Nonparametric point cloud filter

• Published in: IET image processing Vol. 18; no. 2; pp. 388 - 402
• Main Authors: Sun, Yefa, Wang, Jinli
• Format: Journal Article
• Language: English
• Published: Wiley 01.02.2024
• Subjects: adaptive filters; computer vision; filtering theory
• ISSN: 1751-9659; 1751-9667
• DOI: 10.1049/ipr2.12955

This paper proposes a nonparametric point cloud filter to address the issue that existing point cloud filtering methods cannot retain important point cloud features after filtering and often require complex parameter adjustments. Firstly, a nonparametric clustering method is proposed to cluster various features of the point cloud and filter out isolated outliers. Then, the manifold distance truncation method is adopted to remove the outlier cluster generated by the point cloud clustering to complete point cloud filtering. Additionally, the proposed nonparametric clustering algorithm is compared with four of the latest clustering algorithms, including K‐means clustering and OPTICS clustering to verify the rationality of the clustering features. Finally, the filtering results of the nonparametric point cloud filter are compared with those of statistical filtering and two other recently proposed point cloud filters to demonstrate its improved filtering effect and algorithm stability. The experimental results indicate that the proposed nonparametric point cloud filter can achieve better‐filtering results and retain more point cloud features without adjusting parameters. (1) By restoring weight from data, the difficulty in determining the initial parameters of the point cloud filter can be solved. (2) Using manifold distance instead of Euclidean distance can better retain the spatial characteristics of the point cloud. (3) The no‐gap test method can better cluster the characteristics of point clouds. This is significant because to solve the problems of incomplete feature retention and excessive parameters of point cloud filtering.