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Laser & Optoelectronics Progress, Volume. 58, Issue 6, 610015(2021)

Point Cloud Filtering Algorithm Based on Image Processing

Zhang Jianmin1, Chen Fujian2, and Long Jiale1、*
Author Affiliations
  • 1Faculty of Intelligent Manufacturing, Wuyi University, Jiangmen, Guangdong 529020, China
  • 2School of Electronic and Information Engineering, Shenzhen University, Shenzhen, Guangdong 518060, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (19)
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    References (18)
    Cited By (4)
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    Paper Information
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    Figures & Tables(19)
    Object 1 to be measured

    Fig. 1. Object 1 to be measured

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    Front view of point cloud

    Fig. 2. Front view of point cloud

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    Side view of point cloud

    Fig. 3. Side view of point cloud

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    Schematic of point cloud filtering algorithm based on image processing

    Fig. 4. Schematic of point cloud filtering algorithm based on image processing

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    Flow chart of point cloud filtering algorithm based on image processing

    Fig. 5. Flow chart of point cloud filtering algorithm based on image processing

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    Correspondence diagram of point cloud coordinates, image coordinates, and point cloud number

    Fig. 6. Correspondence diagram of point cloud coordinates, image coordinates, and point cloud number

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    Schematic of image processing (selecting the largest connected area)

    Fig. 7. Schematic of image processing (selecting the largest connected area)

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    Schematic of image processing (selecting the connected area with the largest number of point clouds)

    Fig. 8. Schematic of image processing (selecting the connected area with the largest number of point clouds)

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    Filtering process of point cloud for object 1. (a) Result of k nearest neighbor filtering; (b) point cloud mapped image; (c) image closing operation; (d) image filtering processing; (e) result of point cloud filtering (side view); (f) result of point cloud filtering (front view)

    Fig. 9. Filtering process of point cloud for object 1. (a) Result of k nearest neighbor filtering; (b) point cloud mapped image; (c) image closing operation; (d) image filtering processing; (e) result of point cloud filtering (side view); (f) result of point cloud filtering (front view)

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    Filtering process of point cloud for object 2. (a) Object 2 to be measured; (b) front view of point cloud; (c) side view of point cloud; (d) result of k nearest neighbor filtering; (e) point cloud mapped image; (f) image closing operation; (g) image filtering processing; (h) result of point cloud filtering (front view); (i) result of point cloud filtering (side view)

    Fig. 10. Filtering process of point cloud for object 2. (a) Object 2 to be measured; (b) front view of point cloud; (c) side view of point cloud; (d) result of k nearest neighbor filtering; (e) point cloud mapped image; (f) image closing operation; (g) image filtering processing; (h) result of point cloud filtering (front view); (i) result of point cloud filtering (side view)

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    Filtering process of point cloud for object 3. (a) Object 3 to be measured; (b) front view of point cloud; (c) side view of point cloud; (d) result of k nearest neighbor filtering; (e) point cloud mapped image; (f) image closing operation; (g) image filtering processing; (h) result of point cloud filtering (front view); (i) result of point cloud filtering (side view)

    Fig. 11. Filtering process of point cloud for object 3. (a) Object 3 to be measured; (b) front view of point cloud; (c) side view of point cloud; (d) result of k nearest neighbor filtering; (e) point cloud mapped image; (f) image closing operation; (g) image filtering processing; (h) result of point cloud filtering (front view); (i) result of point cloud filtering (side view)

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    Filtering results of different algorithms for object 1. (a) Proposed algorithm; (b) k nearest neighbor filtering algorithm; (c) radius filtering algorithm; (d) voxel filtering algorithm

    Fig. 12. Filtering results of different algorithms for object 1. (a) Proposed algorithm; (b) k nearest neighbor filtering algorithm; (c) radius filtering algorithm; (d) voxel filtering algorithm

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    Filtering results of different algorithms for object 2. (a) Proposed algorithm; (b) k nearest neighbor filtering algorithm; (c) radius filtering algorithm; (d) voxel filtering algorithm

    Fig. 13. Filtering results of different algorithms for object 2. (a) Proposed algorithm; (b) k nearest neighbor filtering algorithm; (c) radius filtering algorithm; (d) voxel filtering algorithm

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    Filtering results of different algorithms for object 3. (a) Proposed algorithm; (b) k nearest neighbor filtering algorithm; (c) radius filtering algorithm; (d) voxel filtering algorithm

    Fig. 14. Filtering results of different algorithms for object 3. (a) Proposed algorithm; (b) k nearest neighbor filtering algorithm; (c) radius filtering algorithm; (d) voxel filtering algorithm

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    • Table 1. Time consuming of largest connected area method

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      Table 1. Time consuming of largest connected area method

      Model nameNumber of raw point cloudsNumber of filtered point cloudsTime consuming in knearest neighbor filtering /sTime consuming in remaining processing /sTotal time /s
      Object 11889251677960.53960.36890.9085
      Object 253838531720.11810.06110.1792
      Object 364933315280.16160.26650.4281

    • Table 2. Time consuming of the connected area with the largest number of point clouds

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      Table 2. Time consuming of the connected area with the largest number of point clouds

      Model nameNumber of raw point cloudsNumber of filtered point cloudsTime consuming in k nearest neighbor filtering /sTime consuming in remaining processing /sTotal time /s
      Object 11889251677960.53960.41200.9516
      Object 253838531720.11810.06150.1796
      Object 364933315280.16160.27430.4359

    • Table 3. Filtering effect analysis of object 1

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      Table 3. Filtering effect analysis of object 1

      ParameterProposed algorithmk nearest neighbor filtering algorithmRadius filtering algorithmVoxel filteringalgorithm
      Number of filtered point clouds16779518702818794090878
      Time consuming in filtering /s0.9090.4860.7550.081
      NTP16773616779616785712702
      NFP60192322008378176
      P /%99.96489.71789.31413.977

    • Table 4. Filtering effect analysis of object 2

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      Table 4. Filtering effect analysis of object 2

      ParameterProposed algorithmk nearest neighbor filtering algorithmRadius filtering algorithmVoxel filtering algorithm
      Number of filtered point clouds53172534175363724428
      Time consuming in filtering /s0.3410.1340.2130.025
      NTP5316653172531783518
      NFP624545920910
      P /%99.98899.54199.14414.402

    • Table 5. Filtering effect analysis of object 3

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      Table 5. Filtering effect analysis of object 3

      ParameterProposed algorithmk nearest neighbor filtering algorithmRadius filtering algorithmVoxel filtering algorithm
      Number of filtered point clouds31528512946391561061
      Time consuming in filtering /s0.4270.1650.2730.028
      NTP31429315283162927262
      NFP99197663228633799
      P /%99.68661.46549.48644.647
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    Zhang Jianmin, Chen Fujian, Long Jiale. Point Cloud Filtering Algorithm Based on Image Processing[J]. Laser & Optoelectronics Progress, 2021, 58(6): 610015

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    Paper Information

    Category: Image Processing

    Received: Aug. 12, 2020

    Accepted: --

    Published Online: Mar. 11, 2021

    The Author Email: Jiale Long (longjiale_528@126.com)

    DOI:10.3788/LOP202158.0610015

    Topics

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