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Laser & Optoelectronics Progress, Volume. 62, Issue 2, 0215003(2025)

Point Cloud Filtering Method for Suburban Areas Based on the Adaptive Local Filter Threshold

Zhipeng Zhang1、*, Xin Liu1, Tao Shi1, Ershen Wang2, and Kuan He3
Author Affiliations
  • 1Shenyang Power Supply Company, State Grid Liaoning Electric Power Co., Ltd., Shenyang 110052, Liaoning , China
  • 2College of Civil Aviation, Shenyang Aerospace University, Shenyang 110136, Liaoning , China
  • 3College of Mechatronics Engineering, Shenyang Aerospace University, Shenyang 110136, Liaoning , China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (12)
    Equations (12)
    References (15)
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    Figures & Tables(12)
    Cloth simulation filtering algorithm principle

    Fig. 1. Cloth simulation filtering algorithm principle

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    CSF algorithm error principle. (a) Steep slope error; (b) elevation correction error

    Fig. 2. CSF algorithm error principle. (a) Steep slope error; (b) elevation correction error

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    Algorithm process chart

    Fig. 3. Algorithm process chart

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    Full point cloud rendering

    Fig. 4. Full point cloud rendering

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    The original point cloud maps of the survey area. (a) Test area 1; (b) teat area 2

    Fig. 5. The original point cloud maps of the survey area. (a) Test area 1; (b) teat area 2

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    Schematic diagrams of the experimental area. (a) Test area 1; (b) test area 2

    Fig. 6. Schematic diagrams of the experimental area. (a) Test area 1; (b) test area 2

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    Model diagrams of the experimental area. (a) Model of test area 1; (b) model of test area 2

    Fig. 7. Model diagrams of the experimental area. (a) Model of test area 1; (b) model of test area 2

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    The results of the proposed algorithm, fabric simulation filtering algorithm, and local slope filtering algorithm. (a) The filtering results of the proposed algorithm in test area 1; (b) the filtering results of the proposed algorithm in test area 2; (c) the filtering results of CSF method in test area 1; (d) the filtering results of CSF in test area 2; (e) the filtering result of local slope filtering in test area 1; (f) the filtering results of local slope filtering in test area 2

    Fig. 8. The results of the proposed algorithm, fabric simulation filtering algorithm, and local slope filtering algorithm. (a) The filtering results of the proposed algorithm in test area 1; (b) the filtering results of the proposed algorithm in test area 2; (c) the filtering results of CSF method in test area 1; (d) the filtering results of CSF in test area 2; (e) the filtering result of local slope filtering in test area 1; (f) the filtering results of local slope filtering in test area 2

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    Comparison of the average accuracy of the two algorithms

    Fig. 9. Comparison of the average accuracy of the two algorithms

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    Comparison results of the proposed algorithm and the CSF algorithm on the ISPRS public dataset

    Fig. 10. Comparison results of the proposed algorithm and the CSF algorithm on the ISPRS public dataset

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    • Table 1. The accuracy of the proposed algorithm, classical CSF algorithm, and partially slope based filtering algorithm

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      Table 1. The accuracy of the proposed algorithm, classical CSF algorithm, and partially slope based filtering algorithm

      AreaAlgorithmtype Ⅰtype ⅡtotalKappa
      Testarea 1CSF32.520.2018.7363.74
      Partially slope4.306.465.0488.85
      Proposed algorithm7.330.944.6090.70
      Testarea 1CSF10.074.978.3382.11
      Partially slope8.058.404.9789.96
      Proposed algorithm0.908.523.5192.08

    • Table 2. Comparison results of the proposed algorithm and the CSF algorithm on the ISPRS public dataset

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      Table 2. Comparison results of the proposed algorithm and the CSF algorithm on the ISPRS public dataset

      Fig. No.Algorithmtype Ιtype ⅡtotalKappa
      Fig. 10（a）Proposed algorithm0.28010.09270.20020.6051
      Fig. 10（b）CSF0.48450.03230.29170.4476
      Fig. 10（c）Proposed algorithm0.01990.04960.03440.9310
      Fig. 10（d）CSF0.07150.02770.05010.8999
      Fig. 10（e）Proposed algorithm0.00580.19680.04820.8511
      Fig. 10（f）CSF0.05850.13990.07660.7834
      Fig. 10（g）Proposed algorithm0.04920.15180.08120.8085
      Fig. 10（h）CSF0.12810.10220.12000.7335
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    Zhipeng Zhang, Xin Liu, Tao Shi, Ershen Wang, Kuan He. Point Cloud Filtering Method for Suburban Areas Based on the Adaptive Local Filter Threshold[J]. Laser & Optoelectronics Progress, 2025, 62(2): 0215003

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    Paper Information

    Category: Machine Vision

    Received: Mar. 18, 2024

    Accepted: Jun. 3, 2024

    Published Online: Jan. 20, 2025

    The Author Email:

    DOI:10.3788/LOP240913

    CSTR:32186.14.LOP240913

    Topics

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