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Acta Optica Sinica, Volume. 39, Issue 4, 0415007(2019)

Recognition and Classification for Three-Dimensional Model Based on Deep Voxel Convolution Neural Network

Jun Yang1、*, Shun Wang2, and Peng Zhou1
Author Affiliations
  • 1 School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, Gansu 730070, China
  • 2 School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou, Gansu 730070, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (10)
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    References (0)
    Cited By (5)
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    Figures & Tables(10)
    Voxelization of 3D mesh models. (a) Rendered models; (b) mesh models; (c) voxelization models

    Fig. 1. Voxelization of 3D mesh models. (a) Rendered models; (b) mesh models; (c) voxelization models

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    Data expansion of 3D models by rotation transformation. (a) Toilet models; (b) chair models

    Fig. 2. Data expansion of 3D models by rotation transformation. (a) Toilet models; (b) chair models

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    Convolution operations. (a) 2D; (b) 3D

    Fig. 3. Convolution operations. (a) 2D; (b) 3D

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    Structure of convolutional neural network

    Fig. 4. Structure of convolutional neural network

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    Schematic of 3D model recognition and classification

    Fig. 5. Schematic of 3D model recognition and classification

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    Recognition and classification for 3D models

    Fig. 6. Recognition and classification for 3D models

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    • Table 1. Accuracy rate for recognition and classification of 3D models in expanded dataset with different rotation angles

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      Table 1. Accuracy rate for recognition and classification of 3D models in expanded dataset with different rotation angles

      Rotation angle /(°)Accuracy rate /%
      070.6
      12077.1
      6083.5
      4087.1
      3087.7

    • Table 2. Accuracy rate for recognition and classification of 3D models obtained at different sizes of convolution kernel

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      Table 2. Accuracy rate for recognition and classification of 3D models obtained at different sizes of convolution kernel

      Kernel sizeAccuracy rate /%
      5×5×584.3
      3×3×387.7

    • Table 3. Accuracy rate for recognition and classification of 3D models obtained at different resolutions

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      Table 3. Accuracy rate for recognition and classification of 3D models obtained at different resolutions

      ResolutionRecognition accuracy rate /%
      24×24×2481.1
      32×32×3287.7

    • Table 4. Accuracy rate for recognition and classification of 3D models obtained with different algorithms

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      Table 4. Accuracy rate for recognition and classification of 3D models obtained with different algorithms

      AlgorithmRecognition accuracy rate /%
      SPH[15]68.2
      LFD[16]75.5
      3D ShapeNets[7]77.3
      Proposed algorithm87.7
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    Jun Yang, Shun Wang, Peng Zhou. Recognition and Classification for Three-Dimensional Model Based on Deep Voxel Convolution Neural Network[J]. Acta Optica Sinica, 2019, 39(4): 0415007

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    Paper Information

    Category: Machine Vision

    Received: Oct. 26, 2018

    Accepted: Dec. 25, 2018

    Published Online: May. 10, 2019

    The Author Email:

    DOI:10.3788/AOS201939.0415007

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology