基于近邻点重加权的点云特征线提取算法

A Point Cloud Feature Line Extraction Algorithm Based on Neighbor Point Reweighting

针对3维点云模型特征线提取存在断裂、不完整问题,提出一种基于近邻点重加权的点云特征线提取算法。算法分为提取特征点和特征点连接成线2个环节,在特征点提取环节,引入近邻重加权局部质心算子获取特征点集,通过欧式最小生成树构建特征线。实验结果表明:采用近邻重加权局部质心算法进行特征点提取,跟传统基于曲率的算法相比其结果更加准确和稳健,能有效提取点云模型的几何特征。

Aiming at the problem of broken and incomplete feature line extraction of 3D point cloud model,a feature line extraction algorithm based on reweighting of neighboring points is proposed.The algorithm is divided into two steps:extracting feature points and connecting feature points into lines.In the step of extracting feature points,the nearest neighbor reweighted local centroid operator is introduced to obtain the feature point set,and the feature line is constructed by the Euclidean minimum spanning tree.The experimental results show that compared with the traditional curvature-based algorithm,the nearest neighbor reweighted local centroid algorithm is more accurate and robust,and can effectively extract the geometric features of point cloud model.