We propose a novel method to efficiently estimate the spatial layout of a
room from a single monocular RGB image. As existing approaches based on
low-level feature extraction, followed by a vanishing point estimation are very
slow and often unreliable in realistic scenarios, we build on semantic
segmentation of the input image. To obtain better segmentations, we introduce a
robust, accurate and very efficient hypothesize-and-test scheme. The key idea
is to use three segmentation hypotheses, each based on a different number of
visible walls. For each hypothesis, we predict the image locations of the room
corners and select the hypothesis for which the layout estimated from the room
corners is consistent with the segmentation. We demonstrate the efficiency and
robustness of our method on three challenging benchmark datasets, where we
significantly outperform the state-of-the-art.
