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• W2895040125 abstract "In this paper, we study the problem of recovering 3D planar surfaces from a single image of man-made environment. We show that it is possible to directly train a deep neural network to achieve this goal. A novel plane structure-induced loss is proposed to train the network to simultaneously predict a plane segmentation map and the parameters of the 3D planes. Further, to avoid the tedious manual labeling process, we show how to leverage existing large-scale RGB-D dataset to train our network without explicit 3D plane annotations, and how to take advantage of the semantic labels come with the dataset for accurate planar and non-planar classification. Experiment results demonstrate that our method significantly outperforms existing methods, both qualitatively and quantitatively. The recovered planes could potentially benefit many important visual tasks such as vision-based navigation and human-robot interaction." @default.
• W2895040125 created "2018-10-12" @default.
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• W2895040125 date "2018-01-01" @default.
• W2895040125 modified "2023-10-05" @default.
• W2895040125 title "Recovering 3D Planes from a Single Image via Convolutional Neural Networks" @default.
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• W2895040125 doi "https://doi.org/10.1007/978-3-030-01249-6_6" @default.
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