Research Article

3D Point Cloud Simplification Based on k-Nearest Neighbor and Clustering

Algorithm 1

Simplification of 3D point cloud based on the clustering algorithm and Shannon’s entropy.
(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
(vii)Decomposing the initial set of points into small clusters denoting , using the k-means algorithm
  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
   End for
 End for
(ix)End for,