散乱点云切片数据快速获取与优化  

An acquisition and optimization algorithm for slicing data of scattered points

作　　者：孙永伟[1] 孙殿柱[1] 朱昌志[1] 朱宗伟[1] 

机构地区：[1]山东理工大学机械工程学院,山东淄博255091

出　　处：《哈尔滨工程大学学报》2010年第11期1514-1518,共5页Journal of Harbin Engineering University

基　　金：国家自然科学基金资助项目(51075247)

摘　　要：为快速准确地获取散乱点云切片数据,以较少数据准确表达模型型面特征,提出一种散乱点云切片数据获取及优化算法.该算法基于散乱点云动态索引实现切片数据快速获取,对所获切片数据建立极坐标系,分别基于极角阈值与极径阈值实现切片数据区域划分与轮廓分离.通过依次连接各区域相同轮廓数据形心实现切片数据的精简与排序,生成精确有序的单轮廓或多轮廓切片数据.实例证明,该算法可应用于各种复杂散乱点云的切片数据快速、精确获取与优化处理,对有效提高参数曲线、曲面重构效率与精度具有重要意义.In order to obtain slicing data for scattered points rapidly and accurately, and to express the information of model with less data, an acquisition and optimization algorithm for slicing data was proposed. The slicing data was obtained based on the dynamic special index structure of scattered points, and the slicing data was divided into many sector zones based on the polar angle threshold. The contour was separated by the distance threshold, and the slicing data was reduced and sorted by connecting the zones＂ cores. Precise and orderly single-contour and multicontour slicing data was generated. The results show that the algorithm can obtain the slicing data of various complex scattered points accurately and effectively and has important significance for improving the efficiency and precision of parameter curves and surface reconstruction.

关 键 词：散乱点云 切片数据获取 动态索引 极角区域划分 轮廓分离 精简与排序 

分 类 号：TP391.72[自动化与计算机技术—计算机应用技术]