用事件传感器和模糊图像重建高速无人机的清晰三维场景
Event-Aided Sharp Radiance Field Reconstruction for Fast-Flying Drones
- 融合事件流与模糊图像，联合优化相机位姿和辐射场
- 真实飞行数据上性能比顶尖方法提升50%以上
- 适合高速无人机视觉重建，无需真值标注
高速飞行的空中机器人在电池限制下可快速完成基础设施巡检、地形探测和搜救任务。然而高速导致图像严重运动模糊，位姿估计出现显著漂移与噪声，使对神经辐射场（NeRFs）的密集3D重建尤其困难。本文提出统一框架，利用异步事件流与运动模糊帧，实现敏捷飞行下的高保真辐射场重建。通过将事件-图像融合嵌入NeRF优化，并联合优化基于事件与图像模态的视觉惯性里程计先验，方法在无真值监督下恢复清晰辐射场与准确相机轨迹。我们在合成数据和真实高速无人机序列上验证了该方法。尽管RGB帧因运动模糊严重退化，位姿先验不可靠，本方法仍能重建高质量辐射场并保留精细场景细节，在真实数据上性能超越现有最佳方法超过50%。
原文摘要 · Abstract (English)
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.
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