计算机应用与软件Issue(2)：42-45,4.DOI:10.3969/j.issn.1000-386x.2016.02.010
基于自适应椭圆距离的点云分区精简算法
REDUCTION ALGORITHM OF POINT CLOUD SEGMENTATION BASED ON ADAPTIVE ELLIPTICAL DISTANCE
摘要
Abstract
Applying traditional point cloud reduction algorithm to reducing scattered point cloud will lead to missing or fuzzy of some detail features of the point cloud model and affecting the smoothness of non planar region.Aiming at these problems,we put forward the adaptive elliptical distance-based point cloud segmentation reduction algorithm.First,by fitting the tangent plane and local surface on neighbourhood set,it calculates the normal vector and curvature of each point;secondly,it uses the derived geometric feature information to extract point cloud boundary characteristics and to complete the partition of planar regions and non planar regions of point cloud;finally,it uses the improved reduction algorithm to simplify different regions.Experimental results show that the algorithm can not only rapidly accomplish data simplification in accord with the required reduction rate,but can also protect the detail characteristics of point cloud model and ensure the smoothness of non planar portion of model.Through software analysis,it is found that the standard deviation between the reduced model and the original model is 0.015 mm.关键词
点云精简/四元数/最小距离法/点云分区/边界提取Key words
Point cloud reduction/Quaternion/Minimum distance method/Segmentation of point cloud/Boundary extraction分类
信息技术与安全科学引用本文复制引用
吴禄慎,俞涛,陈华伟..基于自适应椭圆距离的点云分区精简算法[J].计算机应用与软件,2016,(2):42-45,4.基金项目
国家自然科学基金项目（51065021，51365037）。 （）
