LoD-Structured 3D Gaussian Splatting for Streaming Video Reconstruction
- URL: http://arxiv.org/abs/2601.18475v1
- Date: Mon, 26 Jan 2026 13:27:46 GMT
- Title: LoD-Structured 3D Gaussian Splatting for Streaming Video Reconstruction
- Authors: Xinhui Liu, Can Wang, Lei Liu, Zhenghao Chen, Wei Jiang, Wei Wang, Dong Xu,
- Abstract summary: Free-Viewpoint Video (FVV) reconstruction enables photorealistic and interactive 3D scene visualization.<n>Recent 3D Gaussian Splatting (3DGS) has advanced FVV due to its superior rendering speed.<n>We propose StreamLoD-GS, an LoD-based Gaussian Splatting framework designed specifically for SFVV.
- Score: 19.37120630668256
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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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