Abstract
3D 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.
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Wei Li is supported by SCUT XK2060021005.
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Zhao, S., Li, W. (2022). ResRnnNet: Learning to Reconstruct the 3D Room Layout from a Single RGB Panorama. In: Chu, SC., Chen, SH., Meng, Z., Ryu, K.H., Tsihrintzis, G.A. (eds) Advances in Intelligent Information Hiding and Multimedia Signal Processing. Smart Innovation, Systems and Technologies, vol 277. Springer, Singapore. https://doi.org/10.1007/978-981-19-1057-9_31
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DOI: https://doi.org/10.1007/978-981-19-1057-9_31
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