摘要
点云数据的特征提取是点云数据处理环节中的一项重要内容,对几何分析、数据分割、点云配准、模型重建等研究起关键作用。研究了基于法向量和曲率的点云特征提取技术,阐明了特征提取过程中邻域选取与单一参数计算存在的问题,提出了邻域自适应的双阈值点云特征提取方法。通过实验对比了该算法与基于曲率的特征提取算法的提取效果,验证了本算法的稳定性、准确性。该算法对于几何特征复杂的点云具有较好的提取效果,对提高点云特征点提取的精度及效率具有重要的意义。
The feature extraction of point cloud data is an important part of point cloud data processing,which plays a key role in geometric analysis,data segmentation,point cloud registration,and model reconstruction.The point cloud feature extraction technology based on normal vector and curvature has been studied.The problems of neighborhood selection and single parameter calculation in the feature extraction process are clarified.A two-threshold point cloud feature extraction method with adaptive neighborhood is proposed.The experiment compares the extraction effect of the algorithm with the curvature-based feature extraction algorithm,and verifies the stability and accuracy of the algorithm.This algorithm has a good extraction effect for point clouds with complex geometric features,and has important significance for improving the accuracy and efficiency of point cloud feature point extraction.
作者
周建钊
颜雨吉
陈晨
杜文超
Zhou Jianzhao;Yan Yuji;Chen Chen;Du Wenchao(College of Field Engineering,PLA Army Engineering University,Nanjing 210007,China)
出处
《信息技术与网络安全》
2020年第2期27-33,共7页
Information Technology and Network Security
关键词
点云特征提取
法向量
曲率
双阈值
邻域自适应
point cloud feature extraction
normal vector
curvature
double threshold
neighborhood adaptive
