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contributor authorZhang, Yi
contributor authorLyu, Xiuqin
date accessioned2017-11-25T07:20:31Z
date available2017-11-25T07:20:31Z
date copyright2016/16/11
date issued2017
identifier issn1530-9827
identifier otherjcise_017_01_011010.pdf
identifier urihttp://138.201.223.254:8080/yetl1/handle/yetl/4236501
description abstractTo improve the quality of point cloud data, as well as maintain edge and detail information in the course of filtering intensity data, a three-dimensional (3D) diffusion filtering equation based on the general principle of diffusion filtering is established in this paper. Moreover, we derive theoretical formulas for the scale parameter and maximum iteration number and achieve self-adaptive denoising, fine control of the point cloud filtering, and accurate prediction of the diffusion convergence. Through experiments with three types of typical point cloud intensity data, the theoretical formulas for the scale parameter and iteration number are verified. Comparative experiments with point cloud data of different types show that the 3D diffusion filtering method has significant denoising and edge-preserving abilities. Compared with the traditional median filtering algorithm, the signal-to-noise ratio (SNR) of the point cloud after filtering is increased by an average of 10% and above, with a maximum value of 40% and above.
publisherThe American Society of Mechanical Engineers (ASME)
titleA Three-Dimensional Diffusion Filtering Model Establishment and Analysis for Point Cloud Intensity Noise
typeJournal Paper
journal volume17
journal issue1
journal titleJournal of Computing and Information Science in Engineering
identifier doi10.1115/1.4035000
journal fristpage11010
journal lastpage011010-5
treeJournal of Computing and Information Science in Engineering:;2017:;volume( 017 ):;issue: 001
contenttypeFulltext


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