イベントカメラ支援による高速飛行ドローンのための鮮明な放射輝度場再構成
Event-Aided Sharp Radiance Field Reconstruction for Fast-Flying Drones
高速飛行するドローンの運動ぼけ画像とイベントストリームを融合し、NeRF最適化と自己運動推定を同時に精錬することで、高精度な3D放射輝度場とカメラ軌跡を教師なしで復元する手法を提案。
詳しい要約
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著者: Rong Zou, Marco Cannici, Davide Scaramuzza
分類: cs.CV, cs.RO
原文アブストラクト
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.
関連論文
- Deblur e-NeRF: 高速・低照度環境下での動きぼやけイベントからのNeRF再構成NeRF/イベントカメラ
- E-NeRF: 移動するイベントカメラからのニューラル放射輝度場NeRF/イベントカメラ
