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Point Cloud Denoising and Feature Preservation: An Adaptive Kernel Approach Based on Local Density and Global
Lianchao Wang1, Yijin Chen1, Wenhui Song1
1College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China.
Sensors (Basel, Switzerland)
|March 28, 2024
Summary
This study introduces an adaptive kernel approach for point cloud denoising in outdoor scenes. The method effectively removes noise while preserving crucial structural features, enhancing 3D data quality.
Area of Science:
- Computer Vision
- Geospatial Data Processing
- Signal Processing
Background:
- Point cloud noise significantly impacts downstream tasks like classification and 3D reconstruction.
- Effective noise removal for outdoor LiDAR data remains a critical challenge.
Purpose of the Study:
- To develop an adaptive noise removal method for point clouds from real-world outdoor scenes.
- To improve the accuracy and structural integrity of processed point cloud data.
Main Methods:
- Proposed an adaptive kernel approach based on local density and global statistics (AKA-LDGS).
- Utilized Bayesian estimation theory for the denoising framework.
- Dynamically set prior probabilities using spatial relationships and distance from LiDAR.
- Employed multivariate Gaussian distribution for real points and non-parametric KDE for noise points.
Main Results:
- Successfully removed noise from outdoor point cloud datasets.
- Preserved essential structural features of the point clouds.
- Demonstrated effectiveness in handling real-world outdoor scene noise.
Conclusions:
- The AKA-LDGS method offers an effective solution for point cloud denoising in challenging outdoor environments.
- This approach enhances the reliability of point cloud data for subsequent analysis and applications.
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