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- W4285174860 abstract "Zhao, Shaonan Li, Wei3D room layout reconstruction from a single RGB panoramic image has been an emerging research topic in recent years. To achieve better prediction accuracy, in this paper, we propose a new approach to predict 3D room layout from a single panoramic image. Our reconstruction flow follows a common framework which is same as LayoutNet [9] and HorizonNet [4]; however, we redesign a new deep learning architecture with recurrent neural networks (RNNs) encoder–decoder as an extension for keypoints refinement and use a gradient ascent optimization algorithm to minimize the similar loss. Experiments on both cuboid-shaped and general Manhattan layouts show that the proposed work outperforms recent algorithms in prediction accuracy." @default.
- W4285174860 created "2022-07-14" @default.
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- W4285174860 date "2022-01-01" @default.
- W4285174860 modified "2023-09-27" @default.
- W4285174860 title "ResRnnNet: Learning to Reconstruct the 3D Room Layout from a Single RGB Panorama" @default.
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- W4285174860 doi "https://doi.org/10.1007/978-981-19-1057-9_31" @default.
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