EAG3R: Event-Augmented 3D Geometry Estimation for Dynamic and Extreme-Lighting Scenes

Xiaoshan Wu, Yifei Yu, Xiaoyang Lyu, Yi-Hua Huang, Bo Wang, Baoheng Zhang, Zhongrui Wang, XIAOJUAN QI

Keywords: Event Camera, 3D Vision, Neuromorphic Computing

TL;DR: EAG3R synergistically integrates standard RGB video frames with asynchronous event streams to achieve robust 3D geometry estimation in dynamic and extreme-lighting scenes where conventional RGB-only approaches struggle.

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 \textbf{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.

Primary Area: Applications (e.g., vision, language, speech and audio, Creative AI)

Submission Number: 6067