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Quantitative Performance Analysis of Hybrid Mesh Segmentation

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2nd EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing

Part of the book series: EAI/Springer Innovations in Communication and Computing ((EAISICC))

Abstract

This paper presents a comprehensive quantitative performance analysis of hybrid mesh segmentation algorithm. An important contribution of this proposed hybrid mesh segmentation algorithm is that it clusters facets using “facet area” as a novel mesh attribute. The method does not require to set any critical parameters for segmentation. The performance of the proposed algorithm is evaluated by comparing the proposed algorithm with the recently developed state-of-the-art algorithms in terms of coverage, time complexity, and accuracy. The experimentation results on various benchmark test cases demonstrate that Hybrid Mesh Segmentation approach does not depend on complex attributes, and outperforms the existing state-of-the-art algorithms. The simulation reveals that Hybrid Mesh Segmentation achieves a promising performance with coverage of more than 95%.

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Acknowledgements

This research was supported by Centre for Computational Technologies (CCTech), Pune, India. Special thanks are given to Dr. Truc Le and Dr. Ye Duan [9] for helping us to quantify percentage coverage. The authors are grateful to the authors Attene et al. [4], RANSAC [5], and GlobFit et al. [6] who have made their code available to the public.

Author information

Authors and Affiliations

  1. Amrutvahini College of Engineering, Department of Mechanical Engineering, Savitribai Phule Pune University, Sangamner, India

    Vaibhav J. Hase

  2. Engineering, Faculty of Science, University of East Anglia, Norwich, UK

    Yogesh J. Bhalerao

  3. School of Mechanical Engineering, MIT Academy of Engineering, Alandi, Pune, India

    Yogesh J. Bhalerao

  4. SCSM College of Engineering, Department of Mechanical Engineering, Savitribai Phule Pune University, Ahmednagar, India

    Mahesh P. Nagarkar

  5. Centre for Computational Technologies (CCTech), Pune, India

    Sandip N. Jadhav

Authors
  1. Vaibhav J. Hase
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  2. Yogesh J. Bhalerao
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  3. Mahesh P. Nagarkar
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  4. Sandip N. Jadhav
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Corresponding author

Correspondence to Vaibhav J. Hase .

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Editors and Affiliations

  1. Department of Computer Science and Engineering, Sri Eshwar College of Engineering, Coimbatore, Tamil Nadu, India

    Anandakumar Haldorai

  2. Department of Computer Science and Engineering, Presidency University, Bengaluru, Karnataka, India

    Arulmurugan Ramu

  3. Electronics and Communication Engg, Sri Eshwar College of Engineering, Coimbatore, Tamil Nadu, India

    Sudha Mohanram

  4. Dept of Info Mgmt, Sec 3, Natl Taichung Univ of Sci & Tech, Taichung, Taiwan

    Mu-Yen Chen

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Hase, V.J., Bhalerao, Y.J., Nagarkar, M.P., Jadhav, S.N. (2021). Quantitative Performance Analysis of Hybrid Mesh Segmentation. In: Haldorai, A., Ramu, A., Mohanram, S., Chen, MY. (eds) 2nd EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-47560-4_10

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  • DOI: https://doi.org/10.1007/978-3-030-47560-4_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-47559-8

  • Online ISBN: 978-3-030-47560-4

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