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.

本研究针对4D场景重建中静态和动态组件被无差别处理的问题，提出了SDD-4DGS框架，这是第一个基于高斯溅射的静态-动态解耦4D场景重建方法。通过引入新颖的概率动态感知系数，我们的方法能够自适应地分离静态与动态组件，实现了动态元素运动模式的显式学习和静态结构几何稳定性的维护，实验结果显示该方法在重建保真度和细节恢复方面优于现有最先进方法。

SDD-4DGS：基于高斯溅射的静态-动态感知解耦的4D场景重建