3D Gaussian Splatting (3D-GS) enables real-time rendering but struggles with fast motion due to low temporal resolution of RGB cameras. To address this, we introduce the first approach combining event cameras, which capture high-temporal-resolution, continuous motion data, with deformable 3D-GS for fast dynamic scene reconstruction. We observe that threshold modeling for events plays a crucial role in achieving high-quality reconstruction. Therefore, we propose a GS-Threshold Joint Modeling (GTJM) strategy, creating a mutually reinforcing process that greatly improves both 3D reconstruction and threshold modeling. Moreover, we introduce a Dynamic-Static Decomposition (DSD) strategy that first identifies dynamic areas by exploiting the inability of static Gaussians to represent motions, then applies a buffer-based soft decomposition to separate dynamic and static areas. This strategy accelerates rendering by avoiding unnecessary deformation in static areas, and focuses on dynamic areas to enhance fidelity. Our approach achieves high-fidelity dynamic reconstruction at 156 FPS with a 400$\times$400 resolution on an RTX 3090 GPU.

本研究解决了快速动态场景重建中RGB相机的低时间分辨率带来的问题。通过结合高时间分辨率的事件相机与可变形3D高斯体，提出了一种GS-阈值联合建模策略，显著提升了重建质量，并通过动态-静态分解策略加快了渲染速度。这种新方法在RTX 3090 GPU上实现高保真动态重建，帧率达到156 FPS。

事件增强的可变形3D高斯体用于快速动态场景重建