Papers › HorizonNet: Learning Room Layout with 1D Representation and Pano Stretch Data Augmentation
HorizonNet: Learning Room Layout with 1D Representation and Pano Stretch Data Augmentation
Cheng Sun, Chi-Wei Hsiao, Min Sun, Hwann-Tzong Chen
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.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 3D Room Layouts From A Single RGB Panorama | PanoContext | HorizonNet | 3DIoU | 82.17 | #4 of 7 | Archive leaderboard | report |
| 3D Room Layouts From A Single RGB Panorama | Stanford2D3D Panoramic | HorizonNet | 3DIoU | 79.79 | #7 of 9 | Archive leaderboard | report |
| 3D Room Layouts From A Single RGB Panorama | Stanford2D3D Panoramic | HorizonNet | Corner Error | 0.71 | #7 of 9 | Archive leaderboard | report |
| 3D Room Layouts From A Single RGB Panorama | Stanford2D3D Panoramic | HorizonNet | Pixel Error | 2.39 | #7 of 9 | Archive leaderboard | report |
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