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

TL;DR

EAG3R introduces a novel framework combining RGB images and asynchronous event streams with innovative modules and loss functions to improve 3D geometry estimation in challenging dynamic and low-light scenes.

Contribution

The paper presents a new event-augmented approach that enhances dense pointmap reconstruction with adaptive fusion and a novel photometric consistency loss, improving robustness without retraining for night scenes.

Findings

Outperforms RGB-only methods in low-light and dynamic scenes

Enables accurate depth and pose estimation without retraining for night scenes

Demonstrates significant improvements across multiple 3D reconstruction tasks

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.