3D Point Cloud Simplification Based on k-Nearest Neighbor and Clustering
Joint Authors
Mahdaoui, Abdelaaziz
Sbai, El Hassan
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-07-15
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Mahdaoui, Abdelaaziz& Sbai, El Hassan. 2020. 3D Point Cloud Simplification Based on k-Nearest Neighbor and Clustering. Advances in Multimedia،Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1126711
Modern Language Association (MLA)
Mahdaoui, Abdelaaziz& Sbai, El Hassan. 3D Point Cloud Simplification Based on k-Nearest Neighbor and Clustering. Advances in Multimedia No. 2020 (2020), pp.1-10.
https://search.emarefa.net/detail/BIM-1126711
American Medical Association (AMA)
Mahdaoui, Abdelaaziz& Sbai, El Hassan. 3D Point Cloud Simplification Based on k-Nearest Neighbor and Clustering. Advances in Multimedia. 2020. Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1126711
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1126711