Optics and Precision Engineering, Volume. 29, Issue 10, 2504(2021)

Co-segmentation of three-dimensional shape clusters by shape similarity

Jun Yang1、* and Min-min Zhang2
Author Affiliations
  • 1Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou730070, China
  • 2School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou730070, China
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    Figures & Tables(8)
    Construction of spherical neighborhood
    Convolution process of feature aggregation operator
    Hierarchical co-segmentation network
    Segmentation results of point cloud shapes
    • Table 1. Comparison of segmentation results between our algorithm and other algorithms

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      Table 1. Comparison of segmentation results between our algorithm and other algorithms

      Algorithms

      mIoU/%

      pIoU/%

      PointNet5

      80.4

      83.6

      PointASNL10]

      83.3

      86.1

      PointNet++19

      81.9

      85.1

      MACL27

      71.8

      75.2

      SPLATNet28

      83.7

      85.4

      Ours

      83.3

      86.0

    • Table 2. Comparison of IoU of different algorithms on various shapes

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      Table 2. Comparison of IoU of different algorithms on various shapes

      Method

      Air.

      (2 690)

      Bag

      (76)

      Cap

      (55)

      Car

      (898)

      Chair

      (3 758)

      Ear.

      (69)

      Guitar

      (787)

      Knife

      (392)

      PointNet583.478.782.574.989.673.091.585.9
      PointASNL1084.184.787.979.792.273.791.087.2
      PointNet++1982.479.087.777.390.871.891.085.9
      MACL277574.574.054.581.366.588.484.4
      SPLATNet2883.284.389.180.390.775.592.187.1
      Ours83.284.085.780.391.178.590.388.4
      Method

      Lamp

      (1 547)

      Lap.

      (451)

      Motor.

      (202)

      Mug

      (184)

      Pistol

      (283)

      Rocket

      (66)

      Skate.

      (152)

      Table

      (5 271)

      PointNet580.895.365.293.081.257.972.880.6
      PointASNL1084.295.874.495.281.063.076.383.2
      PointNet++1983.795.371.694.181.358.776.482.6
      MACL2777.678.251.387.366.655.062.171.7
      SPLATNet2883.996.375.695.883.864.075.581.8
      Ours85.795.673.795.481.960.076.183.3
    • Table 3. Comparison of segmentation time between our algorithm and other algorithms

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      Table 3. Comparison of segmentation time between our algorithm and other algorithms

      #pointsInputTime
      Training/minTest/ms
      PointNet51 024Coordinates79322
      PointASNL102 048Coordinates--
      PointNet++191 024Coordinates+Normals1 26647
      MACL272 048Coordinates976.860.7
      SPLATNet28--94360.4
      Ours1 024Coordinates85941.9
    • Table 4. Effectiveness verification of different components for co-segmentation of 3D shape clusters

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      Table 4. Effectiveness verification of different components for co-segmentation of 3D shape clusters

      SamplingpIoU/%
      Shared MLPFA Conv
      kNN67.585.2
      Random-PiS73.186.0
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    Jun Yang, Min-min Zhang. Co-segmentation of three-dimensional shape clusters by shape similarity[J]. Optics and Precision Engineering, 2021, 29(10): 2504

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    Paper Information

    Category: Information Sciences

    Received: Apr. 25, 2021

    Accepted: --

    Published Online: Nov. 23, 2021

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

    DOI:10.37188/OPE.20212910.2504

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