几何-光度事件基于的3D高斯射线追踪
Geometric-Photometric Event-based 3D Gaussian Ray Tracing
AI总结：
本文提出GPERT框架，通过分离事件渲染与快照渲染，解决事件基于3DGS的精度与时间分辨率平衡问题，实现高精度3D重建。
摘要：
事件相机提供比传统帧相机更高的时间分辨率，使其适用于运动和结构估计。然而，事件基于的3D高斯点云（3DGS）方法如何利用稀疏事件的细粒度时间信息仍不明确。本文提出GPERT框架，通过射线追踪和扭曲事件图像将渲染分为事件逐个生成的几何（深度）渲染和快照基于的光度（强度）渲染。大量评估显示，该方法在真实世界数据集上达到最先进的性能，在合成数据集上也表现出竞争力。此外，该方法无需先验信息（如预训练图像重建模型）或COLMAP初始化，事件选择数量更灵活，并在场景边缘实现锐利重建，且训练时间快。本文希望加深对事件稀疏性在3D重建中作用的理解。
英文摘要：
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 training time. We hope that this work deepens our understanding of the sparse nature of events for 3D reconstruction. https://github.com/e3ai/gpert
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
