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A New Volume-Based Convexity Measure for 3D Shapes

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Advances in Computer Graphics (CGI 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12221))

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Abstract

Convexity, as a global and learning-free shape descriptor, has been widely applied to shape classification, retrieval and decomposition. Unlike its extensively addressed 2D counterpart, 3D shape convexity measurement attracting insufficient attention has yet to be studied. In this paper, we put forward a new volume-based convexity measure for 3D shapes, which builds on a conventional volume-based convexity measure but excels it by resolving its problems. By turning the convexity measurement into a problem of influence evaluation through Distance-weighted Volume Integration, the new convexity measure can resolve the major problems of the existing ones and accelerate the overall computational time.

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Author information

Authors and Affiliations

  1. Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai, People’s Republic of China

    Xiayan Shi & Rui Li

  2. The Department of Computer Science, Liverpool John Moores University, Liverpool, L3 3AF, UK

    Yun Sheng

Authors
  1. Xiayan Shi
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  2. Rui Li
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  3. Yun Sheng
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Corresponding author

Correspondence to Xiayan Shi .

Editor information

Editors and Affiliations

  1. University of Geneva, Geneva, Switzerland

    Nadia Magnenat-Thalmann

  2. University of Crete, Heraklion, Greece

    Constantine Stephanidis

  3. University of Macau, Macau, China

    Enhua Wu

  4. Swiss Federal Institute of Technology, Lausanne, Switzerland

    Daniel Thalmann

  5. Shanghai Jiao Tong University, Shanghai, China

    Bin Sheng

  6. University of Sydney, Sydney, Australia

    Jinman Kim

  7. University of Crete, Heraklion, Greece

    George Papagiannakis

  8. University of Calgary, Calgary, AB, Canada

    Marina Gavrilova

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Shi, X., Li, R., Sheng, Y. (2020). A New Volume-Based Convexity Measure for 3D Shapes. In: Magnenat-Thalmann, N., et al. Advances in Computer Graphics. CGI 2020. Lecture Notes in Computer Science(), vol 12221. Springer, Cham. https://doi.org/10.1007/978-3-030-61864-3_6

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  • DOI: https://doi.org/10.1007/978-3-030-61864-3_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-61863-6

  • Online ISBN: 978-3-030-61864-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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