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Acta Optica Sinica, Volume. 38, Issue 10, 1028001(2018)

Large-Scale Scattered Point-Cloud Denoising Based on VG-DBSCAN Algorithm

Kai Zhao1、*, Youchun Xu2、*, Yongle Li2, and Rendong Wang1
Author Affiliations
  • 1 Army Military Transportation University, Tianjin 300161, China
  • 2 Institute of Military Transportation, Tianjin 300161, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (12)
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    Cited By (7)
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    Figures & Tables(12)
    Core idea of DBSCAN algorithm

    Fig. 1. Core idea of DBSCAN algorithm

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    Dividing a three-dimensional voxel grid. (a) Three-dimensional voxel grid; (b) voxel cell

    Fig. 2. Dividing a three-dimensional voxel grid. (a) Three-dimensional voxel grid; (b) voxel cell

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    Two-dimensional schematic of a layer of a voxel grid

    Fig. 3. Two-dimensional schematic of a layer of a voxel grid

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    Simplified example of a merged cluster

    Fig. 4. Simplified example of a merged cluster

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    Original point-cloud

    Fig. 5. Original point-cloud

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    Results using statistical outlier removal algorithm.(a) MeanK=10; (b) MeanK=30; (c) MeanK=50

    Fig. 6. Results using statistical outlier removal algorithm.(a) MeanK=10; (b) MeanK=30; (c) MeanK=50

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    Results using radius outlier removal algorithm. (a) MinNeighbors=5; (b) MinNeighbors=10; (c) MinNeighbors=15

    Fig. 7. Results using radius outlier removal algorithm. (a) MinNeighbors=5; (b) MinNeighbors=10; (c) MinNeighbors=15

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    Denoising results using VG-DBSCAN algorithm. (a) Eps=1, MinPts=10; (b) Eps=1, MinPts=15; (c) Eps=1, MinPts=20

    Fig. 8. Denoising results using VG-DBSCAN algorithm. (a) Eps=1, MinPts=10; (b) Eps=1, MinPts=15; (c) Eps=1, MinPts=20

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    Local denoising results using VG-DBSCAN algorithm. (a) Before denoising; (b) after denoising

    Fig. 9. Local denoising results using VG-DBSCAN algorithm. (a) Before denoising; (b) after denoising

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    Point-cloud-matching results after denoising. (a) Before matching; (b) after matching

    Fig. 10. Point-cloud-matching results after denoising. (a) Before matching; (b) after matching

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    • Table 1. Denosing results using three algorithms

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      Table 1. Denosing results using three algorithms

      AlgorithmParameterPoint sizeConsuming time /ms
      OriginalAfter denoising
      MeanK=1038284180.23
      Statistical outlier removalMeanK=304261837945227.68
      MeanK=5036579288.85
      MinNeighbors=539744465.37
      Radius outlier removalMinNeighbors=104261838543466.91
      MinNeighbors=1537349471.15
      Eps=1, MinPts=103824892.74
      VG-DBSCANEps=1, MinPts=154261837153109.39
      Eps=1, MinPts=2036142124.69

    • Table 2. Point-cloud-matching results before and after denoising

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      Table 2. Point-cloud-matching results before and after denoising

      Denoising operationPoint sizeEuclidean fitness scoreConsuming time /s
      Current framePrevious frame
      Without426184510413.2714566.752
      With36015375496.1251721.215
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    Kai Zhao, Youchun Xu, Yongle Li, Rendong Wang. Large-Scale Scattered Point-Cloud Denoising Based on VG-DBSCAN Algorithm[J]. Acta Optica Sinica, 2018, 38(10): 1028001

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    Paper Information

    Category: Remote Sensing and Sensors

    Received: --

    Accepted: --

    Published Online: May. 9, 2019

    The Author Email:

    DOI:10.3788/AOS201838.1028001

    Topics

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