ResStreamGS: Residual-Sensitive Dual-Stream Gaussian Splatting for Mobile Volumetric Video Streaming

Key points

• Recent 3D Gaussian Splatting (3DGS) systems, such as V$^3$, enable efficient mobile streaming by encoding time-varying Gaussian attributes with commodity video codecs and hardware decoding.1
• Our empirical study shows that frame-wise adaptation and video coding of all Gaussians cause quality degradation in complex full-scene content, particularly in temporally stable regions.1
• To address this issue, we propose ResStreamGS, a residual-sensitive dual-stream framework that processes Gaussians according to their temporal behavior.1
• Experiments on complex full-scene volumetric videos show that ResStreamGS improves post-codec PSNR by 7.51--8.35 dB over a controlled V$^3$-style all-Gaussians baseline, reduces amortized payload by up to 56.5%, and achieves 30.883 FPS on a Qualcomm Snapdragon 778G platform.1

Sources (1)

• [1]ResStreamGS: Residual-Sensitive Dual-Stream Gaussian Splatting for Mobile Volumetric Video Streaming
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 02:25 AM

  Real-time mobile volumetric video enables immersive free-viewpoint experiences, but delivering high-quality 3D content under constrained bandwidth and computation remains challenging.

  Recent 3D Gaussian Splatting (3DGS) systems, such as V$^3$, enable efficient mobile streaming by encoding time-varying Gaussian attributes with commodity video codecs and hardware decoding.

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