摘要： 针对散乱点云特征提取过程中效率低和噪声敏感性差的问题,提出一种双阈值点云特征信息提取算法。采用主成分分析法和局部二次曲面拟合法对点云模型进行微分几何信息估算,得到k邻域内采样点平均法矢夹角和平均曲率的特征权值,并利用双阈值检测方法对散乱点云的特征信息进行提取。实验结果表明,该算法能够快速准确地对散乱以及含有噪声的点云模型进行特征信息提取,具有较高的鲁棒性。

关键词: 散乱点云, 特征提取, 主成分分析, 二次曲面拟合, 双阈值检测

Abstract: To solve the problem of low efficiency and low noise sensitivity in the process of scattered point cloud feature extraction,this paper proposes a double threshold point cloud feature information extraction algorithm.The Principal Component Analysis(PCA) method and the local quadric surface fitting method are used to estimate the differential geometry information of the point cloud model.The characteristic weights of the average normal vector angle and the mean curvature of k neighborhood sampling points are obtained.The feature information of scattered point cloud is extracted by the double threshold detection method.Experimental results show that the algorithm can extract the feature information of scattered and noisy point cloud model quickly and accurately,and it has high robustness.

Key words: scattered point cloud, feature extraction, Principal Component Analysis(PCA), quadric surface fitting, double threshold detection

中图分类号: 

• TP391.41