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Laser & Optoelectronics Progress, Volume. 61, Issue 10, 1015002(2024)

Attention-Based Multi-Stage Network for Point Cloud Completion

Xiyang Yin1, Pei Zhou1,2, and Jiangping Zhu1,2、*
Author Affiliations
  • 1College of Computer Science, Sichuan University, Chengdu 610065, Sichuan, China
  • 2National Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University, Chengdu 610065, Sichuan, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (12)
    Equations (9)
    References (25)
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    Figures & Tables(12)
    Overall architecture of AMCNet

    Fig. 1. Overall architecture of AMCNet

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    Illustration of the aggregation of local features in pyramid feature extractor

    Fig. 2. Illustration of the aggregation of local features in pyramid feature extractor

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    GAB. (a) Improved cross-attention module; (b) self-attention module; (c) channel attention SELayer module (⊕ denotes element-wise addition and ⊙ denotes Hadamard product)

    Fig. 3. GAB. (a) Improved cross-attention module; (b) self-attention module; (c) channel attention SELayer module (⊕ denotes element-wise addition and ⊙ denotes Hadamard product)

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    Structure of the point generator

    Fig. 4. Structure of the point generator

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    Visualization of completion results of different networks on PCN dataset

    Fig. 5. Visualization of completion results of different networks on PCN dataset

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    Visualization of completion results of different networks in terms of the chair class

    Fig. 6. Visualization of completion results of different networks in terms of the chair class

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    Visualization of completion results at different resolutions of input

    Fig. 7. Visualization of completion results at different resolutions of input

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    Visualization of completion results of different point cloud block size

    Fig. 8. Visualization of completion results of different point cloud block size

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    • Table 1. Point cloud completion comparison on PCN dataset in terms of CD(lower is better)

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      Table 1. Point cloud completion comparison on PCN dataset in terms of CD(lower is better)

      ModelCD /103
      AveragePlaneCabinetCarChairLampCouchTableBoat
      FoldingNet14.319.4915.8012.6115.5516.4115.9713.6514.99
      PCN9.645.5022.7010.638.7011.0011.3411.688.59
      GRNet8.836.4510.379.459.417.9610.515.448.04
      PMP-Net8.735.6511.249.649.516.9510.838.727.25
      PoinTr8.384.7510.478.689.397.7510.937.757.29
      SnowflakeNet7.214.299.168.087.896.079.236.556.40
      PointAttN6.863.879.007.637.435.908.686.326.09
      Ours6.453.588.747.366.865.288.325.885.69

    • Table 2. Effect of the resolution of the input point cloud

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      Table 2. Effect of the resolution of the input point cloud

      Resolution20481024512256
      CD6.456.546.667.23

    • Table 3. Effect of point cloud block size

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      Table 3. Effect of point cloud block size

      SizeK=16K=32K=64
      CD6.486.456.47

    • Table 4. Effect of attention module

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      Table 4. Effect of attention module

      ModelSELayerSkip connectionCD
      A6.61
      B√6.58
      C√6.49
      AMCNet√√6.45
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    Xiyang Yin, Pei Zhou, Jiangping Zhu. Attention-Based Multi-Stage Network for Point Cloud Completion[J]. Laser & Optoelectronics Progress, 2024, 61(10): 1015002

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    Paper Information

    Category: Machine Vision

    Received: Jul. 19, 2023

    Accepted: Oct. 9, 2023

    Published Online: Mar. 20, 2024

    The Author Email: Zhu Jiangping (zjp16@scu.edu.cn)

    DOI:10.3788/LOP231758

    Topics

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