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24 November 2021 Point cloud smoothing and denoising algorithm based on local domain change factor
Mingjun Wang, Fang Yi
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Proceedings Volume 12065, AOPC 2021: Optical Sensing and Imaging Technology; 120652C (2021) https://doi.org/10.1117/12.2606500
Event: Applied Optics and Photonics China 2021, 2021, Beijing, China
Abstract
The original point cloud data acquired by 3D scanning equipment has a large number of noise points, which will seriously affect the subsequent work such as point cloud alignment and surface reconstruction. To address this problem, we propose a point cloud smoothing and denoising algorithm based on the local neighborhood change factor. The algorithm classifies the noise in the point cloud into singular and non-singular points according to the magnitude of the surface change factor of each point. For singular points, the improved median filtering algorithm is used to correct the singular points; for non-singular points, the density difference function is introduced in the bilateral filtering algorithm and smoothed by using the improved bilateral filtering algorithm. The smoothing and denoising experiments are conducted for different data models. The experimental results show that the method in this paper can effectively remove the point cloud noise and smooth the point cloud surface while preserving the detailed features of the point cloud. Compared with the bilateral filtering algorithm, the algorithm in this paper is both maximum error and the average error are reduced.
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Mingjun Wang and Fang Yi "Point cloud smoothing and denoising algorithm based on local domain change factor", Proc. SPIE 12065, AOPC 2021: Optical Sensing and Imaging Technology, 120652C (24 November 2021); https://doi.org/10.1117/12.2606500
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KEYWORDS
Denoising
Reconstruction algorithms
3D modeling
3D image processing
Image filtering
Principal component analysis
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