Abstract
Gaussian Splatting SLAM (GS-SLAM) offers a notable improvement over traditional SLAM methods, enabling photorealistic 3D reconstruction that conventional approaches often struggle to achieve. However, existing GS-SLAM systems perform poorly under persistent and severe motion blur commonly encountered in real-world scenarios, leading to significantly degraded tracking accuracy and compromised 3D reconstruction quality. To address this limitation, we propose EGS-SLAM, a novel GS-SLAM framework that fuses event data with RGB-D inputs to simultaneously reduce motion blur in images and compensate for the sparse and discrete nature of event streams, enabling robust tracking and high-fidelity 3D Gaussian Splatting reconstruction. Specifically, our system explicitly models the camera's continuous trajectory during exposure, supporting event- and blur-aware tracking and mapping on a unified 3D Gaussian Splatting scene. Furthermore, we introduce a learnable camera response function to align the dynamic ranges of events and images, along with a no-event loss to suppress ringing artifacts during reconstruction. We validate our approach on a new dataset comprising synthetic and real-world sequences with significant motion blur. Extensive experimental results demonstrate that EGS-SLAM consistently outperforms existing GS-SLAM systems in both trajectory accuracy and photorealistic 3D Gaussian Splatting reconstruction. The source code will be available at this https URL.
Abstract (translated)
Gaussian Splatting SLAM（GS-SLAM）相较于传统的SLAM方法提供了显著的改进，能够实现传统方法难以达到的逼真的三维重建效果。然而，现有的GS-SLAM系统在处理现实世界中常见的持续严重运动模糊时表现不佳，导致跟踪精度大幅降低和三维重建质量受损。为了解决这一局限性，我们提出了EGS-SLAM——一种新型的GS-SLAM框架，该框架融合了事件数据与RGB-D输入，同时减少图像中的运动模糊，并弥补事件流稀疏且离散的不足，从而实现稳健跟踪和高保真度的三维Gaussian Splatting重建。具体而言，我们的系统明确地建模了曝光期间相机连续轨迹的变化，支持基于事件感知和模糊感知进行统一的3D Gaussian Splatting场景下的跟踪与映射操作。此外，我们还引入了一个可学习的摄像机响应函数来对齐事件和图像之间的动态范围，并引入了一种无事件损失以在重建过程中抑制振铃伪影。 我们在一个包含显著运动模糊的新数据集上验证了我们的方法的有效性，该数据集涵盖了合成序列和真实世界序列。通过广泛的实验结果证明，EGS-SLAM系统在轨迹准确性和逼真的三维Gaussian Splatting重建方面始终优于现有的GS-SLAM系统。源代码将在以下链接中提供：[此处应为实际链接，请访问原始文档获取]。
URL
https://arxiv.org/abs/2508.07003
