基于最优邻域局部熵的点云精简算法

Point cloud simplification algorithm based on local entropy of optimal neighborhood

针对传统的点云精简算法中不能良好保留细节特征的问题,提出一种基于最优邻域局部熵的点云精简算法。首先利用点云局部邻域协方差矩阵的3个特征值构造的维度特征,构建局部邻域信息熵函数,其次依据局部熵值最小原则确定最优邻域,然后根据最优邻域下计算的特征值间的关系,以及局部信息熵来剔除平坦区域数据点。通过模拟数据和实例扫描数据精简实验,结果表明该方法能较好的保留细节特征。

To solve the problem that the traditional point cloud simplification algorithm can’t keep the detail features well,a point cloud simplification algorithm based on the optimal neighborhood local entropy is proposed.Firstly,the dimensional features of the three eigenvalues of the local neighborhood covariance matrix of the point cloud are used to construct the information entropy function of the local neighborhood.Secondly,the optimal neighborhood is determined according to the principle of the minimum of the local entropy.Finally,the data points in the flat region are eliminated according to the local information entropy and the relations between the eigenvalues calculated under the optimal neighborhood.Through the simplifying experiment of the simulated data and the example scan data,the result shows that the method can retain the detail features better.