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Updated: Jan 9, 2026

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Neural Compression System for Point Cloud Video Streaming.

Junteng Zhang, Tong Chen, Dandan Ding

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    This summary is machine-generated.

    Yak, a layered point cloud compressor, enhances video streaming by efficiently compressing geometry and attributes. It optimizes bandwidth usage for networked services, outperforming existing methods.

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    Area of Science:

    • Computer Vision
    • Multimedia Compression
    • 3D Graphics

    Background:

    • Point cloud video streaming is crucial for immersive media.
    • Existing compression methods lack efficiency and coder control for networked applications.
    • Bandwidth constraints and fluctuations pose challenges for point cloud services.

    Purpose of the Study:

    • To propose Yak, a novel layered point cloud compressor.
    • To address limitations in performance and coder control for networked point cloud services.
    • To enable efficient intra and inter-frame coding of geometry and attributes.

    Main Methods:

    • A three-layer architecture: Base Layer (G-PCC thumbnail), Enhancement Layer (variational autoencoder), and Dynamic Layer (feature-space predictions).
    • A Content Analysis module for dynamic encoding parameter determination and intelligent bit budget allocation.
    • Parametric rate/distortion models with template matching and delta updating for rate control.

    Main Results:

    • Yak demonstrated superior rate-distortion performance compared to MPEG G-PCC, V-PCC, and other learning-based approaches.
    • The system offers flexible networked adaptation and manageable computational complexity.
    • Effective compression of both geometry and attribute components in point cloud sequences.

    Conclusions:

    • Yak provides a system-level solution for efficient point cloud video streaming.
    • The layered approach and dynamic content analysis enable optimized performance under varying network conditions.
    • Yak represents a significant advancement for practical networked point cloud services.