Work

A 3D Point Cloud Filtering Algorithm based on Surface Variation Factor Classification

Year: 2019

Type: conference-paper

Abstract: In order to effectively smooth the noise in 3D point cloud without losing the detailed features of the model, a new filtering algorithm based on surface variation factor segmentation is proposed. The method adopts different filtering algorithms on different feature regions of the model. Firstly, the normal vectors of a point cloud model are estimated by using weighted principal component analysis (PCA) method, and the surface variation factor of each point is estimated. Secondly, the point cloud model is divided into flat regions and mutant regions by comparing the surface variation factor of the sampling point with the average surface variant factor of the sampling point k-neighborhood. Finally, the improved median filtering algorithm is applied to flat regions, and improved bilateral filtering algorithm is applied to mutant regions. Experimental results show that it has a preferable smoothing effect and reserves the detail features of point cloud.

Source: Procedia Computer Science

DOI: 10.1016/j.procs.2019.06.010

Authors: Chao Chuan Jia, Chuan Jiang Wang, Ting Yang, Bing Hui Fan, Fu Gui He

Institutions: West Anhui University, Shandong University of Science and Technology

Language: English

Topic: 3D Shape Modeling and Analysis

Subfield: Computational Mechanics

Field: Engineering

Domain: Physical Sciences

Open Access status: diamond

Funders: Government of Jiangsu Province, Natural Science Foundation of Anhui Province

Award: G6499345852