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A Data-Driven Approach for Direct and Global Component Separation from a Single Image

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Computer Vision – ACCV 2018 (ACCV 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11366))

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Abstract

The radiance captured by camera is often under influence of both direct and global illumination from complex environment. Though separating them is highly desired, existing methods require strict capture restriction such as modulated active light. Here, we propose the first method to infer both components from a single image without any hardware restriction. Our method is a novel generative adversarial network (GAN) based networks which imposes prior physics knowledge to force a physics plausible component separation. We also present the first component separation dataset which comprises of 100 scenes with their direct and global components. In the experiments, our method has achieved satisfactory performance on our own testing set and images in public dataset. Finally, we illustrate an interesting application of editing realistic images through the separated components.

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Author information

Authors and Affiliations

  1. National Institute of Informatics, Chiyoda, Japan

    Shijie Nie, Lin Gu, Ilyes Kacher & Imari Sato

  2. Tokyo Institute of Technology, Meguro, Japan

    Art Subpa-asa

  3. Kyoto University, Kyoto, Japan

    Ko Nishino

  4. Department of Informatics, SOKENDAI (The Graduate University for Advanced Studies), Hayama, Japan

    Shijie Nie

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  1. Shijie Nie
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  2. Lin Gu
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  3. Art Subpa-asa
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  6. Imari Sato
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Correspondence to Lin Gu .

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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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Nie, S., Gu, L., Subpa-asa, A., Kacher, I., Nishino, K., Sato, I. (2019). A Data-Driven Approach for Direct and Global Component Separation from a Single Image. In: Jawahar, C., Li, H., Mori, G., Schindler, K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science(), vol 11366. Springer, Cham. https://doi.org/10.1007/978-3-030-20876-9_9

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