{"ID":2884406,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.07003","arxiv_id":"2508.07003","title":"EGS-SLAM: RGB-D Gaussian Splatting SLAM with Events","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 https://github.com/Chensiyu00/EGS-SLAM.","short_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 scen...","url_abs":"https://arxiv.org/abs/2508.07003","url_pdf":"https://arxiv.org/pdf/2508.07003v1","authors":"[\"Siyu Chen\",\"Shenghai Yuan\",\"Thien-Minh Nguyen\",\"Zhuyu Huang\",\"Chenyang Shi\",\"Jin Jing\",\"Lihua Xie\"]","published":"2025-08-09T14:44:55Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[]","has_code":false,"code_links":[{"ID":611078,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2884406,"paper_url":"https://arxiv.org/abs/2508.07003","paper_title":"EGS-SLAM: RGB-D Gaussian Splatting SLAM with Events","repo_url":"https://github.com/Chensiyu00/EGS-SLAM","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}