  Reconstruction of indoor surfaces with limited texture information or with
repeated textures, a situation common in walls and ceilings, may be difficult
with a monocular Structure from Motion system. We propose a Semantic Room
Wireframe Detection task to predict a Semantic Wireframe from a single
perspective image. Such predictions may be used with shape priors to estimate
the Room Layout and aid reconstruction. To train and test the proposed
algorithm we create a new set of annotations from the simulated Structured3D
dataset. We show qualitatively that the SRW-Net handles complex room geometries
better than previous Room Layout Estimation algorithms while quantitatively
out-performing the baseline in non-semantic Wireframe Detection.
