• Acta Optica Sinica
  • Vol. 38, Issue 10, 1028001 (2018)
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
  • show less
    DOI: 10.3788/AOS201838.1028001 Cite this Article Set citation alerts
    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 Copy Citation Text show less
    Core idea of DBSCAN algorithm
    Fig. 1. Core idea of DBSCAN algorithm
    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
    Two-dimensional schematic of a layer of a voxel grid
    Fig. 3. Two-dimensional schematic of a layer of a voxel grid
    Simplified example of a merged cluster
    Fig. 4. Simplified example of a merged cluster
    Original point-cloud
    Fig. 5. Original point-cloud
    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
    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
    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
    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
    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
    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 1. Denosing results using three algorithms
    Denoising operationPoint sizeEuclidean fitness scoreConsuming time /s
    Current framePrevious frame
    Without426184510413.2714566.752
    With36015375496.1251721.215
    Table 2. Point-cloud-matching results before and after denoising
    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
    Download Citation