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

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.

PDF Abstract CVPR 2019 PDF CVPR 2019 Abstract
Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
3D Room Layouts From A Single RGB Panorama PanoContext HorizonNet 3DIoU 82.17 # 4
3D Room Layouts From A Single RGB Panorama Stanford2D3D Panoramic HorizonNet 3DIoU 79.79 # 7
Corner Error 0.71 # 4
Pixel Error 2.39 # 5

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