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Accurate Polarimetric BRDF for Real Polarization Scene Rendering

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Computer Vision – ECCV 2020 (ECCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12364))

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Abstract

Polarization has been used to solve a lot of computer vision tasks such as Shape from Polarization (SfP). But existing methods suffer from ambiguity problems of polarization. To overcome such problems, some research works have suggested to use Convolutional Neural Network (CNN). But acquiring large scale dataset with polarization information is a very difficult task. If there is an accurate model which can describe a complicated phenomenon of polarization, we can easily produce synthetic polarized images with various situations to train CNN.

In this paper, we propose a new polarimetric BRDF (pBRDF) model. We prove its accuracy by fitting our model to measured data with variety of light and camera conditions. We render polarized images using this model and use them to estimate surface normal. Experiments show that the CNN trained by our polarized images has more accuracy than one trained by RGB only.

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Acknowledgment

We express our sincere thanks to our colleagues from Sony Corporation for their helpful discussion and support.

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Authors and Affiliations

  1. Sony Corporation, Tokyo, Japan

    Yuhi Kondo, Taishi Ono, Legong Sun, Yasutaka Hirasawa & Jun Murayama

Authors
  1. Yuhi Kondo
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  2. Taishi Ono
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  3. Legong Sun
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  4. Yasutaka Hirasawa
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  5. Jun Murayama
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Corresponding author

Correspondence to Yuhi Kondo .

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Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

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Kondo, Y., Ono, T., Sun, L., Hirasawa, Y., Murayama, J. (2020). Accurate Polarimetric BRDF for Real Polarization Scene Rendering. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12364. Springer, Cham. https://doi.org/10.1007/978-3-030-58529-7_14

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  • DOI: https://doi.org/10.1007/978-3-030-58529-7_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58528-0

  • Online ISBN: 978-3-030-58529-7

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