Research Article
3D Point Cloud Simplification Based on kNearest Neighbor and Clustering
Algorithm 1
Simplification of 3D point cloud based on the clustering algorithm and Shannon’s entropy.
 Input  (i)  : the data sample (point cloud)  (ii)  : the array in which cluster indexes are stored  (iii)  : the number of clusters  (iv)  : the number of clusters to delete ()  (v)  : minimal entropy  (vi)  Begin  (vii)  Decomposing the initial set of points into small clusters denoting , using the kmeans algorithm  (viii)  For   For   Calculate global entropy of a cluster by using all data samples in according to equation (7), Note this entropy   If then     pos ⟵ j   End if   For     End for   End for  (ix)  End for,     End. 
