摘要
平均曲率是分析三维表面的重要几何特征之一。根据平均曲率进行海量散乱点云数据的精简,首先通过空间包围盒法建立K邻域,然后对K邻域内的点拟合二次曲面计算平均曲率,最后以邻近区域内点的平均曲率中误差为阈值,结合点的精简概率判定点是否保留。通过与传统方法对比,证实了文中方法在保留特征点和压缩上具有较好的优势。
The mean curvature is one of the important geometric features of 3D surface analysis .This paper reduces scattered point cloud data according to mean curvature .Firstly ,K‐neighborhood of points is established based on bounding box method .Secondly ,mean curvature is calculated based on quadric surface w hich is fitted by the points of K‐neighborhood .Finally ,mean square error of mean curvature of points in K‐neighborhood combined with reduction probability is used to decide whether to reserve these points . Compared with traditional method ,this method has some advantage in feature keeping and data reduction .
出处
《测绘工程》
CSCD
2015年第11期13-16,21,共5页
Engineering of Surveying and Mapping
基金
国家海洋局第一海洋研究所基本科研业务基金(GY0214G20)
关键词
点云
包围盒法
平均曲率
局部曲率
精简概率
point cloud
bounding box method
mean curvature
local curvature
reduction probability
