Algorithm for RGBD Point Cloud Denoising and Simplification Based on K-means Clustering

Authors

Benyue Su, 1. School of Computer and Information, Anqing Normal University, Anqing 246133, China;;2. The University Key Laboratory of Intelligent Perception and Computing of Anhui Province, Anqing 246133, China;;
Jinyu Ma, 1. School of Computer and Information, Anqing Normal University, Anqing 246133, China;;2. The University Key Laboratory of Intelligent Perception and Computing of Anhui Province, Anqing 246133, China;;
Yusheng Peng, 2. The University Key Laboratory of Intelligent Perception and Computing of Anhui Province, Anqing 246133, China;;3. School of Mathematics and Computational Science, Anqing Normal University, Anqing 246133, China;
Sheng Min, 2. The University Key Laboratory of Intelligent Perception and Computing of Anhui Province, Anqing 246133, China;;3. School of Mathematics and Computational Science, Anqing Normal University, Anqing 246133, China;

Abstract

Abstract: Aiming at the problem that the point cloud data scanned by Kinect or other depth camera have a huge number and more noise, a feature preserving method for point cloud denoising and simplification was proposed. This algorithm classified the point cloud rapidly by K-D tree; find The corresponding surface curvature values were obtained using curvature estimation algorithm; The K-means clustering algorithm for point cloud clustering was used. For each point in the cluster, the Euclidean distance was depended on between the point and center of the cluster and the change of the near points curvature to determine whether the noise points. The point cloud data was simplified by the feature preserving method. The experimental results show that the denoising and feature preserving point cloud simplification method is quickly and efficiently, for the removal of a large number of external noise has a positive effect, and the streamline point cloud data have the retention of original point cloud features.

Recommended Citation

Su, Benyue; Ma, Jinyu; Peng, Yusheng; and Min, Sheng (2020) "Algorithm for RGBD Point Cloud Denoising and Simplification Based on K-means Clustering," Journal of System Simulation: Vol. 28: Iss. 10, Article 6.
Available at: https://dc-china-simulation.researchcommons.org/journal/vol28/iss10/6

First Page

2329

Revised Date

2016-07-14

DOI Link

https://doi.org/

Last Page

2335

CLC

TP391.9

Recommended Citation

Su Benyue, Ma Jinyu, Peng Yusheng, Sheng Min. Algorithm for RGBD Point Cloud Denoising and Simplification Based on K-means Clustering[J]. Journal of System Simulation, 2016, 28(10): 2329-2335.