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    A new 3D point cloud model accurately calculates space object apparent magnitude by describing object shape. Increasing data points improves calculation accuracy for diverse objects and surfaces.

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    Area of Science:

    • Space object characterization
    • Computational astrophysics
    • Photometry

    Background:

    • Accurate apparent magnitude calculation is crucial for space object detection and tracking.
    • Existing models may struggle with complex shapes and surface details.
    • 3D point cloud data offers a detailed representation of object geometry.

    Purpose of the Study:

    • To introduce a novel 3D point-cloud-based model for calculating space object apparent magnitude.
    • To demonstrate the model's ability to handle various object shapes and surface textures.
    • To assess the impact of data resolution on model accuracy.

    Main Methods:

    • Development of a 3D point-cloud-based computational model.
    • Inputting 3D point cloud data and material parameters (specific wavelength).
    • Simulation of apparent magnitude calculation for objects with different geometries.

    Main Results:

    • The model accurately describes space object shapes and calculates apparent magnitude.
    • Complex shapes and wrinkled surfaces can be easily handled.
    • Increased point density in the 3D model demonstrably improves calculation accuracy.

    Conclusions:

    • The 3D point-cloud model provides an effective method for apparent magnitude calculation.
    • The model's accuracy is sensitive to the resolution of the input 3D data.
    • This approach facilitates the analysis of diverse space objects based on their geometric properties.