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Laser & Optoelectronics Progress, Volume. 62, Issue 2, 0215006(2025)

KD-Tree-Guided Surface-Curvature-Driven SteelBillet Point-Cloud Simplification Algorithm

Zichao Chen1,2、*, Wen Ren2,3, and Long Wu2,3
Author Affiliations
  • 1School of Mechanical and Electronic Engineering, Fujian Agriculture and Forestry University,Fuzhou 350002, Fujian , China
  • 2Key Laboratory of Equipment Intelligence Control of Fujian Province, Sanming University,Sanming 365004, Fujian China
  • 3College of Mechanical and Electrical Engineering, Sanming University, Sanming 365004, Fujian , China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (20)
    Equations (26)
    References (21)
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    Figures & Tables(20)
    KDSCP algorithm flow

    Fig. 1. KDSCP algorithm flow

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    Three-dimensional space KD-tree segmentation diagram

    Fig. 2. Three-dimensional space KD-tree segmentation diagram

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    KD-tree partition plan

    Fig. 3. KD-tree partition plan

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    k-nearest neighbor search example

    Fig. 4. k-nearest neighbor search example

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    Centroid nearest neighbor point simplification method

    Fig. 5. Centroid nearest neighbor point simplification method

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    Collection field of point cloud data for steel billets

    Fig. 6. Collection field of point cloud data for steel billets

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    Schematic diagram of structured light scanner data acquisition

    Fig. 7. Schematic diagram of structured light scanner data acquisition

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    Simplification results and packaging model of KDSCP. (a) (h) Original point cloud encapsulation model; (b)(i) simplify 55.56%; (c)(j) simplify 65.44%; (d)(k) simplify 75.31%; (e)(l) simplify 85.19%

    Fig. 8. Simplification results and packaging model of KDSCP. (a) (h) Original point cloud encapsulation model; (b)(i) simplify 55.56%; (c)(j) simplify 65.44%; (d)(k) simplify 75.31%; (e)(l) simplify 85.19%

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    Simplification results and packaging model of random sampling method. (a)(h) Original point cloud encapsulation model; (b)(i) simplify 55.56%; (c)(j) simplify 65.44%; (d)(k) simplify 75.31%; (e)(l) simplify 85.19%

    Fig. 9. Simplification results and packaging model of random sampling method. (a)(h) Original point cloud encapsulation model; (b)(i) simplify 55.56%; (c)(j) simplify 65.44%; (d)(k) simplify 75.31%; (e)(l) simplify 85.19%

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    Simplification results and packaging model of Improved curvature sampling method. (a)(h) Original point cloud encapsulation model; (b) (i) simplify 55.62%; (c)(j) simplify 65.95%; (d) (k) simplify 75.26%; (e) (l) simplify 85.19%

    Fig. 10. Simplification results and packaging model of Improved curvature sampling method. (a)(h) Original point cloud encapsulation model; (b) (i) simplify 55.62%; (c)(j) simplify 65.95%; (d) (k) simplify 75.26%; (e) (l) simplify 85.19%

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    Turbine 3D model

    Fig. 11. Turbine 3D model

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    KDSCP-based encapsulated turbine model. (a) Original turbine model; (b) simplify 54.75%; (c) simplify 64.77%; (d) simplify 75.43%; (e) simplify 85.45%

    Fig. 12. KDSCP-based encapsulated turbine model. (a) Original turbine model; (b) simplify 54.75%; (c) simplify 64.77%; (d) simplify 75.43%; (e) simplify 85.45%

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    Random sampling method-based encapsulated turbine model. (a) Original turbine model; (b) simplify 54.75%; (c) simplify 64.77%; (d) simplify 75.43%; (e) simplify 85.45%

    Fig. 13. Random sampling method-based encapsulated turbine model. (a) Original turbine model; (b) simplify 54.75%; (c) simplify 64.77%; (d) simplify 75.43%; (e) simplify 85.45%

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    Improved curvature sampling method-based encapsulated turbine model. (a) Original turbine model; (b) simplify 54.77%; (c) simplify 65.14%; (d) simplify 75.42%; (e) simplify 85.61%

    Fig. 14. Improved curvature sampling method-based encapsulated turbine model. (a) Original turbine model; (b) simplify 54.77%; (c) simplify 65.14%; (d) simplify 75.42%; (e) simplify 85.61%

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    Key feature areas of steel billet point cloud

    Fig. 15. Key feature areas of steel billet point cloud

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    Comparison of key feature retention rates of three methods under different simplification rates

    Fig. 16. Comparison of key feature retention rates of three methods under different simplification rates

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    Comparison of PSNR for steel billet simplification results using three methods under different reduction rates

    Fig. 17. Comparison of PSNR for steel billet simplification results using three methods under different reduction rates

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    Comparison of point cloud information entropy in simplified results of steel billet

    Fig. 18. Comparison of point cloud information entropy in simplified results of steel billet

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    Comparison of point cloud information entropy in simplified results of turbine

    Fig. 19. Comparison of point cloud information entropy in simplified results of turbine

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    • Table 1. Comparison of hausdorff distance for simplification results of three methods

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      Table 1. Comparison of hausdorff distance for simplification results of three methods

      Simplification rate /%Hausdorff distance /mm
      55657585
      Random sampling method20.083720.217422.218323.2245
      Improved curvature sampling method18.758619.452319.995822.1912
      KDSCP16.204617.450718.176320.3291
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    Zichao Chen, Wen Ren, Long Wu. KD-Tree-Guided Surface-Curvature-Driven SteelBillet Point-Cloud Simplification Algorithm[J]. Laser & Optoelectronics Progress, 2025, 62(2): 0215006

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    Paper Information

    Category: Machine Vision

    Received: Apr. 28, 2024

    Accepted: Jun. 3, 2024

    Published Online: Jan. 6, 2025

    The Author Email:

    DOI:10.3788/LOP241190

    CSTR:32186.14.LOP241190

    Topics

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