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Bivariate BRDF Estimation Based on Compressed Sensing

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Book cover Advances in Computer Graphics (CGI 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11542))

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Abstract

We propose a method of estimating a bivariate BRDF from a small number of sampled data using compressed sensing. This method aims to estimate the reflectance of various materials by using the representation space that keeps local information when restored by compressed sensing. We conducted simulated measurements using randomly sampled data and data sampled according to the camera position and orientation, and confirmed that most of the BRDF was successfully restored from 40% sampled data in the case of simulated measurement using a camera and markers.

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Author information

Authors and Affiliations

  1. Saitama University, 255 Shimo-Okubo, Sakura-ku, Saitama, 338-8570, Japan

    Haru Otani & Takashi Komuro

  2. Tokyo Metropolitan College of Industrial Technology, 8-17-1 Minami-senju, Arakawa-ku, Tokyo, 116-8523, Japan

    Shoji Yamamoto

  3. Chiba University, 1-33 Yayoi-cho, Inage-ku, Chiba, 263-8522, Japan

    Norimichi Tsumura

Authors
  1. Haru Otani
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  2. Takashi Komuro
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  3. Shoji Yamamoto
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  4. Norimichi Tsumura
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Corresponding author

Correspondence to Takashi Komuro .

Editor information

Editors and Affiliations

  1. University of Calgary, Calgary, AB, Canada

    Marina Gavrilova

  2. Bournemouth University, Poole, UK

    Jian Chang

  3. University of Geneva, MIRALab, Carouge, Switzerland

    Nadia Magnenat Thalmann

  4. International Christian University, Tokyo, Japan

    Eckhard Hitzer

  5. Waseda University, Tokyo, Japan

    Hiroshi Ishikawa

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Otani, H., Komuro, T., Yamamoto, S., Tsumura, N. (2019). Bivariate BRDF Estimation Based on Compressed Sensing. In: Gavrilova, M., Chang, J., Thalmann, N., Hitzer, E., Ishikawa, H. (eds) Advances in Computer Graphics. CGI 2019. Lecture Notes in Computer Science(), vol 11542. Springer, Cham. https://doi.org/10.1007/978-3-030-22514-8_48

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  • DOI: https://doi.org/10.1007/978-3-030-22514-8_48

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-22513-1

  • Online ISBN: 978-3-030-22514-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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