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Deep Near-Light Photometric Stereo for Spatially Varying Reflectances

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Book cover Computer Vision – ECCV 2020 (ECCV 2020)

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Abstract

This paper presents a near-light photometric stereo method for spatially varying reflectances. Recent studies in photometric stereo proposed learning-based approaches to handle diverse real-world reflectances and achieve high accuracy compared to conventional methods. However, they assume distant (i.e., parallel) lights, which can in practical settings only be approximately realized, and they fail in near-light conditions. Near-light photometric stereo methods address near-light conditions but previous works are limited to over-simplified reflectances, such as Lambertian reflectance. The proposed method takes a hybrid approach of distant- and near-light models, where the surface normal of a small area (corresponding to a pixel) is computed locally with a distant light assumption, and the reconstruction error is assessed based on a near-light image formation model. This paper is the first work to solve unknown, spatially varying, diverse reflectances in near-light photometric stereo.

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Notes

  1. 1.

    \(\max (0, x)\) is differentiable everywhere except at \(x=0\), which is in practice not a problem with numerical differentiation as in many other works.

  2. 2.

    PyTorch v1.1.0: http://pytorch.org.

  3. 3.

    Mitsuba v0.5.0: http://mitsuba-renderer.org.

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Acknowledgment

This work was supported by JSPS KAKENHI Grant Number JP19H01123. Hiroaki Santo and Michael Waechter are grateful for support through a JSPS Research Fellowship for Young Scientists (JP19J10326) and JSPS Postdoctoral Fellowship (JP17F17350), respectively.

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Authors and Affiliations

  1. Graduate School of Information Science and Technology, Osaka University, Osaka, Japan

    Hiroaki Santo, Michael Waechter & Yasuyuki Matsushita

Authors
  1. Hiroaki Santo
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  2. Michael Waechter
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  3. Yasuyuki Matsushita
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Corresponding author

Correspondence to Hiroaki Santo .

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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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Santo, H., Waechter, M., Matsushita, Y. (2020). Deep Near-Light Photometric Stereo for Spatially Varying Reflectances. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12353. Springer, Cham. https://doi.org/10.1007/978-3-030-58598-3_9

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  • DOI: https://doi.org/10.1007/978-3-030-58598-3_9

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