基于法向夹角与Hausdorff距离的点云精简方法研究  

Point Cloud Simplification Based on Angle of Inter-normal and Hausdorff Distance

作　　者：武敬民[1] 韩燮[1] 李定主 韩焱[1] 张奥扬[3] 

机构地区：[1]中北大学计算机与控制工程学院,山西太原030051 [2]中国兵工集团207所,山西太原030051 [3]浙江大学电气工程学院,浙江杭州310027

出　　处：《微电子学与计算机》2014年第4期52-55,共4页Microelectronics & Computer

基　　金：国家自然科学基金项目(61379080;61071193);国家科技支撑计划(2013BAH45F02)

摘　　要：提出一种新的点云数据精简方法,首先将包围盒内点云的平均法矢与各点法矢的夹角计算出来,依据此夹角来判断该包围盒是否需要利用八叉树分割法继续细分.然后利用二次参数曲面逼近法对点云拟合并计算各点的主曲率,并依据各点主曲率的Hausdorff距离来提取并保留特征点.实验表明简化效果十分明显,能够删除大量冗余数据且保留点云的几何特征,为后续的三维重建提高了效率.Proposing a new point cloud data simplification method. At first, calculate the normal vectors of all the points and the average normal vector of all points in the bounding box, whether or not to subdivide the cube using octree grid division algorithm depends on the angle between the two vectors. Secondly, the point cloud is fitted using quadratic parametric curve method and principal curvatures of all points are estimated, and Hausdorff distance of principal curvatures decides to get and whether to keep the feature points. Experiments show that simplication results are obvious, can delete the redundant points in the meantime reserve the original cloud geometry features, and increases the three dimensional reconstruction efficient.

关 键 词：数据简化 八叉树网格 主曲率 Hausdroff距离 

分 类 号：TP317.4[自动化与计算机技术—计算机软件与理论]