用事件相机实现高精度3D重建，兼顾速度与细节。
Geometric-Photometric Event-based 3D Gaussian Ray Tracing
- 分两路渲染：事件逐个算深度，快照式算亮度，各司其职。
- 真实数据集上表现领先，合成数据也具竞争力。
- 无需预训练模型或初始化，训练快、边缘清晰，适合动态场景。
事件相机相比传统帧相机具有更高的时间分辨率，适用于运动与结构估计。然而，事件驱动的3D高斯溅射（3DGS）如何利用稀疏事件的细粒度时间信息尚不明确。本文提出GPERT框架，解决事件3DGS中精度与时间分辨率之间的权衡问题。核心思想是将渲染解耦为两个分支：基于逐事件的几何（深度）渲染和基于快照的辐射度（亮度）渲染，通过光线追踪和变形事件图像实现。大量实验表明，该方法在真实世界数据集上达到当前最优性能，在合成数据集上也表现良好。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，对事件数量选择更灵活，且在场景边缘实现锐利重建，训练速度快。我们希望本工作深化对事件稀疏性在3D重建中作用的理解。
原文摘要 · Abstract (English)
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
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