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Laser & Optoelectronics Progress, Volume. 57, Issue 10, 101510(2020)

Point Cloud Model Recognition and Classification Based on K-Nearest Neighbor Convolutional Neural Network

Ting Yu and Jun Yang*
Author Affiliations
  • School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, Gansu 730070, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (11)
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    References (27)
    Cited By (4)
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    Figures & Tables(11)
    Construction process of the local neighborhood

    Fig. 1. Construction process of the local neighborhood

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    Schematic diagram of feature extraction process

    Fig. 2. Schematic diagram of feature extraction process

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    JANet model

    Fig. 3. JANet model

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    KNN-CNN model

    Fig. 4. KNN-CNN model

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    Graph of activation function

    Fig. 5. Graph of activation function

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    Three representations of three-dimensional models. (a) Rendered model; (b) mesh model; (c) 3D point cloud model

    Fig. 6. Three representations of three-dimensional models. (a) Rendered model; (b) mesh model; (c) 3D point cloud model

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    Examples of misclassified point cloud models. (a) Television stand; (b) chair; (c) plant; (d) flower pot

    Fig. 7. Examples of misclassified point cloud models. (a) Television stand; (b) chair; (c) plant; (d) flower pot

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    • Table 1. Recognition accuracy of different algorithms on ModelNet40 dataset

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      Table 1. Recognition accuracy of different algorithms on ModelNet40 dataset

      MethodInputMA /%OA /%
      PointNet[4]102486.289.2
      PointNet++[20]1024-90.7
      PointNet++[20]normal-91.9
      KCNet[22]1024-91.0
      Kd-Net(depth 10)[24]102486.390.6
      Kd-Net(depth 15)[24]3276888.591.8
      Ours102489.992.0

    • Table 2. Comparison of recognition accuracy of each category model in ModelNet40 test setunit:%

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      Table 2. Comparison of recognition accuracy of each category model in ModelNet40 test setunit:%

      CategoryPointNet[4]Our model
      Airplane100.0100.0
      Bathtub86.094.0
      Bed97.099.0
      Bench70.075.0
      Bookshelf91.099.0
      Bottle94.098.0
      Bow90.0100.0
      Car98.099.0
      Chair97.098.0
      Cone95.0100.0
      Cup80.075.0
      Curtain90.095.0
      Desk80.287.2
      Door80.095.0
      Dresser72.176.7
      Flower pot20.020.0
      Glass box98.092.0
      Guitar100.0100.0
      Keyboard100.0100.0
      Lamp95.090.0
      Laptop100.0100.0
      Mantel95.096.0
      Monitor96.099.0
      Night stand72.182.6
      Person90.0100.0
      Piano86.095.0
      Plant76.082.0
      Radio75.090.0
      Range hood92.095.0
      Sink80.080.0
      Sofa95.096.0
      Stairs85.095.0
      Stool85.085.0
      Table81.081.0
      Tent95.095.0
      Toilet98.098.0
      Television stand79.091.0
      Vase80.083.0
      Wardrobe60.070.0
      Xbox85.090.0

    • Table 3. Effect of k value on OA

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      Table 3. Effect of k value on OA

      k1k2OA /%Time /h
      151588.572.0
      201091.272.1
      202091.393.3
      302091.6107.3

    • Table 4. Recognition accuracy of different optimization schemesunit:%

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      Table 4. Recognition accuracy of different optimization schemesunit:%

      Activate functionOA
      SELU91.3
      ReLU+BN91.6
      ELU+BN91.8
      SELU+BN92.0
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    Ting Yu, Jun Yang. Point Cloud Model Recognition and Classification Based on K-Nearest Neighbor Convolutional Neural Network[J]. Laser & Optoelectronics Progress, 2020, 57(10): 101510

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    Paper Information

    Category: Machine Vision

    Received: Sep. 9, 2019

    Accepted: Sep. 17, 2019

    Published Online: May. 8, 2020

    The Author Email: Jun Yang (yangj@mail.lzjtu.cn)

    DOI:10.3788/LOP57.101510

    Topics

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