Fast outlier removing method for point cloud of microscopic 3D measurement based on social circle

Haihua Cui; Qianjin Wang; Dengfeng Dong; Hao Wei; Yihua Zhang; Haihua Cui; Qianjin Wang; Dengfeng Dong; Hao Wei; Yihua Zhang

doi:10.3934/mbe.2020413

Mathematical Biosciences and Engineering

2020, Volume 17, Issue 6: 8138-8151. doi: 10.3934/mbe.2020413

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Fast outlier removing method for point cloud of microscopic 3D measurement based on social circle

1.
College of Mechanical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
2.
Institute of Microelectronics of the Chinese Academy of Sciences, Beijing 100029, China

Received: 19 August 2020 Accepted: 11 November 2020 Published: 13 November 2020

Measurement outliers are easily caused by illumination, surface texture, human factors and so on during the process of microscopic topography measurement. These numerous cloud point noise will heavily affect instrument measurement accuracy and surface reconstruction quality. We propose a quick and accurate method for removing outliers based on social circle algorithm. First, the gaussian kernel function is used to calculate the voting value to determine the social circle's initial point, and then select the appropriate social circle radius and search window based on the initial point, and finally expand the social circle through an iterative method. Points which are not in the social circle can be considered as outliers and filtered out. The experimental results show the good performance of the algorithm with comparison to the existing filtering methods. The developed method has great potential in microscopic topography reconstruction, fitting and other point cloud processing tasks.
- outlier removal,
- social circle,
- microscopic measurement,
- three-dimensional reconstruction
Citation: Haihua Cui, Qianjin Wang, Dengfeng Dong, Hao Wei, Yihua Zhang. Fast outlier removing method for point cloud of microscopic 3D measurement based on social circle[J]. Mathematical Biosciences and Engineering, 2020, 17(6): 8138-8151. doi: 10.3934/mbe.2020413

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Abstract

Measurement outliers are easily caused by illumination, surface texture, human factors and so on during the process of microscopic topography measurement. These numerous cloud point noise will heavily affect instrument measurement accuracy and surface reconstruction quality. We propose a quick and accurate method for removing outliers based on social circle algorithm. First, the gaussian kernel function is used to calculate the voting value to determine the social circle's initial point, and then select the appropriate social circle radius and search window based on the initial point, and finally expand the social circle through an iterative method. Points which are not in the social circle can be considered as outliers and filtered out. The experimental results show the good performance of the algorithm with comparison to the existing filtering methods. The developed method has great potential in microscopic topography reconstruction, fitting and other point cloud processing tasks.

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