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 400×400 resolution on an RTX 3090 GPU.
3D 高斯投影（3D Gaussian Splatting, 3D-GS）支持实时渲染，但由于 RGB 相机的时间分辨率较低，在处理快速运动时表现不足。为了解决这一问题，我们首次将事件相机（Event Cameras）与可变形 3D-GS 相结合，利用事件相机捕获的高时间分辨率连续运动数据，实现快速动态场景重建。 我们观察到，事件的阈值建模对于实现高质量重建至关重要。因此，我们提出了一种 GS-Threshold Joint Modeling (GTJM) 策略，通过创建一个相互增强的过程，大幅提升了 3D 重建质量和阈值建模的准确性。此外，我们引入了一种 Dynamic-Static Decomposition (DSD) 策略，首先通过静态高斯无法表示运动的特性识别动态区域，然后采用基于缓冲的软分解方法将动态区域和静态区域分离。该策略通过避免静态区域中的不必要形变加速了渲染，同时聚焦于动态区域以提高细节保真度。 我们的方法在 RTX 3090 GPU 上以 400×400 分辨率实现了 156 FPS 的高保真动态重建。