Name
Title | ||
|---|---|---|
Horizonnet: Learning Room Layout With 1d Representation And Pano Stretch Data Augmentation |
Abstract | ||
|---|---|---|
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 fine-tuning. Our approach shows good performance on general layouts by qualitative results and cross-validation. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/CVPR.2019.00114 | 2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019) |
DocType | Volume | ISSN |
Conference | abs/1901.03861 | 1063-6919 |
Citations | PageRank | References |
2 | 0.36 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cheng Sun | 1 | 2 | 3.06 |
| Chi-Wei Hsiao | 2 | 2 | 1.04 |
| Min Sun | 3 | 1083 | 59.15 |
| Hwann-Tzong Chen | 4 | 826 | 52.13 |