LiDAR Point Object Primitive Obtaining Based on Multiconstraint Graph Segmentation

Zhenyang Hui; Zhuoxuan Li; Penggen Cheng; Zhaochen Cai; Xianchun Guo

doi:10.3788/LOP231575

Laser & Optoelectronics Progress, Volume. 61, Issue 10, 1037001(2024)

LiDAR Point Object Primitive Obtaining Based on Multiconstraint Graph Segmentation

Zhenyang Hui^1,2, Zhuoxuan Li^1,2, Penggen Cheng^1,2、*, Zhaochen Cai^1,2, and Xianchun Guo^1,2

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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Figures & Tables(9)

Fig. 1. Flowchart of multiconstraint graph segmentation

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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

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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

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Fig. 4. Study areas. (a) Area1; (b) Area2; (c) Area3; (d) Area4; (e) Area5

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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

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Fig. 6. Comparison of average accuracy of point cloud segmentation results

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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

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Table 1. Accuracy comparison of point cloud segmentation

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Table 1. Accuracy comparison of point cloud segmentation

Area	Method	P /%	R /%	F₁ /%
Area1	DBSCAN	61.36	1.02	2.01
	Spectral clustering	68.19	25.91	37.55
	Proposed method	68.78	84.83	75.97
Area2	DBSCAN	59.17	3.95	7.41
	Spectral clustering	73.31	59.95	65.96
	Proposed method	71.93	99.61	83.54
Area3	DBSCAN	99.20	8.19	15.12
	Spectral clustering	81.61	36.58	50.52
	Proposed method	60.62	93.53	73.56
Area4	DBSCAN	46.02	29.31	35.81
	Spectral clustering	54.41	59.04	56.63
	Proposed method	47.10	64.00	54.30
Area5	DBSCAN	89.20	64.54	74.90
	Spectral clustering	64.64	45.20	59.46
	Proposed method	92.63	22.33	35.98

Table 2. Comparison of segmentation time
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Table 2. Comparison of segmentation time
Area Proposed method DBSCAN Spectral clustering
Mean 1.526 52.835 73.503
Area1 1.951 17.303 82.117
Area2 2.121 107.587 102.463
Area3 2.645 130.124 134.064
Area4 0.519 5.006 29.237
Area5 0.393 4.157 19.635

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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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