摘要
A systematic scheme is proposed to automatically extract geometric surface features from a point cloud composed of a set of unorganized three-dimensional coordinate points by data segmentation. The key technology is a algorithm that estimates the local surface curvature properties of scattered point data based on local base surface parameterization. Eight surface types from the signs of the Gaussian and mean curvatures provide an initial segmentation, which will be refined by an iterative region growing method. Experimental results show the scheme's performance on two point clouds.
给出了数据分块系统性方案 ,即从仅含有三维坐标的散乱的点云中自动提取几何曲面特性 .首先基于局部基面参数化估算散乱数据点云的局部表面曲率分析是其方案的关键性技术 .再采用由高斯曲率和平均曲率的记号得到的 8种曲面类型 ,就形成初始数据分块 .通过区域增长法可以使粗略数据分块进一步被提取 ,得到更小的噪声影响及更精确的区域划分 .其方案得到了实例验证 ,具有较强的可操作性和实用性 .基于新曲率算法的分块方案使数据分块技术能够直接运用于散乱数据点云 .
基金
AeronauticalScienceFoundationofChina (No .0 0H5 2 0 69)andtheNaturalScienceFoundationofJiangsuProvince(No .BK2 0 0 14 0 8)
