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A Study of 3D Shape Similarity Search in Point Representation by Using Machine Learning

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Advances on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC 2019)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 96))

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Abstract

3D shape similarity seach have been studied for detecting or finding a specified 3D model among 3D CAD model database. We propose to use 3D point data for the search, because it has become easier to obtain 3D point data by photographs. CAD data is converted into 3D point data in advance. Then, using machine learning, we attempt to match those data with the 3D point data acquired in the field. It can be expected that the accuracy of matching is improved by directly handling 3D data. As a preliminary trial, we have tried to clasify 10 kinds of chair models in represented as 3D point data with a machine learning approach. It was suggested that 3D shape matching between 3D point data is possible by our proposed method as the result.

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Acknowledgments

This research was financially supported by KAKENHI 17K00162. We would like to thank ARK Information Systems, INC. for their work on sample programs for machine learning.

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Authors and Affiliations

  1. Department of Information Systems, Tokyo City University, 3-3-1 Ushikubo-nishi, Tsuzuki-ku, Yokohama City, Japan

    Hideo Miyachi & Koshiro Murakami

Authors
  1. Hideo Miyachi
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  2. Koshiro Murakami
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Corresponding author

Correspondence to Hideo Miyachi .

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Editors and Affiliations

  1. Department of Information and Communication Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. University of Antwerp, Antwerpen, Belgium

    Peter Hellinckx

  3. Chiang Mai University, Chiang Mai, Thailand

    Juggapong Natwichai

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Miyachi, H., Murakami, K. (2020). A Study of 3D Shape Similarity Search in Point Representation by Using Machine Learning. In: Barolli, L., Hellinckx, P., Natwichai, J. (eds) Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 3PGCIC 2019. Lecture Notes in Networks and Systems, vol 96. Springer, Cham. https://doi.org/10.1007/978-3-030-33509-0_24

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  • DOI: https://doi.org/10.1007/978-3-030-33509-0_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33508-3

  • Online ISBN: 978-3-030-33509-0

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