Multi-Factor Segmentation of Point Cloud Based on Improved Multi-Rule Region Growing

Wenqi Wang; Zongchun Li; Yongjian Fu; Hua He; Feng Xiong

doi:10.3788/AOS202141.0528001

Acta Optica Sinica, Volume. 41, Issue 5, 0528001(2021)

Multi-Factor Segmentation of Point Cloud Based on Improved Multi-Rule Region Growing

Wenqi Wang, Zongchun Li^*, Yongjian Fu, Hua He, and Feng Xiong

Institute of Geospatial Information, PLA Strategic Support Force Information Engineering University, Zhengzhou, Henan 450001, China

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Figures & Tables(23)

Fig. 1. An example of point cloud multi-factor segmentation based on traditional RG algorithm

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Fig. 2. An example of point cloud multi-factor segmentation based on MRG algorithm

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Fig. 3. An example of normal vector. (a) Point cloud; (b) normal vector of point cloud

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Fig. 4. An example of linear and planar point set segmentation. (a) Linear point set; (b) planar point set

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Fig. 5. Schematic diagram of threshold v_t setting

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Fig. 6. An example of volume change based on correct merging (ΔV=2.13). (a) Unmerged segments; (b) convex hulls of unmerged segments; (c) merged segment; (d) convex hull of merged segments

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Fig. 7. An example of volume change based on incorrect merging (ΔV=17214.04). (a) Unmerged segments; (b) convex hulls of unmerged segments; (c) merged segment; (d) convex hull of merged segments

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Fig. 8. Pseudo-code of merge strategy

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Fig. 9. Point cloud data set. (a) Airborne point cloud (scene I); (b) terrestrial point cloud (scene II); (c) vehicle-borne point cloud (scene III)

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Fig. 10. Reference data of each scene. (a) Scene I; (b) scene II; (c) scene III

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Fig. 11. Segmentation results of planar point set in Scene II and Scene III (N_t=15°). (a) MRG in Scene II; (b) IMRG in Scene II; (c) MRG in Scene III; (d) IMRG in Scene III

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Fig. 12. Incorrect segmentation. (a) RG+mean shift; (b) CSF+DBSCAN; (c) GMIC

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Table 1. 1 Segmentation results of ground in Scene I
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Table 1. 1 Segmentation results of ground in Scene I
Method T_P F_P
RG+mean shift 351708 29669
CSF+DBSCAN 364594 34771
IMRG 340502 38946

Table 1. Segmentation evaluation of building plane in each scene

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Table 1. Segmentation evaluation of building plane in each scene

Method	N_t /(°)	P /%			R /%			F₁ /%
Method	N_t /(°)	Scene I	Scene II	Scene III	Scene I	Scene II	Scene III	Scene I	Scene II	Scene III
	5	97.38	99.23	99.40	84.96	99.06	99.14	90.75	99.15	99.27
MRG	10	97.32	99.40	99.34	77.80	97.41	75.80	86.47	98.40	85.99
	15	93.80	72.38	99.41	68.09	99.90	34.56	78.91	83.94	51.29
	5	97.56	99.33	99.45	87.68	99.00	99.25	92.35	99.16	99.35
IMRG	10	97.50	99.41	99.52	90.94	98.96	88.12	94.11	99.19	93.48
	15	97.23	99.43	99.38	89.91	96.91	64.50	93.43	98.16	78.23

Table 1. 0 F₁ score of segmentation in Scene III

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Table 1. 0 F₁ score of segmentation in Scene III

Method	F₁ /%							Time /s
Method	Building plan	Ground		Vegetation		Car		Time /s
Method 1	98.56		98.19		98.41		93.89	954.01
Method 2	97.52		98.69		97.01		92.72	1750.89
Method 3	93.08		96.18		89.58		82.65	1218.09
Method 4	99.01		98.71		98.90		95.39	987.68
Method 5	91.92		96.10		89.47		79.11	5922.04

Table 2. Segmentation evaluation of ground in each scene

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Table 2. Segmentation evaluation of ground in each scene

Method	N_t /(°)	P /%			R /%			F₁ /%
Method	N_t /(°)	Scene I	Scene II	Scene III	Scene I	Scene II	Scene III	Scene I	Scene II	Scene III
	5	88.41	97.64	98.61	87.87	98.09	99.03	88.14	97.87	98.82
MRG	10	85.78	93.77	84.42	90.65	98.55	99.31	88.15	96.10	91.26
	15	83.09	93.65	67.05	90.55	3.66	99.54	86.66	7.05	80.13
	5	90.11	97.51	98.75	86.23	98.34	99.07	88.13	97.92	98.91
IMRG	10	89.85	97.39	91.50	89.81	98.56	99.31	89.83	97.97	95.25
	15	89.27	92.66	78.99	90.58	98.63	99.45	89.92	95.55	88.05

Table 3. Merge threshold parameters
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Table 3. Merge threshold parameters
Scene d_t /m s_t /m V_t
Scene I 1.54 0.40 18.71
Scene II 0.25 0.05 16.33
Scene III 0.13 0.03 14.61

Table 4. Merge results in Scene I

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Table 4. Merge results in Scene I

Type of ground object		Building plan	Ground	Vegetation
	P /%	89.70	87.53	91.77
Strategy① (M=1150)	R /%	80.50	87.92	92.14
	F₁ /%	84.85	87.72	91.96
	P /%	96.44	89.74	92.99
Strategy② (M=1515)	R /%	90.43	89.19	94.14
	F₁ /%	93.34	89.46	93.56

Table 5. Merge results in Scene II

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Table 5. Merge results in Scene II

Type of ground object		Building plan	Ground	Vegetation	Power line
	P /%	97.67	91.15	99.78	95.72
Strategy①(M=1550)	R /%	96.22	97.25	96.47	60.97
	F₁ /%	96.94	94.10	98.10	74.49
	P /%	99.38	97.42	99.78	99.40
Strategy②(M=2730)	R /%	98.97	98.45	99.79	98.36
	F₁ /%	99.17	97.94	99.79	98.88

Table 6. Merge results in Scene III

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Table 6. Merge results in Scene III

Type of ground object		Building plan	Ground	Vegetation	Car
	P /%	97.09	98.30	98.75	94.67
Strategy①(M=1673)	R /%	98.65	98.18	96.40	94.14
	F₁ /%	97.86	98.24	97.56	94.41
	P /%	98.96	98.72	99.01	95.35
Strategy②(M=2386)	R /%	99.07	98.71	98.79	95.44
	F₁ /%	99.01	98.71	98.90	95.39

Table 7. Parameters for Method 4
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Table 7. Parameters for Method 4
Scene CSF DBSCAN
l_c /m h_c /m r_d /m m_d
Scene I 0.8 0.5 2 5
Scene II 2 0.4 0.5 10
Scene III 2 0.5 0.5 30

Table 8. F₁ score of segmentation in Scene I
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Table 8. F₁ score of segmentation in Scene I
Method F₁ /% Time /s
Buildingplan Ground Vegetation
Method 1 76.84 85.03 90.62 294.03
Method 2 92.99 92.17 94.71 4697.64
Method 3 83.70 93.35 94.42 3230.18
Method 4 93.34 89.46 93.56 372.53

Table 9. F₁ score of segmentation in Scene II

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Table 9. F₁ score of segmentation in Scene II

Method	F₁ /%							Time /s
Method	Building plan	Ground		Vegetation		Power line		Time /s
Method 1	97.00		94.56		97.53		54.95	499.06
Method 2	98.94		97.57		98.74		93.61	943.72
Method 3	98.64		97.51		99.19		79.06	1523.06
Method 4	99.17		97.94		99.79		98.88	830.65
Method 5	97.90		95.71		96.36		78.59	4627.44

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Wenqi Wang, Zongchun Li, Yongjian Fu, Hua He, Feng Xiong. Multi-Factor Segmentation of Point Cloud Based on Improved Multi-Rule Region Growing[J]. Acta Optica Sinica, 2021, 41(5): 0528001

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