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    This study introduces an automated method for converting large LiDAR point clouds into 3D models. The novel approach eliminates user-defined parameters for efficient urban reconstruction.

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    Area of Science:

    • Computer Vision
    • Computer Graphics
    • Geospatial Data Processing

    Background:

    • Large-scale urban reconstruction from LiDAR data is crucial but challenging.
    • Existing methods require labor-intensive, data-dependent user parameters.
    • Automating this process is essential for creating functional virtual environments.

    Purpose of the Study:

    • To develop an automated method for converting large LiDAR point clouds into simplified polygonal 3D models.
    • To eliminate the need for user-defined parameters in the reconstruction process.
    • To improve the efficiency and accuracy of urban reconstruction.

    Main Methods:

    • Data is divided into components processed concurrently to extract point metrics.
    • Extracted metrics are converted into tensors for clustering.
    • A parameter-free agglomerate clustering algorithm segments tensors into geospatial objects.
    • A multi-stage boundary refinement process uses global optimization for cluster boundary extraction.

    Main Results:

    • Successfully segmented geospatial objects like roads and buildings from diverse LiDAR datasets.
    • Demonstrated a robust method independent of data-specific parameters.
    • Achieved accurate boundary extraction through global optimization.

    Conclusions:

    • The proposed automated method effectively converts LiDAR point clouds into 3D models without user intervention.
    • The parameter-free clustering and boundary refinement offer a significant advancement in urban reconstruction.
    • Publicly available source code will facilitate further research and application.