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DeepRoom: 3D Room Layout and Pose Estimation from a Single Image

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Pattern Recognition (ACPR 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12047))

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Abstract

Though many deep learning approaches have significantly boosted the accuracy for room layout estimation, the existing methods follow the long-established traditional pipeline. They replace the front-end model with CNN and still rely heavily on post-processing for layout reasoning. In this paper, we propose a geometry-aware framework with pure deep networks to estimate the 2D as well as 3D layout in a row. We decouple the task of layout estimation into two stages, first estimating the 2D layout representation and then the parameters for 3D cuboid layout. Moreover, with such a two-stage formulation, the outputs of deep networks are explainable and also extensible to other training signals jointly and separately. Our experiments demonstrate that the proposed framework can provide not only competitive 2D layout estimation but also 3D room layout estimation in real time without post-processing.

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Authors and Affiliations

  1. Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan

    Hung Jin Lin & Shang-Hong Lai

Authors
  1. Hung Jin Lin
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  2. Shang-Hong Lai
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Correspondence to Shang-Hong Lai .

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Editors and Affiliations

  1. University of Malaya, Kuala Lumpur, Malaysia

    Shivakumara Palaiahnakote

  2. Consiglio Nazionale delle Ricerche, ICAR, Naples, Italy

    Gabriella Sanniti di Baja

  3. Chinese Academy of Sciences, Beijing, China

    Liang Wang

  4. Auckland University of Technology, Auckland, New Zealand

    Wei Qi Yan

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Lin, H.J., Lai, SH. (2020). DeepRoom: 3D Room Layout and Pose Estimation from a Single Image. In: Palaiahnakote, S., Sanniti di Baja, G., Wang, L., Yan, W. (eds) Pattern Recognition. ACPR 2019. Lecture Notes in Computer Science(), vol 12047. Springer, Cham. https://doi.org/10.1007/978-3-030-41299-9_56

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  • DOI: https://doi.org/10.1007/978-3-030-41299-9_56

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-41298-2

  • Online ISBN: 978-3-030-41299-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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