散乱点集拓扑邻域均值逆向漂移查询算法  

Mean Reverse Shift Query Algorithm for Topological Neighbors of Scattered Point-cloud

作　　者：孙殿柱[1] 白银来 李延瑞[1] 李聪[1] 

机构地区：[1]山东理工大学机械工程学院,淄博255049

出　　处：《机械工程学报》2015年第1期182-187,共6页Journal of Mechanical Engineering

基　　金：国家自然科学基金资助项目(51075247)

摘　　要：为获取散乱点集的拓扑邻域数据,提出一种基于均值逆向漂移的自适应迭代搜索查询算法,基于R*树动态空间索引,采用动态扩展空心球k邻域查询算法快速获取目标样点的拓扑邻域参考数据,根据邻域查询与点集密度分布的关系,采用核密度估计描述点集的分布规律,利用均值漂移算法计算得到能够反映散乱点集局部分布特征的均值漂移矢量和均值点,将邻域搜索区域沿逆向均值漂移矢量移动进行邻域查询并实时更新样点的拓扑邻域参考数据,进而完成拓扑邻域查询的均值逆向漂移迭代计算,最终实现样点拓扑邻域数据的自适应搜索查询。试验表明,该算法可快速获取复杂型面均匀或非均匀采样点集的邻域数据,查询结果包含Voronoi邻域及其他有效邻域参考数据,能更好地反映散乱点集局部型面特征。A self-adaptive iterative query algorithm based on mean reverse shift is proposed, which can be used to query topological neighbors for 3d scattered point-cloud. R＊-tree is applied to organize the dynamic spatial index structure of scattered cloud-point. The topological neighboring reference points of object point are obtained by the k-nearest neighbor query algorithm based on R＊-tree According to the relationship between neighbors query and density distribution of the point set, kernel density estimation is used to describe the distribution law of point set. The mean shift vector and mean point which can reflect the local distribution features of sampling points are computed by mean shift algorithm. The topological neighbors self-adaptive iterative query algorithm is realized through the iterative search process by shifting neighbors search area along the reverse mean shift vector and querying new neighbors The experimental results show that this method can obtain topological neighbors of arbitrary complicated scattered point-cloud efficiently, and the neighbors include Voronoi neighbors as well as some more available neighborhood reference data, which can reflect the local surface features around the object point better.

关 键 词：散乱点集 拓扑邻域查询 R＊树 核密度估计 均值逆向漂移 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]