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Learning-Based Multiple Pooling Fusion in Multi-View Convolutional Neural Network for 3D Model Classification and Retrieval

  • Zeng, Hui (Beijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing) ;
  • Wang, Qi (Beijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing) ;
  • Li, Chen (School of Computer Science and Technology, North China University of Technology) ;
  • Song, Wei (School of Computer Science and Technology, North China University of Technology)
  • Received : 2017.06.09
  • Accepted : 2018.01.28
  • Published : 2019.10.31

Abstract

We design an ingenious view-pooling method named learning-based multiple pooling fusion (LMPF), and apply it to multi-view convolutional neural network (MVCNN) for 3D model classification or retrieval. By this means, multi-view feature maps projected from a 3D model can be compiled as a simple and effective feature descriptor. The LMPF method fuses the max pooling method and the mean pooling method by learning a set of optimal weights. Compared with the hand-crafted approaches such as max pooling and mean pooling, the LMPF method can decrease the information loss effectively because of its "learning" ability. Experiments on ModelNet40 dataset and McGill dataset are presented and the results verify that LMPF can outperform those previous methods to a great extent.

Keywords

Learning-Based Multiple Pooling Fusion;Multi-View Convolutional Neural Network;3D Model Classification;3D Model Retrieval

Acknowledgement

Supported by : National Natural Science Foundation of China

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