3D Point Cloud Simplification Based on k-Nearest Neighbor and Clustering. (15th July 2020)

• Record Type: Journal Article
• Authors: Mahdaoui, Abdelaaziz; Sbai, El Hassan
• Other Names: Seeling Patrick Academic Editor.
• Is Part Of: Advances in multimedia. Volume 2020(2020)
• Publication Date: 2020-07-15
• Subjects: Multimedia systems -- Periodicals; Computer networks -- Periodicals; Multimédia; Réseaux d'ordinateurs; Computer networks; Multimedia systems; Periodicals; 006.7
• DOI: 10.1155/2020/8825205
• Languages: English
• ISSNs: 1687-5680

Abstract

Abstract : While the reconstruction of 3D objects is increasingly used today, the simplification of 3D point cloud, however, becomes a substantial phase in this process of reconstruction. This is due to the huge amounts of dense 3D point cloud produced by 3D scanning devices. In this paper, a new approach is proposed to simplify 3D point cloud based on k -nearest neighbor ( k -NN) and clustering algorithm. Initially, 3D point cloud is divided into clusters using k -means algorithm. Then, an entropy estimation is performed for each cluster to remove the ones that have minimal entropy. In this paper, MATLAB is used to carry out the simulation, and the performance of our method is testified by test dataset. Numerous experiments demonstrate the effectiveness of the proposed simplification method of 3D point cloud.