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Locally Geometry-Aware Improvements of LOP for Efficient Skeleton Extraction

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Pattern Recognition and Computer Vision (PRCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13536))

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Abstract

The skeletons extracted from 3D point clouds depict the general distributions of the mesh surfaces, which are affected by the local geometrical relations embedded in the neighboring points. However, the local mesh geometry is still not effectively utilized by the popular contraction based skeleton extraction method LOP and its variants. Therefore, this paper improves LOP from two aspects based on the local geometrical distributions. One is the bilateral filter based weighting scheme which additionally takes curvature similarities between neighboring points to better distribute the samples and the other is the eigenvalue based adaptive radius scheme which makes the contraction area varied according to the local shape. These two updates combine together so that an effective contraction of samples during optimization can be obtained. The experiments demonstrate that the improved LOP can obtain more efficient skeleton extractions than existing methods.

Supported by the Natural Science Foundation of Anhui Province (2108085MF210, 1908085MF187).

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Authors and Affiliations

  1. School of Computer Science and Technology, Anhui University, Hefei, 230601, China

    Xianyong Fang, Lingzhi Hu, Fan Ye & Linbo Wang

Authors
  1. Xianyong Fang
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  2. Lingzhi Hu
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  3. Fan Ye
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  4. Linbo Wang
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Corresponding author

Correspondence to Xianyong Fang .

Editor information

Editors and Affiliations

  1. Southern University of Science and Technology, Shenzhen, China

    Shiqi Yu

  2. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Zhaoxiang Zhang

  3. Hong Kong Baptist University, Hong Kong, China

    Pong C. Yuen

  4. Northwestern Polytechnical University, Xi'an, China

    Junwei Han

  5. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Tieniu Tan

  6. Hong Kong Baptist University, Hong Kong, China

    Yike Guo

  7. Sun Yat-sen University, Guangzhou, China

    Jianhuang Lai

  8. Southern University of Science and Technology, Shenzhen, China

    Jianguo Zhang

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Fang, X., Hu, L., Ye, F., Wang, L. (2022). Locally Geometry-Aware Improvements of LOP for Efficient Skeleton Extraction. In: Yu, S., et al. Pattern Recognition and Computer Vision. PRCV 2022. Lecture Notes in Computer Science, vol 13536. Springer, Cham. https://doi.org/10.1007/978-3-031-18913-5_1

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  • DOI: https://doi.org/10.1007/978-3-031-18913-5_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-18912-8

  • Online ISBN: 978-3-031-18913-5

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