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Hilbert Space Filling Curve Based Scan-Order for Point Cloud Attribute Compression.

Jiafeng Chen, Lu Yu, Wenyi Wang

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |July 1, 2022
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces the Hilbert curve for reordering 3D point cloud data, significantly improving attribute compression. This method enhances spatial correlation, leading to better data compression for autonomous navigation and immersive applications.

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    Area of Science:

    • Computer Vision
    • Data Compression
    • 3D Data Processing

    Background:

    • Point clouds are crucial for 3D scene representation in autonomous navigation and immersive applications.
    • The large data volume of point clouds necessitates efficient compression techniques.
    • Current voxel-based compression schemes use naive point reordering, limiting efficiency.

    Purpose of the Study:

    • To analyze the 3C properties (Compactness, Clustering, Correlation) of different point cloud scan orders.
    • To identify a scan order that optimizes spatial and attribute correlation preservation for better compression.
    • To propose an efficient method for implementing the optimal scan order.

    Main Methods:

    • Theoretical analysis of point cloud 3C properties for various space-filling curves (Hilbert, Z-order, Gray-coded).
    • Statistical verification of attribute correlation preservation across different point cloud sparsities.
    • Development of a fast, iterative Hilbert address code generation for point reordering.

    Main Results:

    • The Hilbert curve demonstrates superior spatial correlation preservation compared to Z-order and Gray-coded curves.
    • Hilbert curve consistently shows the best attribute correlation preservation for varying point cloud sparsities.
    • The proposed Hilbert scan-order yields a 6.1% coding gain for prediction and 6.5% for transform coding.

    Conclusions:

    • The Hilbert curve-based scan order is highly effective for point cloud attribute compression.
    • This method offers significant coding gains, improving efficiency in 3D data processing.
    • The proposed approach is suitable for integration with existing point cloud attribute coding methods.