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

Cheng Sun, Chi-Wei Hsiao, Min Sun, Hwann-Tzong Chen; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 1047-1056

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 finetuning. Our approach shows good performance on general layouts by qualitative results and cross-validation.

Related Material


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[bibtex]
@InProceedings{Sun_2019_CVPR,
author = {Sun, Cheng and Hsiao, Chi-Wei and Sun, Min and Chen, Hwann-Tzong},
title = {HorizonNet: Learning Room Layout With 1D Representation and Pano Stretch Data Augmentation},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}