摘要
针对现有点云特征提取算法中,采用全局特征度量阈值及仅使用点的局部信息进行特征提取而造成的特征尖锐程度敏感、对潜在曲面差异较大模型效果差等问题,提出一种基于密度空间聚类的散乱点云特征提取方法.首先,对点的反k近邻进行定义,并提出一种新的特征检测算子;然后,将反k近邻的尺度作为点密度,引入特征的全局约束性信息;最后,对基于密度空间聚类方法中的相关概念进行重定义并建立新的特征识别准则,提取特征点.实验结果表明,该算法简单、有效、鲁棒,同时考虑了特征的局部性信息及全局约束性信息,针对潜在曲面形状差异较大的模型表现出了较强的优越性.
The existing feature extraction methods often depend on the global fixed thresholds and the local information of features, resulting in sensitivity to significance of features and failure in models with different surfaces. To overcome those problems, a novel method based on DBSCAN Clustering is proposed. First, a new reverse k nearest neighbors（kNN） of points are defined as a new feature detection operator. Second, the scales of the reverse k nearest neighbors of points are utilized as the density information of points and then the introduction of the global constraints information is proposed. Finally, based on the redefinition of the concepts of the DBSCAN clustering method and the creation of a new feature recognition criterion, an improved version of the DBSCAN clustering method is used to extract features. Experimental results show that the method is simple, effective and robust, which takes into account the local information and global constraint information and outperforms existing feature detection methods on point clouds with surfaces that have diverse geometries.
作者
张雨禾
耿国华
魏潇然
石晨晨
张顺利
ZHANG Yuhe GENG Guohua WEI Xiaoran SHI Chenchen ZHANG Shunli(School of Information Science and Technology, Northwest Univ., Xi＇an 710127, China)
出处
《西安电子科技大学学报》
EI
CAS
CSCD
北大核心
2017年第2期114-120,共7页
Journal of Xidian University
基金
国家自然科学基金资助项目(61373117,61572400)
国家自然科学基金青年基金资助项目(61305032)
陕西省教育厅科研专项资助项目(2013JK1180)
关键词
点云
特征提取
DBSCAN
聚类
全局约束性
反
k
近邻
point clouds
feature extraction
DBSCAN clustering
global constraints
reverse kNN
