[2512.18640] Geometric-Photometric Event-based 3D Gaussian Ray Tracing

Computer Science > Computer Vision and Pattern Recognition arXiv:2512.18640 (cs) [Submitted on 21 Dec 2025 (v1), last revised 1 Apr 2026 (this version, v2)] Title:Geometric-Photometric Event-based 3D Gaussian Ray Tracing Authors:Kai Kohyama, Yoshimitsu Aoki, Guillermo Gallego, Shintaro Shiba View a PDF of the paper titled Geometric-Photometric Event-based 3D Gaussian Ray Tracing, by Kai Kohyama and 3 other authors View PDF HTML (experimental) Abstract:Event cameras offer a high temporal resolution over traditional frame-based cameras, which makes them suitable for motion and structure estimation. However, it has been unclear how event-based 3D Gaussian Splatting (3DGS) approaches could leverage fine-grained temporal information of sparse events. This work proposes GPERT, a framework to address the trade-off between accuracy and temporal resolution in event-based 3DGS. Our key idea is to decouple the rendering into two branches: event-by-event geometry (depth) rendering and snapshot-based radiance (intensity) rendering, by using ray-tracing and the image of warped events. The extensive evaluation shows that our method achieves state-of-the-art performance on the real-world datasets and competitive performance on the synthetic dataset. Also, the proposed method works without prior information (e.g., pretrained image reconstruction models) or COLMAP-based initialization, is more flexible in the event selection number, and achieves sharp reconstruction on scene edges with fast...