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    This study introduces a unified geometry and attribute enhancement (UGAE) framework to improve lossy point cloud compression. UGAE significantly enhances geometry and attribute quality, outperforming existing methods for reduced storage and transmission costs.

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    Area of Science:

    • Computer Vision
    • 3D Data Processing
    • Signal Compression

    Background:

    • Lossy compression of point clouds is essential for reducing storage and transmission costs.
    • Existing methods suffer from irreversible distortion in geometry structure and attribute information.
    • High-frequency details are often lost during attribute compression.

    Purpose of the Study:

    • To propose a unified geometry and attribute enhancement (UGAE) framework to address distortion issues in lossy point cloud compression.
    • To reconstruct high-precision geometry and preserve attribute details effectively.
    • To enhance the overall perceptual quality of compressed point clouds.

    Main Methods:

    • The UGAE framework comprises three components: post-geometry enhancement (PoGE), pre-attribute enhancement (PAE), and post-attribute enhancement (PoAE).
    • PoGE utilizes a Transformer-based sparse convolutional U-Net for precise geometry reconstruction by predicting voxel occupancy probabilities.
    • PAE employs an enhanced geometry-guided recoloring strategy with a detail-aware K-Nearest Neighbors (DA-KNN) method for accurate recoloring.
    • PoAE uses an attribute residual prediction network with a weighted mean squared error (W-MSE) loss for attribute enhancement.

    Main Results:

    • UGAE significantly outperformed existing methods on benchmark datasets (8iVFB, Owlii, MVUB).
    • Achieved an average BD-PSNR gain of 9.98 dB and -90.54% BD-bitrate for geometry compared to TMC13v29.
    • Achieved a 3.34 dB BD-PSNR improvement and -55.53% BD-bitrate for attributes.
    • Demonstrated significant improvements in perceptual quality.

    Conclusions:

    • The proposed UGAE framework effectively mitigates distortion in lossy point cloud compression.
    • UGAE offers superior performance in both geometry and attribute enhancement, leading to higher quality point cloud data.
    • The framework provides a promising solution for efficient and high-fidelity point cloud storage and transmission.