We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Point Cloud Denoising and Feature Preservation: An Adaptive Kernel Approach Based on Local Density and Global Statistics.
- Authors
Wang, Lianchao; Chen, Yijin; Song, Wenhui; Xu, Hanghang
- Abstract
Noise removal is a critical stage in the preprocessing of point clouds, exerting a significant impact on subsequent processes such as point cloud classification, segmentation, feature extraction, and 3D reconstruction. The exploration of methods capable of adapting to and effectively handling the noise in point clouds from real-world outdoor scenes remains an open and practically significant issue. Addressing this issue, this study proposes an adaptive kernel approach based on local density and global statistics (AKA-LDGS). This method constructs the overall framework for point cloud denoising using Bayesian estimation theory. It dynamically sets the prior probabilities of real and noise points according to the spatial function relationship, which varies with the distance from the points to the center of the LiDAR. The probability density function (PDF) for real points is constructed using a multivariate Gaussian distribution, while the PDF for noise points is established using a data-driven, non-parametric adaptive kernel density estimation (KDE) approach. Experimental results demonstrate that this method can effectively remove noise from point clouds in real-world outdoor scenes while maintaining the overall structural features of the point cloud.
- Subjects
POINT cloud; PROBABILITY density function; POINT processes; ESTIMATION theory; GAUSSIAN distribution; FEATURE extraction; CLOUD storage
- Publication
Sensors (14248220), 2024, Vol 24, Issue 6, p1718
- ISSN
1424-8220
- Publication type
Article
- DOI
10.3390/s24061718