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Boosting Multi-view Convolutional Neural Networks for 3D Object Recognition via View Saliency

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Advances in Image and Graphics Technologies (IGTA 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 757))

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Abstract

2D views of objects play an important role in 3D object recognition. In this paper, we focus on 3D object recognition using the 2D projective views. The discriminativeness of each view of an object is first investigated with view saliency using 2D Zernike Moments. The proposed view saliency is then used to boost a multi-view convolutional neural network for 3D object recognition. The proposed method is compared with several state-of-the-art methods on the ModelNet dataset. Experimental results have shown that the performance of our method has been significantly improved over the existing multi-view based 3D object recognition methods.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (Nos. 61602499, 61601488, 61471371, 61403265), the National Postdoctoral Program for Innovative Talents (No. BX201600172), the Science and Technology Plan of Sichuan Province (No. 2015SZ0226), and China Postdoctoral Science Foundation.

Author information

Authors and Affiliations

  1. College of Electronic Science and Engineering, National University of Defense Technology, Changsha, China

    Yanxin Ma, Yulan Guo & Jun Zhang

  2. Henan Information and Engineering College, Zhengzhou, China

    Bin Zheng

  3. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Yulan Guo

  4. College of Electronics and Information Engineering, Sichuan University, Chengdu, China

    Yinjie Lei

Authors
  1. Yanxin Ma
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  2. Bin Zheng
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  3. Yulan Guo
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  4. Yinjie Lei
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  5. Jun Zhang
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Corresponding author

Correspondence to Yulan Guo .

Editor information

Editors and Affiliations

  1. Beijing Institute of Technology, Beijing, China

    Yongtian Wang

  2. Tsinghua University, Beijing, China

    Shengjin Wang

  3. Beijing Institute of Technology, Beijing, China

    Yue Liu

  4. Beijing Institute of Technology, Beijing, China

    Jian Yang

  5. School of EECS, Center for Information Science, Peking University, Beijing, China

    Xiaoru Yuan

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

    Ran He

  7. La Trobe University, Melbourne, Victoria, Australia

    Henry Been-Lirn Duh

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Ma, Y., Zheng, B., Guo, Y., Lei, Y., Zhang, J. (2018). Boosting Multi-view Convolutional Neural Networks for 3D Object Recognition via View Saliency. In: Wang, Y., et al. Advances in Image and Graphics Technologies. IGTA 2017. Communications in Computer and Information Science, vol 757. Springer, Singapore. https://doi.org/10.1007/978-981-10-7389-2_20

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  • DOI: https://doi.org/10.1007/978-981-10-7389-2_20

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-7388-5

  • Online ISBN: 978-981-10-7389-2

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