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Research Papers

A Three-Dimensional Diffusion Filtering Model Establishment and Analysis for Point Cloud Intensity Noise

[+] Author and Article Information
Yi Zhang

School of Geodesy and Geomatics,
Wuhan University,
Wuhan, Hubei 430072, China
e-mail: yzhang@sgg.whu.edu.cn

Xiuqin Lyu

School of Resource and
Environmental Science,
Wuhan University,
Wuhan, Hubei 430072, China
e-mail: winterlxq@sina.com

1Corresponding author.

Contributed by the Computers and Information Division of ASME for publication in the JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING. Manuscript received February 29, 2016; final manuscript received October 14, 2016; published online November 16, 2016. Editor: Bahram Ravani.

J. Comput. Inf. Sci. Eng 17(1), 011010 (Nov 16, 2016) (5 pages) Paper No: JCISE-16-1871; doi: 10.1115/1.4035000 History: Received February 29, 2016; Revised October 14, 2016

To 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.

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Figures

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Fig. 1

Three types of typical point cloud intensity data: (a) intensity gradient, (b) salt-and-pepper noise, and (c) intensity edge

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Fig. 2

Theoretical distribution of the scale parameter k=d2/λε in the neighborhood: (a) intensity gradient, (b) salt-and-pepper noise, and (c) intensity edge

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Fig. 3

Theoretical distribution of the iteration number t<1.5+kλε/2d2 in the neighborhood: (a) intensity gradient, (b) salt-and-pepper noise, and (c) intensity edge

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Fig. 4

Change curves of the three types of typical point cloud data: (a) intensity gradient, (b) salt-and-pepper noise, and (c) intensity edge

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Fig. 5

Comparison of various point clouds after 3D diffusion and median filtering (k = 1.0)

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Fig. 6

SNR curves of various point clouds after 3D diffusion filtering

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