  Single-image room layout reconstruction aims to reconstruct the enclosed 3D
structure of a room from a single image. Most previous work relies on the
cuboid-shape prior. This paper considers a more general indoor assumption,
i.e., the room layout consists of a single ceiling, a single floor, and several
vertical walls. To this end, we first employ Convolutional Neural Networks to
detect planes and vertical lines between adjacent walls. Meanwhile, estimating
the 3D parameters for each plane. Then, a simple yet effective geometric
reasoning method is adopted to achieve room layout reconstruction. Furthermore,
we optimize the 3D plane parameters to reconstruct a geometrically consistent
room layout between planes and lines. The experimental results on public
datasets validate the effectiveness and efficiency of our method.
