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提出一种散乱点云自适应滤波算法,该算法采用改进的R*-树组织散乱点云的拓扑近邻关系,基于该结构快速准确获取局部型面参考数据,自适应调节二维高斯分布的数字特征计算滤波权值,计算局部型面参考数据对原始型面数据的影响因子,以此作为权值计算各点滤波后的坐标,采用加权平均方法实现散乱点云的自适应滤波.实验证明该算法可有效提高点云的滤波效率,在保留原始型面特征的基础上,减小点云的随机误差,提高光顺性。
An adaptive filtering algorithm for scattered point clouds is proposed. This algorithm uses an improved R * - tree to organize the topological neighbor relations of scattered point clouds. Based on this structure, local surface reference data can be obtained rapidly and accurately, and the number of two - dimensional Gaussian distribution The feature is used to calculate the filtering weights, and the influencing factors of the reference data of the local surface to the original surface data are calculated, which are used as the weights to calculate the filtered coordinates of each point, and the weighted average method is used to adaptively filter the scattered point clouds. The algorithm can effectively improve the filtering efficiency of point clouds. On the basis of preserving the characteristics of the original surface, the random error of point cloud can be reduced and the smoothness can be improved.