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Double Refinement Network for Room Layout Estimation

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Book cover Pattern Recognition (ACPR 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12046))

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Abstract

Room layout estimation is a challenge of segmenting a cluttered room image into floor, walls and ceiling. We apply a Double Refinement Network (DRN) which has been successfully used to the monocular depth map estimation. Our method is the first not using encoder-decoder architecture for the room layout estimation. ResNet50 was utilized as a backbone for the network instead of VGG16 commonly used for the task, allowing the network to be more compact and faster. We introduced a special layout scoring function and layout ranking algorithm for key points and edges output. Our method achieved the lowest pixel and corner errors on the LSUN data set. The input image resolution is 224 * 224.

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Authors and Affiliations

  1. Samsung AI Center, Lesnaya 5, 125047, Moscow, Russia

    Ivan Kruzhilov, Mikhail Romanov & Anton Konushin

  2. Moscow Power Engineering Institute, Krasnokazarmennay 14, 111250, Moscow, Russia

    Ivan Kruzhilov

  3. Higher School of Economics, Myasnitskaya 20, 101000, Moscow, Russia

    Dmitriy Babichev

  4. Lomonosov Moscow State University, Leninskie gory 1, 119991, Moscow, Russia

    Anton Konushin

Authors
  1. Ivan Kruzhilov
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  2. Mikhail Romanov
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  3. Dmitriy Babichev
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  4. Anton Konushin
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Corresponding author

Correspondence to Ivan Kruzhilov .

Editor information

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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Kruzhilov, I., Romanov, M., Babichev, D., Konushin, A. (2020). Double Refinement Network for Room Layout Estimation. In: Palaiahnakote, S., Sanniti di Baja, G., Wang, L., Yan, W. (eds) Pattern Recognition. ACPR 2019. Lecture Notes in Computer Science(), vol 12046. Springer, Cham. https://doi.org/10.1007/978-3-030-41404-7_39

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  • DOI: https://doi.org/10.1007/978-3-030-41404-7_39

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-41403-0

  • Online ISBN: 978-3-030-41404-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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