Genetic fuzzy clustering algorithm for point cloud data segmentation
应用遗传模糊聚类实现点云数据区域分割

In order to realize point cloud data segmentation accurately, this paper applied genetic fuzzy clustering algorithm to the point cloud data segmentation in reverse engineering. First, it estimated the normal vector, Gaussian curvature and mean curvature, together with the coordinates of the eight-dimensional feature vector component, using weight distance replaced the Euclidean distance. Through the genetic algorithm, it obtained the approximate solution of the global optimal solution. Finally it used the approximate solution as the initial solutions of fuzzy clustering iteration achieved the point cloud data region segmentation, therefore, avoided the locality and sensitiveness of the initial condition of fuzzy clustering algorithm, at the same time, it reduced the number of iterations. Taking car sheet metal for an example proves the validation of genetic fuzzy clustering algorithm applied to the point cloud data segmentation. And point cloud data can be segmented fast and accurately by this algorithm.