Autonomous aerial scanning of target structures is crucial for practical applications, requiring online adaptation to unknown obstacles during flight. Existing methods largely emphasize collision avoidance and efficiency, but overlook occlusion-induced visibility degradation, severely compromising scanning quality. In this study, we propose FC-Vision, an on-the-fly visibility-aware replanning framework that proactively and safely prevents target occlusions while preserving the intended coverage and efficiency of the original plan. Our approach explicitly enforces dense surface-visibility constraints to regularize replanning behavior in real-time via an efficient two-level decomposition: occlusion-free viewpoint repair that maintains coverage with minimal deviation from the nominal scan intent, followed by segment-wise clean-sensing connection in 5-DoF space. A plug-in integration strategy is also presented to seamlessly interface FC-Vision with existing UAV scanning systems without architectural changes. Comprehensive simulation and real-world evaluations show that FC-Vision consistently improves scanning quality under unexpected occluders, delivering a maximum coverage gain of 55.32% and a 73.17% reduction in the occlusion ratio, while achieving real-time performance with a moderate increase in flight time. The source code will be made publicly available.

翻译：目标结构的自主空中扫描在实际应用中至关重要，需要在飞行过程中在线适应未知障碍物。现有方法主要强调避碰与效率，但忽视了遮挡导致的可见性退化问题，严重影响了扫描质量。本研究提出FC-Vision，一种在线可见性感知重规划框架，能够在保持原始规划预期覆盖范围与效率的同时，主动且安全地预防目标遮挡。该方法通过高效的两层分解机制，显式地施加密集表面可见性约束以实时规范重规划行为：首先执行无遮挡视点修复，在最小化偏离标称扫描意图的前提下维持覆盖度；随后在五自由度空间中进行分段洁净感知连接。本文还提出了一种插件式集成策略，可在无需改变架构的情况下将FC-Vision无缝接入现有无人机扫描系统。全面的仿真与实体验证表明，在突发遮挡物场景下，FC-Vision能持续提升扫描质量，实现最高55.32%的覆盖增益与73.17%的遮挡率降低，同时以适度的飞行时间增加为代价达成实时性能。源代码将公开发布。