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Depth Reconstruction of Translucent Objects from a Single Time-of-Flight Camera Using Deep Residual Networks

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Computer Vision – ACCV 2018 (ACCV 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11365))

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Abstract

We propose a novel approach to recovering translucent objects from a single time-of-flight (ToF) depth camera using deep residual networks. When recording translucent objects using the ToF depth camera, their depth values are severely contaminated due to complex light interactions with surrounding environment. While existing methods suggested new capture systems or developed the depth distortion models, their solutions were less practical because of strict assumptions or heavy computational complexity. In this paper, we adopt deep residual networks for modeling the ToF depth distortion caused by translucency. To fully utilize both the local and semantic information of objects, multi-scale patches are used to predict the depth value. Based on the quantitative and qualitative evaluation on our benchmark database, we show the effectiveness and robustness of the proposed algorithm.

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Acknowledgement

This research was supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the University Information Technology Research Center support program (IITP-2016-R2718-16-0014) supervised by the IITP, by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the MSIP (NRF-2016R1A2B4016236), and also by the MIST(Ministry of Science and ICT), Korea, under the “ICT Consilience Creative Program” (IITP-2018-2017-0-01015) supervised by the IITP(Institute for Information & communications Technology Promotion).

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Authors and Affiliations

  1. School of Integrated Technology, Yonsei University, Incheon, South Korea

    Seongjong Song & Hyunjung Shim

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  1. Seongjong Song
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  2. Hyunjung Shim
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Correspondence to Hyunjung Shim .

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Editors and Affiliations

  1. IIIT Hyderabad, Hyderabad, India

    C.V. Jawahar

  2. ANU, Canberra, ACT, Australia

    Hongdong Li

  3. Simon Fraser University, Burnaby, BC, Canada

    Greg Mori

  4. ETH Zurich, Zurich, Zürich, Switzerland

    Konrad Schindler

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Song, S., Shim, H. (2019). Depth Reconstruction of Translucent Objects from a Single Time-of-Flight Camera Using Deep Residual Networks. In: Jawahar, C., Li, H., Mori, G., Schindler, K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science(), vol 11365. Springer, Cham. https://doi.org/10.1007/978-3-030-20873-8_41

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  • DOI: https://doi.org/10.1007/978-3-030-20873-8_41

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20872-1

  • Online ISBN: 978-3-030-20873-8

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