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Journal of Graphics ›› 2023, Vol. 44 ›› Issue (1): 131-138.DOI: 10.11996/JG.j.2095-302X.2023010131

• Computer Graphics and Virtual Reality • Previous Articles     Next Articles

Adaptive bilateral filtering point cloud smoothing and IMLS evaluation method considering normal outliers

CHEN Ya-chao1,2(), FAN Yan-guo1(), YU Ding-feng3, FAN Bo-wen4   

  1. 1. College of Oceanography and Space Informatics, China University of Petroleum, Qingdao Shandong 266580, China
    2. Research and Development Center, The Second Institute of Civil Aviation Administration of China, Chengdu Sichuan 610041, China
    3. Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao Shandong 266061, China
    4. College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin Heilongjiang 150001, China
  • Received:2022-03-20 Revised:2022-07-31 Online:2023-10-31 Published:2023-02-16
  • Contact: FAN Yan-guo
  • About author:CHEN Ya-chao (1996-), master student. His main research interest covers graphic image processing. E-mail:yachaochen1@163.com
  • Supported by:
    Key Research and Development Project of Shandong Province(2019GHY112017)

Abstract:

In order to address the problems that the unreasonable parameters of bilateral filtering lead to poor smoothing effect of point cloud, volume shrinkage, and the limitation of existing quality evaluation methods, a bilateral filtering algorithm with adaptive parameters and a quality evaluation method based on implicit moving least squares (IMLS) were proposed. Firstly, the KD-tree data structure was constructed for point cloud topology, then the neighborhood of each point was searched to calculate the normal of each point using the SVD decomposition method, and the normal outlier factor was introduced into bilateral filtering to remove outliers in the neighborhood. Additionally, the space and normal characteristic parameters were calculated according to the Gaussian kernel function extended by the neighborhood norm. Finally, the constructed bilateral filtering model was applied to the smoothing of the point cloud, and the implicit moving least squares method was introduced to evaluate the quality of smoothing. The experimental results of the point cloud with noise show that the adaptive bilateral filtering point cloud smoothing algorithm considering normal outliers could attain a good effect and result in smaller volume shrinkage compared with other algorithms, and that the IMLS evaluation method could be objective and effective.

Key words: point cloud smoothing, bilateral filtering, normal outlier, evaluation of denoising quality

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