Free-Viewpoint Video (FVV) reconstruction enables photorealistic and interactive 3D scene visualization; however, real-time streaming is often bottlenecked by sparse-view inputs, prohibitive training costs, and bandwidth constraints. While recent 3D Gaussian Splatting (3DGS) has advanced FVV due to its superior rendering speed, Streaming Free-Viewpoint Video (SFVV) introduces additional demands for rapid optimization, high-fidelity reconstruction under sparse constraints, and minimal storage footprints. To bridge this gap, we propose StreamLoD-GS, an LoD-based Gaussian Splatting framework designed specifically for SFVV. Our approach integrates three core innovations: 1) an Anchor- and Octree-based LoD-structured 3DGS with a hierarchical Gaussian dropout technique to ensure efficient and stable optimization while maintaining high-quality rendering; 2) a GMM-based motion partitioning mechanism that separates dynamic and static content, refining dynamic regions while preserving background stability; and 3) a quantized residual refinement framework that significantly reduces storage requirements without compromising visual fidelity. Extensive experiments demonstrate that StreamLoD-GS achieves competitive or state-of-the-art performance in terms of quality, efficiency, and storage.

翻译：自由视点视频重建技术能够实现照片级真实感与交互式三维场景可视化，然而其实时流式传输常受限于稀疏视角输入、高昂的训练成本及带宽约束。尽管近期提出的3D高斯泼溅技术凭借其卓越的渲染速度推动了自由视点视频的发展，但流式自由视点视频对快速优化、稀疏约束下的高保真重建以及最小化存储占用提出了更高要求。为弥合这一差距，本文提出StreamLoD-GS——一种专为流式自由视点视频设计的基于层级细节结构的高斯泼溅框架。我们的方法融合了三大核心创新：1）基于锚点与八叉树的层级细节结构化3D高斯泼溅系统，配合分层高斯丢弃技术，在保持高质量渲染的同时确保高效稳定的优化；2）基于高斯混合模型的运动分割机制，可分离动态与静态内容，在精修动态区域的同时保持背景稳定性；3）量化残差优化框架，在不损失视觉保真度的前提下显著降低存储需求。大量实验表明，StreamLoD-GS在重建质量、运行效率与存储开销方面均达到具有竞争力或最先进的性能水平。