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    Area of Science:

    • Computer Vision
    • Geometric Modeling
    • 3D Data Processing

    Background:

    • 3D geometry acquisition is widespread due to low-cost scanners and improved multi-view stereo techniques.
    • Data from diverse devices exhibit significant variations in detail, scale, and coverage, necessitating registration for visualization, comparison, and archiving.
    • Current geometry registration methods struggle with intrinsic differences in models like sampling, scale, and noise.

    Purpose of the Study:

    • To present a method for automatic registration of multi-modal geometric data acquired by devices with differing properties.
    • To develop a scale-invariant matching technique robust to noise and variations in sampling density and detail.
    • To enable the estimation of relative scale between points in 3D models.

    Main Methods:

    • A descriptor based on Growing Least Squares is employed for feature extraction.
    • The method computes local similarity between geometry surrounding points and estimates relative scale.
    • Implemented in assisted and automatic registration procedures for point cloud analysis.

    Main Results:

    • The proposed method demonstrates robustness to noise, variations in sampling density, and differing levels of detail.
    • Scale-invariant matching and relative scale estimation are achieved.
    • Successful registration of multi-modal 3D models, including synthetic and real-world cases, regardless of data variations.

    Conclusions:

    • The developed method enables automatic registration of multi-modal 3D geometric data.
    • It effectively handles differences in noise, detail, scale, and unknown relative coverage between models.
    • Facilitates reliable visualization, comparison, and archiving of diverse 3D datasets.