面向流式视频重建的分层高斯点云框架，兼顾速度、质量与存储。
LoD-Structured 3D Gaussian Splatting for Streaming Video Reconstruction
- 基于锚点与八叉树的分层结构，实现高效稳定优化。
- 动态背景分离重建，提升稀疏视图下画面质量。
- 量化残差精修压缩存储，适合实时流传输场景。
自由视角视频（FVV）可实现逼真交互式3D场景可视化；但实时流传输常受限于稀疏视角输入、高昂训练成本及带宽瓶颈。尽管近期3D高斯泼溅（3DGS）在FVV中取得进展，流式自由视角视频（SFVV）仍需快速优化、稀疏约束下的高保真重建以及极小存储占用。为此，我们提出StreamLoD-GS——专为SFVV设计的分层细节（LoD）结构高斯泼溅框架。核心创新包括：1）基于锚点与八叉树的层次化3DGS结构，结合分层高斯丢弃机制，确保高效稳定优化并保持高质量渲染；2）基于高斯混合模型（GMM）的运动分割机制，分离动态与静态内容，在细化动态区域的同时保持背景稳定；3）量化残差精修框架，显著降低存储需求而不损失视觉保真度。大量实验表明，StreamLoD-GS在质量、效率与存储方面均达到竞争性或领先水平。
原文摘要 · Abstract (English)
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.
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