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    Area of Science:

    • Computer Vision
    • 3D Data Processing
    • LiDAR Technology

    Background:

    • Terrestrial LiDAR scanners capture large-scale 3D point clouds (LS3DPCs) that often contain virtual points due to glass reflections.
    • These virtual points can significantly degrade the performance of computer vision algorithms applied to LS3DPCs.

    Purpose of the Study:

    • To propose and evaluate a novel algorithm for the effective removal of virtual points from LS3DPCs.
    • To address the challenge of virtual points arising from multiple glass planes in 3D scanned environments.

    Main Methods:

    • Estimating multiple glass regions by modeling reliability based on multiple echo pulses per laser pulse.
    • Detecting virtual points by recursively traversing reflection trajectories and identifying optimal paths based on geometric similarity.
    • Evaluating the algorithm's performance on diverse LS3DPC models with varying numbers of glass planes.

    Main Results:

    • The proposed algorithm successfully estimates multiple glass regions.
    • Virtual points are detected with high accuracy.
    • The method demonstrates superior performance in reflection artifact removal compared to existing techniques, both qualitatively and quantitatively.

    Conclusions:

    • The developed algorithm effectively removes virtual points caused by glass reflections in LS3DPCs.
    • This technique enhances the quality and reliability of 3D point cloud data for subsequent computer vision tasks.
    • The algorithm offers a significant improvement over current methods for handling reflection artifacts in 3D scanning.