Research on plant stem complement based on L1-medial skeleton extraction

doi:10.12302/j.issn.1000-2006.202110040

JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2022, Vol. 46 ›› Issue (1): 40-50.doi: 10.12302/j.issn.1000-2006.202110040

Special Issue: 第二届中国林草计算机大会论文精选

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Research on plant stem complement based on L1-medial skeleton extraction

JIANG Jincen¹(), LI Liandui², WANG Meili¹^,³^,⁴^,^*()

1. College of Information Engineering Northwest A&F University, Yangling 712100, China
2. Shaanxi Academy of Forestry, Xi’an 710082, China
3. Key Laboratory of Agricultural Internet of Things,Ministry of Agriculture and Rural Affairs, Yangling 712100, China
4. Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling 712100, China

Received:2021-10-20 Accepted:2021-11-18 Online:2022-01-30 Published:2022-02-09
Contact: WANG Meili E-mail:jinec@nwsuaf.edu.cn;wml@nwsuaf.edu.cn

Abstract

Abstract:

【Objective】 Plant visualization technology is an important part of digital forestry research. This study used the stem missing points to propose a plant stem complement based on L1-medial skeleton extraction to provide technical support for plant visualization. 【Method】 First, a method to determine the position of the missing part based on topological connection was utilized. Based on the density of the point cloud, the point cloud was classed by using the union-find sets. The weight of the edge of the cluster between nodes was calculated based on the probability graph model, and the minimum spanning tree was used to determine the topological connection between clusters; this enabled the determination of the position of missing parts. Following this, a search-based method was used to determine the set of points to be fitted. The L1-medial local iterative method was used to extract the stem point cloud skeleton. A search algorithm based on nearest neighbor distance was proposed to sort the skeleton point set for the disorder of the point cloud, to determine points to be fitted for missing parts. To address issues with inaccurate skeleton extraction, an iterative optimization method of stem skeleton based on Gauss kernel weight was proposed. This approach used Gauss smoothing stem direction vector, and the Gauss weighted average to calculate stem radius and the updated stem skeleton point set. Finally, a method of point cloud completion of missing parts based on fitting was utilized. The stem radius of missing parts was fitted based on the least squares method, and the stem line of missing parts was fitted based on the Bezier curve. A method based on point cloud density to adjust the generation of the fitting point cloud was proposed to better fit the actual point cloud. 【Result】 The experimental results show that the method proposed in this paper can effectively complete the plant point cloud with leaf and stem missing separation; the fitting result was smooth and has certain practical, physical significance. 【Conclusion】 To some extent, the research results in this paper make up for the defects of scanning, building a complete and realistic three-dimensional point cloud model of plants; this may be more effectively applied to the three-dimensional visual modeling of plants.

Key words: 3D point cloud, detail completion, skeleton optimization, point cloud sorting, spatial fitting, plant visualization technology

CLC Number:

S237
TP391

JIANG Jincen, LI Liandui, WANG Meili. Research on plant stem complement based on L1-medial skeleton extraction[J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2022, 46(1): 40-50.

Figures/Tables 17

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Table 1

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模型 model	倒角距离 chamfer distance	均方误差 mean square error
玫瑰花(图15上)	0.44×10^-5	1.22×10^-3
向日葵(图15中)	1.21×10^-5	1.91×10^-3
罂粟(图15下)	1.03×10^-5	1.84×10^-3

Research on plant stem complement based on L1-medial skeleton extraction

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