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Laser & Optoelectronics Progress, Volume. 62, Issue 8, 0815004(2025)

3D Reconstruction of Irregular Surface Point Cloud Data for Rail Gates Based on Line Laser

Zelin Liu1, Jiaxing Tang2, Shengyi Chen2, Yexuan Chen3, and Tiezheng Guo4、*
Author Affiliations
  • 1State Key Laboratory of Mechanics and Control for Aerospace Structures, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, Jiangsu , China
  • 2School of Computer Engineering, Nanjing Institute of Technology, Nanjing 211167, Jiangsu , China
  • 3School of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211167, Jiangsu , China
  • 4Industrial Center, Nanjing Institute of Technology, Nanjing 211167, Jiangsu , China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (16)
    Equations (4)
    References (34)
    Cited By (1)
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    Figures & Tables(16)
    Comparison of extraction methods. (a) Color segmentation; (b) image subtraction

    Fig. 1. Comparison of extraction methods. (a) Color segmentation; (b) image subtraction

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    Specific steps of image subtraction algorithm

    Fig. 2. Specific steps of image subtraction algorithm

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    Photography agency. (a) Physical picture; (b) structural picture

    Fig. 3. Photography agency. (a) Physical picture; (b) structural picture

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    Improved image subtraction algorithm

    Fig. 4. Improved image subtraction algorithm

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    Comparison of center line extraction by center method, gray gravity center method, and extreme value method. (a) Gray gravity center method; (b) extreme value method; (c) center method

    Fig. 5. Comparison of center line extraction by center method, gray gravity center method, and extreme value method. (a) Gray gravity center method; (b) extreme value method; (c) center method

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    Triangle modeling diagram

    Fig. 6. Triangle modeling diagram

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    Point cloud processing flow

    Fig. 7. Point cloud processing flow

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    Combination filtering effect. (a) Original point cloud; (b) simplify point cloud

    Fig. 8. Combination filtering effect. (a) Original point cloud; (b) simplify point cloud

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    2D view of 3D point cloud. (a) 2D view 1; (b) 2D view 2

    Fig. 9. 2D view of 3D point cloud. (a) 2D view 1; (b) 2D view 2

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    Alpha Shape algorithm edge extraction image

    Fig. 10. Alpha Shape algorithm edge extraction image

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    Internal point cloud reprocessing

    Fig. 11. Internal point cloud reprocessing

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    Point cloud data processing. (a) Characteristic region; (b) combination filtering; (c) statistical filtering; (d) radius filtering

    Fig. 12. Point cloud data processing. (a) Characteristic region; (b) combination filtering; (c) statistical filtering; (d) radius filtering

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    Neighborhood curvature

    Fig. 13. Neighborhood curvature

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    • Table 1. Comparison of advantages and disadvantages of linear structured light center extraction method

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      Table 1. Comparison of advantages and disadvantages of linear structured light center extraction method

      AlgorithmAdvantageDisadvantage
      Center methodSimple operation and fast processing speedIt requires high image quality and low accuracy in complex shapes or noisy environments
      Extreme value methodIt is more applicable to the light stripe with Gaussian distribution of cross section pixelsIt is sensitive to noise and needs to be combined with other techniques for image preprocessing
      Gray gravity center methodGood real-time performance， high precision， image display more coherent and accurateThe light stripe cross section is required to have a complete Gaussian distribution

    • Table 2. Comparison of the results of various filtering processing

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      Table 2. Comparison of the results of various filtering processing

      Type of filterInterior points of the track door /bunnyOutliers of the track door / bunnyTotal number of the track door /bunny
      Statistical filtering497821/3589734266/1349532087/37243
      Radius filtering520498/3677311589/470532087/37243
      Combined filtering496624/3570235463/1541532087/37243

    • Table 3. Comparison of the 3D reconstructed points with the actual points

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      Table 3. Comparison of the 3D reconstructed points with the actual points

      Kinds of pointsConvex point （point 35595）Concave point （point 35606）Flat point （point 35605）
      3D reconstructed pointx=1040.0，y=1218.0x=1054.0，y=1218.0x=1053.0，y=1218.0
      KG=7.02973030932367×10-6KG=-6.43565007881129×10-5KG=0.00020239522159024826
      Hmean=0.005530152557563066Hmean=0.00090789094372733Hmean=0.000498743424155213
      Actual pointHmean=0.005963114369145359Hmean=0.00083549609518396Hmean=0.000463485069410203
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    Zelin Liu, Jiaxing Tang, Shengyi Chen, Yexuan Chen, Tiezheng Guo. 3D Reconstruction of Irregular Surface Point Cloud Data for Rail Gates Based on Line Laser[J]. Laser & Optoelectronics Progress, 2025, 62(8): 0815004

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    Paper Information

    Category: Machine Vision

    Received: Aug. 7, 2024

    Accepted: Sep. 30, 2024

    Published Online: Apr. 3, 2025

    The Author Email: Tiezheng Guo (guotiezheng@njit.edu.cn)

    DOI:10.3788/LOP241818

    CSTR:32186.14.LOP241818

    Topics

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