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Three-dimensional point cloud denoising via a gravitational feature function.

Chunhao Shi, Chunyang Wang, Xuelian Liu

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    Summary
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    This study introduces a novel point cloud denoising algorithm using a gravitational feature function to accurately remove noise from LiDAR scans. The method effectively filters sparse, dense, and mixed noises while preserving point cloud structure.

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    Area of Science:

    • Computer Vision
    • Geospatial Data Processing
    • 3D Scanning Technologies

    Background:

    • LiDAR scanning inherently produces noisy point clouds, compromising measurement accuracy.
    • Existing denoising methods may struggle with preserving both local and global point cloud structures.
    • Noise in point cloud data affects the integrity and reliability of 3D object reconstruction and analysis.

    Purpose of the Study:

    • To develop a novel point cloud denoising algorithm based on gravitational principles.
    • To introduce a universal gravitation formula specifically for point cloud data.
    • To effectively remove various types of noise (sparse, dense, mixed) while preserving essential geometric features.

    Main Methods:

    • Calculating the point cloud barycenter and spherical neighborhoods.
    • Applying a universal gravitation formula to compute gravitational forces between points and the barycenter.
    • Developing a gravitational feature function by combining these forces.
    • Filtering noise using a threshold on the gravitational feature function.

    Main Results:

    • The proposed algorithm effectively removes sparse, dense, and mixed noise from point clouds.
    • The method preserves the local and global structural integrity of the point cloud data.
    • Experimental results validate the algorithm's effectiveness in diverse noise scenarios.

    Conclusions:

    • The gravitational feature function-based algorithm offers a robust solution for point cloud denoising.
    • This novel approach enhances measurement accuracy and data integrity in LiDAR applications.
    • The algorithm demonstrates significant potential for improving 3D data processing pipelines.