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    We introduce a deep learning method for Joint Geometry and Attribute Up-sampling (JGAU) to create denser colored point clouds. This method significantly improves up-sampling quality, outperforming existing techniques.

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    Area of Science:

    • Computer Vision
    • Computer Graphics
    • Machine Learning

    Background:

    • Colored point clouds are crucial for realistic 3D applications.
    • Existing methods struggle with generating large-scale, dense colored point clouds.

    Purpose of the Study:

    • To develop a deep learning-based method for joint geometry and attribute up-sampling of colored point clouds.
    • To establish a large-scale dataset for colored point cloud up-sampling research.

    Main Methods:

    • Proposed a Joint Geometry and Attribute Up-sampling (JGAU) framework with separate geometry and attribute networks.
    • Introduced Geometric Distance Weighted Attribute Interpolation (GDWAI) and Deep Learning-based Attribute Interpolation (DLAI) for coarse attribute up-sampling.
    • Developed an attribute enhancement module to refine attributes using geometry and attribute patterns.

    Main Results:

    • Achieved PSNR values of 33.90 dB (4x), 32.10 dB (8x), 31.10 dB (12x), and 30.39 dB (16x).
    • Demonstrated significant PSNR gains over state-of-the-art methods, averaging 2.32 dB, 2.47 dB, 2.28 dB, and 2.11 dB at respective up-sampling rates.
    • Released the SYSU-PCUD dataset with 121 large-scale colored point clouds.

    Conclusions:

    • The JGAU method effectively enhances colored point cloud density and quality.
    • The proposed approach offers substantial improvements over existing state-of-the-art up-sampling techniques.
    • The SYSU-PCUD dataset facilitates further research in colored point cloud up-sampling.