%0 Journal Article
%T Adaptive up-sampling algorithm of point cloud model<br>点云模型适应性上采样算法
%A YANG Bin
%A FAN Yuan-yuan
%A <br>杨　斌
%A 范媛媛b
%J 计算机应用研究
%D 2012
%I 
%X For insufficient sampling density of point cloud model, this paper proposed a new adaptive up-sampling algorithm. Firstly, it used uniform grids method to represent the spatial topology relationship of point cloud in order to improve the efficiency of finding the K-nearest neighbors for each data point, and estimated normal vectors of data points by constructing covariance matrix, and computed a consistent orientation of the normal vectors using normal propagation algorithm. Then, it detected these regions with insufficient sampling density dynamically, and adaptive resampled points uniformly in the tangent plane of bounding rectangle originated at this point of insufficient sampling density, and the re-samples were projected onto the underlying surface of point cloud model to achieve the final up-sampling result using the almost orthogonal projection. The up-sampling models could preferably supplement these little detail information of point cloud, and satisfy the needs of rendering point cloud model and subsequent geometric processing.
%K point cloud model
%K uniform grids
%K covariance matrix
%K projection
%K up-sampling<br>点云模型
%K 均匀栅格
%K 协方差矩阵
%K 投影
%K 上采样
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=924F7D591EA7595EEE7AC501DA691BB4&yid=99E9153A83D4CB11&vid=771469D9D58C34FF&iid=B31275AF3241DB2D&sid=5B41211CF7398ABF&eid=DE73B43A18DEAEE3&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=0&reference_num=12