%0 Journal Article
%T Filtering methods of scattered point set based on multi-scale kernel function<br>基于多尺度核函数的散乱点云数据过滤方法*
%A LIU Guang-shuai
%A LI Bai-lin
%A HE Chao-ming
%A <br>刘光帅
%A 李柏林
%A 何朝明
%J 计算机应用研究
%D 2010
%I 
%X 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.
%K scattered point data
%K multi-scale kernel function
%K mean-shift algorithm
%K likelihood estimation
%K noise
%K outliers
%K filtering<br>散乱点云数据
%K 多尺度核函数
%K 均值漂移跟踪算法
%K 似然估计
%K 噪声
%K 异常数据
%K 过滤
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=E4CE090F8BA248645C5E36AA1142EB90&yid=140ECF96957D60B2&vid=DB817633AA4F79B9&iid=708DD6B15D2464E8&sid=95A8BFD52F538E8C&eid=1896EE7108D08E4B&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=0&reference_num=13