用事件相机提升动态场景重建精度，实现更流畅的运动捕捉。
Event-boosted Deformable 3D Gaussians for Dynamic Scene Reconstruction
- 结合事件相机与可变形3D高斯点云，利用高帧率运动数据增强重建。
- 提出联合阈值建模与动静分离策略，显著提升重建质量和渲染速度。
- 首个包含事件数据的4D动态场景基准，适合做动态视觉重建研究者参考。
可变形3D高斯溅射（3D-GS）受限于普通摄像头低时间分辨率导致的中间运动信息缺失。为此，我们首次将能捕捉高时间分辨率连续运动数据的事件相机与可变形3D-GS结合，用于动态场景重建。我们发现事件阈值建模在高质量重建中起关键作用，因此提出GS-阈值联合建模策略，形成相互促进的优化过程，显著提升3D重建与阈值建模效果。此外，我们引入动态-静态分解策略：先通过静态高斯无法表达运动的特性识别动态区域，再采用基于缓冲区的软分解方式分离动态与静态部分。该策略通过避免静态区域的无谓形变加速渲染，并集中优化动态区域以提高保真度。我们还构建了首个包含事件数据的4D动态场景基准，涵盖合成与真实场景，所提方法在该基准上达到当前最优性能。
原文摘要 · Abstract (English)
Deformable 3D Gaussian Splatting (3D-GS) is limited by missing intermediate motion information due to the 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 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 strategy, creating a mutually reinforcing process that greatly improves both 3D reconstruction and threshold modeling. Moreover, we introduce a Dynamic-Static Decomposition 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. Additionally, we contribute the first event-inclusive 4D benchmark with synthetic and real-world dynamic scenes, on which our method achieves state-of-the-art performance.
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