• Laser & Optoelectronics Progress
  • Vol. 61, Issue 10, 1037001 (2024)
Zhenyang Hui1、2, Zhuoxuan Li1、2, Penggen Cheng1、2、*, Zhaochen Cai1、2, and Xianchun Guo1、2
Author Affiliations
  • 1School of Surveying, Mapping and Spatial Information Engineering, East China University of Technology, Nanchang 330013, Jiangxi , China
  • 2Key Laboratory of Mine Environmental Monitoring and Improving around Poyang Lake, Ministry of Natural Resources, East China University of Technology, Nanchang 330013, Jiangxi , China
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    DOI: 10.3788/LOP231575 Cite this Article Set citation alerts
    Zhenyang Hui, Zhuoxuan Li, Penggen Cheng, Zhaochen Cai, Xianchun Guo. LiDAR Point Object Primitive Obtaining Based on Multiconstraint Graph Segmentation[J]. Laser & Optoelectronics Progress, 2024, 61(10): 1037001 Copy Citation Text show less
    Flowchart of multiconstraint graph segmentation
    Fig. 1. Flowchart of multiconstraint graph segmentation
    Angle of normal vectors. (a) Sketch map of normal vector angle of the neighboring building points; (b) sketch map of normal vector angle of the neighboring vegetation points
    Fig. 2. Angle of normal vectors. (a) Sketch map of normal vector angle of the neighboring building points; (b) sketch map of normal vector angle of the neighboring vegetation points
    Graph segmentation based on multi-constraints. (a) Result of graph segmentation based on multi-constraints; (b) enlarged version of area I; (c) enlarged version of area II
    Fig. 3. Graph segmentation based on multi-constraints. (a) Result of graph segmentation based on multi-constraints; (b) enlarged version of area I; (c) enlarged version of area II
    Study areas. (a) Area1; (b) Area2; (c) Area3; (d) Area4; (e) Area5
    Fig. 4. Study areas. (a) Area1; (b) Area2; (c) Area3; (d) Area4; (e) Area5
    Comparison of the point cloud segmentation results processed by different methods. (a) Proposed method; (b) DBSCAN; (c) spectral clustering method; (d) referenced segmentation result
    Fig. 5. Comparison of the point cloud segmentation results processed by different methods. (a) Proposed method; (b) DBSCAN; (c) spectral clustering method; (d) referenced segmentation result
    Comparison of average accuracy of point cloud segmentation results
    Fig. 6. Comparison of average accuracy of point cloud segmentation results
    Graph segmentation based on multi-constraint results with different ς. (a) ς=1°; (b) ς=10°; (c) ς=15°; (d) reference segmentation result
    Fig. 7. Graph segmentation based on multi-constraint results with different ς. (a) ς=1°; (b) ς=10°; (c) ς=15°; (d) reference segmentation result
    AreaMethodP /%R /%F1 /%
    Area1DBSCAN61.361.022.01
    Spectral clustering68.1925.9137.55
    Proposed method68.7884.8375.97
    Area2DBSCAN59.173.957.41
    Spectral clustering73.3159.9565.96
    Proposed method71.9399.6183.54
    Area3DBSCAN99.208.1915.12
    Spectral clustering81.6136.5850.52
    Proposed method60.6293.5373.56
    Area4DBSCAN46.0229.3135.81
    Spectral clustering54.4159.0456.63
    Proposed method47.1064.0054.30
    Area5DBSCAN89.2064.5474.90
    Spectral clustering64.6445.2059.46
    Proposed method92.6322.3335.98
    Table 1. Accuracy comparison of point cloud segmentation
    AreaProposed methodDBSCANSpectral clustering
    Mean1.52652.83573.503
    Area11.95117.30382.117
    Area22.121107.587102.463
    Area32.645130.124134.064
    Area40.5195.00629.237
    Area50.3934.15719.635
    Table 2. Comparison of segmentation time
    Zhenyang Hui, Zhuoxuan Li, Penggen Cheng, Zhaochen Cai, Xianchun Guo. LiDAR Point Object Primitive Obtaining Based on Multiconstraint Graph Segmentation[J]. Laser & Optoelectronics Progress, 2024, 61(10): 1037001
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