提出自适应3D高斯点云视频流传输方案，解决大体积数据压缩与带宽波动难题。
Adaptive 3D Gaussian Splatting Video Streaming
- 基于高斯变形场构建3DGS视频，结合显著性分块与差异化质量建模
- 在多种带宽下保持高质量传输，压缩效率优于现有方法
- 适合高保真三维视频流应用，如虚拟现实与远程交互
3D高斯点云（3DGS）的出现显著提升了体素视频的表示质量。然而，相较于传统体素视频，3DGS视频因数据量巨大且压缩传输复杂度高，给流媒体传输带来挑战。为此，我们提出一种创新的3DGS体素视频流传输框架。具体地，设计了一种基于高斯变形场的3DGS视频构建方法；通过混合显著性分块与差异化质量建模，实现高效数据压缩，并能自适应带宽波动，同时保障高传输质量。进一步构建了完整的3DGS视频流系统并验证其传输性能。实验结果表明，该方法在视频质量、压缩效率和传输速率等方面均优于现有方法。
原文摘要 · Abstract (English)
The advent of 3D Gaussian splatting (3DGS) has significantly enhanced the quality of volumetric video representation. Meanwhile, in contrast to conventional volumetric video, 3DGS video poses significant challenges for streaming due to its substantially larger data volume and the heightened complexity involved in compression and transmission. To address these issues, we introduce an innovative framework for 3DGS volumetric video streaming. Specifically, we design a 3DGS video construction method based on the Gaussian deformation field. By employing hybrid saliency tiling and differentiated quality modeling of 3DGS video, we achieve efficient data compression and adaptation to bandwidth fluctuations while ensuring high transmission quality. Then we build a complete 3DGS video streaming system and validate the transmission performance. Through experimental evaluation, our method demonstrated superiority over existing approaches in various aspects, including video quality, compression effectiveness, and transmission rate.
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