HorizonNet: Learning Room Layout with 1D Representation and Pano Stretch Data Augmentation

We present a new approach to the problem of estimating 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 architecture, 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 training data available for non-cuboid layout, we re-annotate 65 general layout data from the current dataset for fine-tuning and qualitatively show the ability of our approach to estimate general layouts.