离散点云原始形状及边界曲线提取算法  

Extracting Methods of Primitive Shapes and Boundary Curves from Scattered Point Set

作　　者：刘光帅[1] 李柏林[1] 何朝明[1] 

机构地区：[1]西南交通大学机械学院,成都610031

出　　处：《计算机科学》2011年第5期279-282,共4页Computer Science

基　　金：国家自然科学基金(50675180);四川省科技计划项目(2008GZ0149;2009GZ0007);中央高校基本科研业务费专项资金项目(SWJTU09BR104)资助

摘　　要：大规模离散点云包含多种类型的扫描缺陷:噪声、异常数据、孔洞及不规则的各向异性采样,大部分现有的算法不能够很好地处理这些缺陷,这对点云拓扑关系的恢复及特征提取带来了困难。针对此问题,提出了一种健壮有效的点云重构算法,首先,计算每个数据点的局部属性;然后利用局部属性探测点云中包含的原始形状;最后利用统计优化方法对原始形状中包含的边界曲线进行提取和优化,通过优化的边界曲线可以获得分段光滑的网格曲面。实例证明,该算法实用性好,对合成点云及真实场景点云的重构效果理想。A huge scattered point data includes all kinds of scanning artifacts including noise,outliers,holes and irregular/anisotropic sampling.Most common surface reconstruction methods fail due to these shortcomings.This poses challenges to recovering datasets topology and retrieving features.In order to solve this problem,a robust and efficient reconstruction method was proposed.First,computed local properties for each data point,then used this information to detect simple primitive shapes in the data,at last,described a novel method to extract and optimize boundary curves on the primitive shapes and Employed the reconstructed boundary curves to extract a piecewise smooth surface mesh.The experimental results show the effectiveness of our method with reconstructions of synthetic datasets and real-scenes datasets.

关 键 词：散乱点云 原始形状 边界曲线 特征点 多尺度分析 统计优化 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]