Hybrid simplification algorithm for unorganized point cloud based on two-level fuzzy decision making

Abstract

Traditional point cloud simplification algorithms have specific application scenarios. If these well-known methods can be accurately applied to different feature regions of a point cloud, high-quality point cloud feature preservation can be realized. In this paper, a hybrid point cloud simplification method, based on two-level fuzzy decision making, is proposed. Specifically, the number of peak points and bin intervals of each dimension density histogram of the point cloud are counted, and the cluster number and initial centers of fuzzy c-means (FCM) clustering method are determined, hence the autonomous FCM clustering method of point clouds is realized. Based on the expert linguistic inference rules of type-1 fuzzy system, its output is used to accurately identify clusters as flat, transitional and drastic type. According to the attributes of the clusters, predefined thresholds or algorithms are applied in each one, achieving high quality point cloud simplification. The experimental results show that the algorithm can effectively determine the number of clusters, improve the speed of FCM clustering algorithm and achieve feature preserving point cloud simplification, after removing 50–90 % of the points.