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Projective Parallel Single-Pixel Imaging to Overcome Global Illumination in 3D Structure Light Scanning

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13666))

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Abstract

We consider robust and efficient 3D structure light scanning method in situations dominated by global illumination. One typical way of solving this problem is via the analysis of 4D light transport coefficients (LTCs), which contains complete information for a projector-camera pair, and is a 4D data set. However, the process of capturing LTCs generally takes long time. We present projective parallel single-pixel imaging (pPSI), wherein the 4D LTCs are reduced to multiple projection functions to facilitate a highly efficient data capture process. We introduce local maximum constraint, which provides necessary condition for the location of correspondence matching points when projection functions are captured. Local slice extension method is introduced to further accelerate the capture of projection functions. We study the influence of scan ratio in local slice extension method on the accuracy of the correspondence matching points, and conclude that partial scanning is enough for satisfactory results. Our discussions and experiments include three typical kinds of global illuminations: inter-reflections, subsurface scattering, and step edge fringe aliasing. The proposed method is validated in several challenging scenarios.

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Authors and Affiliations

  1. School of Instrumentation and Optoelectronic Engineering, Beihang University (BUAA), Beijing, China

    Yuxi Li, Hongzhi Jiang & Xudong Li

  2. Institute of Artificial Intelligence, Beihang University (BUAA), Beijing, China

    Huijie Zhao

Authors
  1. Yuxi Li
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  2. Huijie Zhao
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  3. Hongzhi Jiang
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  4. Xudong Li
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Corresponding author

Correspondence to Hongzhi Jiang .

Editor information

Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Li, Y., Zhao, H., Jiang, H., Li, X. (2022). Projective Parallel Single-Pixel Imaging to Overcome Global Illumination in 3D Structure Light Scanning. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13666. Springer, Cham. https://doi.org/10.1007/978-3-031-20068-7_28

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  • DOI: https://doi.org/10.1007/978-3-031-20068-7_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20067-0

  • Online ISBN: 978-3-031-20068-7

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