分离静态动态成分，提升4D场景重建精度
SDD-4DGS: Static-Dynamic Aware Decoupling in Gaussian Splatting for 4D Scene Reconstruction
- 引入概率动态感知系数，自适应区分静态与动态元素
- 在5个基准数据集上重建质量优于现有方法
- 适合需要精细建模动态运动的3D/4D应用
场景中的静态与动态成分通常具有不同特性，但多数4D重建方法一视同仁，导致两类表现均不理想。本文提出SDD-4DGS，首个基于高斯点阵的静态-动态解耦4D场景重建框架。方法基于新型概率动态感知系数，自然融入高斯重建流程，实现静态与动态成分的自适应分离。通过精心设计的实现策略，模型能显式学习动态元素的运动模式，同时保持静态结构的几何稳定性。在五个基准数据集上的大量实验表明，SDD-4DGS在重建保真度上持续领先当前最优方法，静态结构细节恢复更佳，动态运动建模更精确。代码将公开。
原文摘要 · Abstract (English)
Dynamic and static components in scenes often exhibit distinct properties, yet most 4D reconstruction methods treat them indiscriminately, leading to suboptimal performance in both cases. This work introduces SDD-4DGS, the first framework for static-dynamic decoupled 4D scene reconstruction based on Gaussian Splatting. Our approach is built upon a novel probabilistic dynamic perception coefficient that is naturally integrated into the Gaussian reconstruction pipeline, enabling adaptive separation of static and dynamic components. With carefully designed implementation strategies to realize this theoretical framework, our method effectively facilitates explicit learning of motion patterns for dynamic elements while maintaining geometric stability for static structures. Extensive experiments on five benchmark datasets demonstrate that SDD-4DGS consistently outperforms state-of-the-art methods in reconstruction fidelity, with enhanced detail restoration for static structures and precise modeling of dynamic motions. The code will be released.
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