Abstract
Robust 3D geometry estimation from videos is critical for applications such as autonomous navigation, SLAM, and 3D scene reconstruction. Recent methods like DUSt3R demonstrate that regressing dense pointmaps from image pairs enables accurate and efficient pose-free reconstruction. However, existing RGB-only approaches struggle under real-world conditions involving dynamic objects and extreme illumination, due to the inherent limitations of conventional cameras. In this paper, we propose EAG3R, a novel geometry estimation framework that augments pointmap-based reconstruction with asynchronous event streams. Built upon the MonST3R backbone, EAG3R introduces two key innovations: (1) a retinex-inspired image enhancement module and a lightweight event adapter with SNR-aware fusion mechanism that adaptively combines RGB and event features based on local reliability; and (2) a novel event-based photometric consistency loss that reinforces spatiotemporal coherence during global optimization. Our method enables robust geometry estimation in challenging dynamic low-light scenes without requiring retraining on night-time data. Extensive experiments demonstrate that EAG3R significantly outperforms state-of-the-art RGB-only baselines across monocular depth estimation, camera pose tracking, and dynamic reconstruction tasks.
Abstract (translated)
从视频中稳健地估计三维几何对于自主导航、SLAM（同步定位与地图构建）和三维场景重建等应用至关重要。最近的方法如DUSt3R表明，通过图像对回归密集点云可以实现准确且高效的无姿态（pose-free）重建。然而，现有的仅基于RGB的方法在涉及动态物体和极端光照条件的真实世界环境中表现不佳，这是由于传统相机的固有限制所致。 为此，在本文中我们提出了EAG3R，这是一种新型几何估计框架，它通过异步事件流增强点云基础的重建技术。基于MonST3R骨干网络，EAG3R引入了两项关键创新：（1）一个灵感来自Retinex理论的图像增强模块和一个轻量级事件适配器，该适配器具有信噪比感知融合机制，可以自适应地结合RGB和事件特征以提升局部可靠性；（2）一种新的基于事件的时间空间一致性损失函数，它在全局优化过程中加强了时域和空域的一致性。我们的方法能够在没有夜间数据重新训练的情况下，在挑战性的动态低光场景中实现稳健的几何估计。 大量的实验表明，EAG3R在单目深度估计、相机姿态跟踪以及动态重建任务上显著优于现有的仅基于RGB的方法，并且是当前最先进的基准方法。
URL
https://arxiv.org/abs/2512.00771
