基于曲率突变分析的点云特征线自动提取

陈华伟; 袁小翠; 吴禄慎; 王晓辉

doi:10.3788/OPE.20192705.1218

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基于曲率突变分析的点云特征线自动提取

信息科学 | 更新时间：2020-08-13

- 基于曲率突变分析的点云特征线自动提取
- Automatic point cloud feature-line extraction algorithm based on curvature-mutation analysis
- 光学精密工程 2019年27卷第5期页码：1218-1228
- 作者机构：
  
  1.贵州师范大学机电工程学院，贵州贵阳 550025
  2.南昌工程学院江西省精密驱动与控制重点实验室，江西南昌 330099
  3.南昌大学机电工程学院，江西南昌 330031
- 作者简介：
  
  [ "陈华伟 (1977-)，男，湖北当阳人，博士后，副教授，分别于2002年和2011年获得北京理工大学硕士和博士学位，主要从事数字化设计与制造、图像处理方面的研究。E-mail:chwei0130@126.com" ]
  [ "袁小翠 (1988-)，女，江西抚州人，博士，讲师，2010年于南昌航空大学获得学士学位，2016年于南昌大学获得博士学位，主要从事图像处理与逆向工程研究。E-mail: yuanxc2012@163.com" ]
- 基金信息：
  
  贵州师范大学博士科研启动项目资助(GZNUD[2017]5号)
- DOI：10.3788/OPE.20192705.1218
  中图分类号： TP391.4
- 收稿日期：2018-10-23，
  
  录用日期：2018-12-14，
  
  纸质出版日期：2019-05-15
- 稿件说明：
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陈华伟, 袁小翠, 吴禄慎, 等. 基于曲率突变分析的点云特征线自动提取[J]. 光学精密工程, 2019,27(5):1218-1228.

Hua-wei CHEN, Xiao-cui YUAN, Lu-chen WU, et al. Automatic point cloud feature-line extraction algorithm based on curvature-mutation analysis[J]. Optics and precision engineering, 2019, 27(5): 1218-1228.
陈华伟, 袁小翠, 吴禄慎, 等. 基于曲率突变分析的点云特征线自动提取[J]. 光学精密工程, 2019,27(5):1218-1228. DOI： 10.3788/OPE.20192705.1218.

Hua-wei CHEN, Xiao-cui YUAN, Lu-chen WU, et al. Automatic point cloud feature-line extraction algorithm based on curvature-mutation analysis[J]. Optics and precision engineering, 2019, 27(5): 1218-1228. DOI： 10.3788/OPE.20192705.1218.

摘要

点云特征线提取是点云模型重构的基础，国内外对此从边缘检测、特征线跟踪和面域分析等方面展开了研究，但由于存在模型多样性、点云数据噪声和不完整性、特征复杂性等问题，看似简单的特征线自动化提取很难实现。从曲率突变点隐含了点云特征线这一论断出发，借鉴图像处理中的区域分割和边缘检测思想，提出了特征线提取中的聚类、细化、分段和排序方案。在具体实现中分别提出了基于连通区域聚类的备选点集分离算法，基于局部影响区域腐蚀的点集细化算法，以及基于组合搜索准则和主成分分析(PCA)双向搜索的特征线分支截断和排序算法。在对比实验中，确定了算法关键参数曲率突变点比例

和方向夹角阈值

的推荐值，并与类似算法对比能提取更多的特征点；在模型实验中，简单几何模型的特征线提取正确率达到了100%，复杂机械零件模型和艺术品模型的特征线提取正确率均达到了85%以上，取得了预想的棱线和特征轮廓线提取效果。算法具有通用性和可扩展性，通过程序优化可获得更好的特征提取效果。

Abstract

Point cloud feature-line extraction is the basis of point cloud model reconstruction. Globally

the research on feature-line extraction from point clouds has been conducted in terms of edge detection

feature line tracking

and surface analysis. However

this seemingly simple operation is actually difficult to realize because of problems such as varied models

point cloud noise and imperfection

and feature complexity. Starting from the assertion that curvature mutated points imply the feature line of a single point cloud

this study presented a clustering

refinement

segmentation

and sorting scheme for feature-line extraction. This scheme was based on the idea of regional segmentation and edge detection in image processing. In the specific implementation of this scheme

this study proposed an alternative point set segmentation algorithm based on connection region clustering

a point set thinning algorithm based on locally influenced area corrosion

and a feature line branching truncation and sorting algorithm based on combinatorial search criteria and principal component analysis bidirectional search. Recommended values of the two key parameters

that was

curvature-mutated point ratio

and directional angle threshold

were determined by conducting a comparative experiment. The proposed method could also extract a greater number of feature points as compared to the similar algorithm. The anticipated contour extraction effect was achieved through model experiments

in which the accuracy ratio of the feature-line extraction for models of simple geometry and complex mechanical parts

as well as artwork models

are 100% and 85%

respectively. The algorithm has the characteristics of generality and extendibility

thus enabling an improved feature extraction effect to be obtained through program improvement.

关键词

Keywords

references

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