文献类型：期刊文献

中文题名：基于变半径球的数据压缩

英文题名：Data Reduction Based on Variable Radius Sphere

作者：刘晶[1];WONG Edward K[2]

机构：[1]华东理工大学机械与动力工程学院,上海200237;[2]纽约大学理工学院计算机科学与工程系,美国纽约11201

年份：2016

卷号：42

期号：3

起止页码：433

中文期刊名：华东理工大学学报（自然科学版）

外文期刊名：Journal of East China University of Science and Technology

收录：CSTPCD;;Scopus;北大核心:【北大核心2014】；CSCD:【CSCD2015_2016】；

基金：国家留学基金委资助

语种：中文

中文关键词：数据压缩;离散点云;逆向工程;曲度

外文关键词：data reduction; discrete point cloud; reverse engineering; curvedness

摘要：逆向工程中的点云数据压缩正受到越来越多的关注。它是模型重构和配准的基础。点云数据压缩后,可以改善重构以及配准的速度。在保持原始点云形状的基础上,提出了一种数据压缩算法。首先计算每一点处的法向量并且通过相邻点法向量间的关系去除坏点,然后计算每一点的曲度,最后通过移动球检测控制点并且简化点云。实验表明该算法在保持点云几何形状的同时,能够有效地降低点云数量。
There is an increasing interest for data reduction of point cloud in reverse engineering. It is the base of model reconstruction and registration. After point cloud data is reduced, the speed of reconstruction and registration can be improved. A data reduction algorithm is proposed to preserve the shape of original point cloud. Firstly, normal vector of every point is calculated and outliers are discarded by normal vectors＇ relation of neighbor points. Then curvedness of each point is calculated. Finally, point cloud is simplified and dominant points are detected based on moving variable spheres. Experiments demonstrate the algorithm can reduce points of point cloud effectively when the geometrical shape of point cloud is preserved.