摘要
针对逆向工程领域中散乱点云模型过渡线及细节特征线提取不完整问题,提出一种法向量区域聚类的特征线提取方法。采用自适应邻域的主成分分析法估算模型的法向量,利用萤火虫算法优化的模糊C均值聚类算法对法向量的进行聚类实现模型的有效分割。构造点集剔除与合并准则从各分割块边界点集中析取候选特征点,再以局部邻域主轴方向为基准提取特征点。实验结果表明:简单模型的特征线基本可准确完整提取,相对复杂模型的特征线数量提取率可达90%,长度提取率达到了85%。算法具有良好的自适应性和准确性,能有效提取点云模型尖锐特征和细节特征,并尽可能多地保留模型过渡特征。
In oder to solve the problems of incomplete extraction of transition lines and detail feature lines of scattered point cloud models in reverse engineering,a method of extracting feature lines was proposed based on normal vector region clustering.Principal component analysis of adaptive neighborhood was used to estimate the normal vector of the model,and the FA optimized FCM clustering algorithm was introduced to cluster the normal vectors for realizing the effective segmentation of the model.The candidate feature points were extracted from the boundary points of each block by constructing the rule of eliminating and merging point sets,and then the feature points were extracted based on the principal axis direction of local neighborhood.Experimental results show that the feature lines of simple models may be extracted accurately and completely,and the extraction rate of the number and the length of feature lines of some complex models may reach 90%and 85%,respectively.The algorithm has good adaptability and accuracy,and may extract sharp features and detail features from the point cloud model,meanwhile getting transition features as much as possible.
作者
史红霞
王建民
SHI Hongxia;WANG Jianmin(College of Mining Engineering,Taiyuan University of Technology,Taiyuan,030024)
出处
《中国机械工程》
EI
CAS
CSCD
北大核心
2021年第21期2552-2561,共10页
China Mechanical Engineering
基金
地质灾害防治与地质环境保护国家重点实验室开放基金(SKLGP2020K027)
山西省自然科学基金(201901D111048)。
关键词
点云数据
特征线提取
萤火虫算法
模糊C均值聚类
法向量
point cloud data
feature line extraction
firefly algorithm(FA)
fuzzy C-means(FCM)clustering
normal vector
