  We present a new approach to the problem of estimating the 3D room layout
from a single panoramic image. We represent room layout as three 1D vectors
that encode, at each image column, the boundary positions of floor-wall and
ceiling-wall, and the existence of wall-wall boundary. The proposed network,
HorizonNet, trained for predicting 1D layout, outperforms previous
state-of-the-art approaches. The designed post-processing procedure for
recovering 3D room layouts from 1D predictions can automatically infer the room
shape with low computation cost - it takes less than 20ms for a panorama image
while prior works might need dozens of seconds. We also propose Pano Stretch
Data Augmentation, which can diversify panorama data and be applied to other
panorama-related learning tasks. Due to the limited data available for
non-cuboid layout, we relabel 65 general layout from the current dataset for
finetuning. Our approach shows good performance on general layouts by
qualitative results and cross-validation.
