A feature preserving algorithm for point cloud simplification based on hierarchical clustering

Abstract

The efficiency and accuracy loss are the key issues for the point cloud simplification. In this paper, a feature preserving algorithm is proposed for point cloud simplification based on hierarchical clustering with the surface feature description. The surface variation is presented as the main criterion for the efficient hierarchical clustering method to simplify the mass and dense point cloud fast, meanwhile we retain the feature points to ensure a small accuracy loss. The experiment results show that the proposed method is efficient and has a good effect to maintain the features as the same degree of simplification.