散乱点云自适应滤波算法

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.

Self-adaptive filtering algorithm for scattered points

A self-adaptive filtering algorithm for scattered points was proposed. The node splitting algorithm and the clustering algorithm of R*-tree were improved and the spacial index structure of triangular mesh model was established based on the improved R*-tree; The local surface reference data was obtained according to data nodes- distributing of the spacial index structure; The filtering weight was computed by self-adjusting figure feature of 2-D Gauss distributing according to its local surface reference data; The weight value of local surface reference data to originality surface data was computed, and the coordinate of scattered points was computed according to this weight value; The self-adaptive filtering for scattered points was realized with the method of weighted mean. It proved that this algorithm can improve the efficiency of filtering and reduce the random error of the scattered points on the basis of the accurate reservation of surface characteristic.