  This paper focuses on the task of room layout estimation from a monocular RGB
image. Prior works break the problem into two sub-tasks: semantic segmentation
of floor, walls, ceiling to produce layout hypotheses, followed by an iterative
optimization step to rank these hypotheses. In contrast, we adopt a more direct
formulation of this problem as one of estimating an ordered set of room layout
keypoints. The room layout and the corresponding segmentation is completely
specified given the locations of these ordered keypoints. We predict the
locations of the room layout keypoints using RoomNet, an end-to-end trainable
encoder-decoder network. On the challenging benchmark datasets Hedau and LSUN,
we achieve state-of-the-art performance along with 200x to 600x speedup
compared to the most recent work. Additionally, we present optional extensions
to the RoomNet architecture such as including recurrent computations and memory
units to refine the keypoint locations under the same parametric capacity.
