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• W2908617908 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." @default.
• W2908617908 created "2019-01-25" @default.
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• W2908617908 date "2019-01-12" @default.
• W2908617908 modified "2023-09-28" @default.
• W2908617908 title "HorizonNet: Learning Room Layout with 1D Representation and Pano Stretch Data Augmentation" @default.
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• W2908617908 doi "https://doi.org/10.48550/arxiv.1901.03861" @default.
• W2908617908 hasPublicationYear "2019" @default.
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