Fast simplification with sharp feature preserving for 3D point clouds

This paper presents a fast point cloud simplification method that allows to preserve sharp edge points. The method is based on the combination of both clustering and coarse-to-fine simplification approaches. It consists to firstly create a coarse cloud using a clustering algorithm. Then each point of the resulting coarse cloud is assigned a weight that quantifies its importance, and allows to classify it into a sharp point or a simple point. Finally, both kinds of points are used to refine the coarse cloud and thus create a new simplified cloud characterized by high density of points in sharp regions and low density in flat regions. Experiments show that our algorithm is much faster than the last proposed simplification algorithm [1] which deals with sharp edge points preserving, and still produces similar results.