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    This study introduces SUPPLE (Spherical UnwraPping ProfiLEs), a novel method for embedding unified skeletons into 3D scans. It enables accurate skeleton extraction for articulated objects, regardless of mesh quality or pose.

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    Area of Science:

    • Computer Vision
    • 3D Geometry Processing
    • Machine Learning

    Background:

    • Embedding unified skeletons is crucial for analyzing articulated objects.
    • Existing methods struggle with mesh quality, topology, and canonical pose requirements.
    • This limits applications in motion analysis and structural understanding.

    Purpose of the Study:

    • To develop a robust method for embedding unified skeletons into unregistered 3D scans.
    • To overcome limitations of existing registration-based and pose-dependent approaches.
    • To enable reliable skeleton extraction across diverse articulated object categories.

    Main Methods:

    • Introduced SUPPLE (Spherical UnwraPping ProfiLEs) for topology-independent surface unwrapping.
    • Developed a learning-based framework using fully convolutional architectures.
    • Localized and connected skeletal joints based on the lower-dimensional representation.

    Main Results:

    • Achieved reliable skeleton extraction from various articulated object categories.
    • Demonstrated effectiveness across raw scans and online CAD models.
    • SUPPLE's unwrapping is independent of mesh topology, water-tightness, and vertex density.

    Conclusions:

    • The proposed framework offers a robust solution for unified skeleton embedding.
    • SUPPLE enables accurate skeletal representations for unregistered and unposed 3D scans.
    • This advances the analysis of articulated objects in computer vision and graphics.