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View-Based 3D Model Retrieval via Convolutional Neural Networks

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Proceedings of 2017 Chinese Intelligent Systems Conference (CISC 2017)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 460))

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Abstract

In this paper, we propose a multi-view fusion 3D model retrieval using convolutional neural network to solve the problem of the local perception in feature descriptor. By view pooling, we combine information from multiple views of a 3D model to eliminate the position correlation caused by the viewing angle of camera. In addition, integrating pre-processed RGB view-feature with Binary view-feature in the same model is used to generate a single model descriptor. Experiments on ETH dataset demonstrate the superiority of the proposed method.

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Author information

Authors and Affiliations

  1. School of Computer and Information Engineering, Beijing Technology and Business University, Beijing, 100048, China

    Bowen Du, Haisheng Li & Qiang Cai

  2. Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing, 100048, China

    Bowen Du, Haisheng Li & Qiang Cai

Authors
  1. Bowen Du
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  2. Haisheng Li
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  3. Qiang Cai
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Corresponding author

Correspondence to Haisheng Li .

Editor information

Editors and Affiliations

  1. Beihang University , Beijing, China

    Yingmin Jia

  2. Beijing University of Posts and Telecommunications, Beijing, China

    Junping Du

  3. University of Science and Technology Beijing, Beijing, China

    Weicun Zhang

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Du, B., Li, H., Cai, Q. (2018). View-Based 3D Model Retrieval via Convolutional Neural Networks. In: Jia, Y., Du, J., Zhang, W. (eds) Proceedings of 2017 Chinese Intelligent Systems Conference. CISC 2017. Lecture Notes in Electrical Engineering, vol 460. Springer, Singapore. https://doi.org/10.1007/978-981-10-6499-9_44

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  • DOI: https://doi.org/10.1007/978-981-10-6499-9_44

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6498-2

  • Online ISBN: 978-981-10-6499-9

  • eBook Packages: EngineeringEngineering (R0)

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