Comparison of K-means and fuzzy C-means algorithms on simplification of 3D point cloud based on entropy estimation

Abstract

In this article we will present a method simplifying 3D point clouds. This method is based on the Shannon entropy. This technique of simplification is a hybrid technique where we use the notion of clustering and iterative computation. In this paper, our main objective is to apply our method on different clouds of 3D points. In the clustering phase we will use two different algorithms; K-means and Fuzzy C-means. Then we will make a comparison between the results obtained.