Abstract
Fast-flying aerial robots promise rapid inspection under limited battery constraints, with direct applications in infrastructure inspection, terrain exploration, and search and rescue. However, high speeds lead to severe motion blur in images and induce significant drift and noise in pose estimates, making dense 3D reconstruction with Neural Radiance Fields (NeRFs) particularly challenging due to their high sensitivity to such degradations. In this work, we present a unified framework that leverages asynchronous event streams alongside motion-blurred frames to reconstruct high-fidelity radiance fields from agile drone flights. By embedding event-image fusion into NeRF optimization and jointly refining event-based visual-inertial odometry priors using both event and frame modalities, our method recovers sharp radiance fields and accurate camera trajectories without ground-truth supervision. We validate our approach on both synthetic data and real-world sequences captured by a fast-flying drone. Despite highly dynamic drone flights, where RGB frames are severely degraded by motion blur and pose priors become unreliable, our method reconstructs high-fidelity radiance fields and preserves fine scene details, delivering a performance gain of over 50% on real-world data compared to state-of-the-art methods.
Abstract (translated)
高速飞行的空中机器人在电池限制条件下能够快速进行检查，具有广泛的应用前景，例如基础设施检测、地形探索和搜索救援。然而，高速度会导致图像中出现严重的运动模糊，并引发姿态估计中的显著漂移和噪声，使得使用神经辐射场（NeRFs）进行密集三维重建变得尤为困难，因为它们对这类退化非常敏感。 在此工作中，我们提出了一种统一框架，利用异步事件流与运动模糊帧结合来从敏捷无人机飞行中重建高保真度的辐射场。通过将事件-图像融合嵌入到NeRF优化过程中，并使用事件和帧两种模态共同细化基于事件的姿态视觉惯性测距先验知识，我们的方法能够在没有地面真实监督的情况下恢复清晰的辐射场以及准确的相机轨迹。 我们在合成数据集及由高速飞行无人机捕获的真实世界序列上验证了该方法的有效性。即使在高度动态的无人机飞行中，RGB帧严重受运动模糊影响且姿态先验变得不可靠时，我们的方法仍能重建高保真的辐射场并保留精细场景细节，在真实世界数据上的表现比现有最佳方法高出超过50%。
URL
https://arxiv.org/abs/2602.21101
