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.

	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 k-means 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.