基于多尺度核函数的散乱点云数据过滤方法  

Filtering methods of scattered point set based on multi-scale kernel function

作　　者：刘光帅[1] 李柏林[1] 何朝明[1] 

机构地区：[1]西南交通大学机械学院,成都610031

出　　处：《计算机应用研究》2010年第11期4348-4349,4352,共3页Application Research of Computers

基　　金：国家自然科学基金资助项目(50675180);四川省科技计划资助项目(2008GZ0149;2009GZ0007);中央高校基本科研业务费专项资金资助项目(SWJTU09BR104)

摘　　要：针对光滑曲面采样散乱点云含有噪声及异常数据的问题,提出了一种基于多尺度核函数的过滤处理方法。采用核密度估计技术及均值漂移跟踪算法对原始点云数据进行聚类,结合局部似然函数来测度一个三维点位于采样曲面上的概率,利用过滤后的极大似然点集精确地逼近采样曲面,最后结合经典网格化算法能够获得较好的曲面重构效果。处理实例证明,该方法实用性好,不仅能够很好地抑制不同幅值的噪声,同时也能够探测到异常数据并进行自动清除。This paper proposed a method based on multi-scale kernel function for robust filtering of a noisy set of points sampled from a smooth surface.The method used a kernel density estimation technique and a mean-shift algorithm for point clustering.With every point of the input data,associated a local likelihood measure capturing the probability that a 3D point was located on the sampled surface.The remaining set of maximum likelihood points deliverd an accurate point-based approximation of the surface.Some established meshing techniques work well in conjunction with the filtering method for surface reconstruction.Experiment results show that the filtering procedure suppresses noise of different amplitudes and allows for an easy detection of outliers which are then automatically removed.

关 键 词：散乱点云数据 多尺度核函数 均值漂移跟踪算法 似然估计 噪声 异常数据 过滤 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]