UAVLight：无人机场景下光照鲁棒三维重建的基准
UAVLight: A Benchmark for Illumination-Robust 3D Reconstruction in Unmanned Aerial Vehicle (UAV) Scenes
为多视图立体、SfM 与神经渲染方法提供了一个几何与视角固定、仅光照变化的受控评测基准，可用于诊断和缓解光照不一致导致的几何漂移、颜色不一致与阴影烙印问题，并支撑可重光照重建研究。
原文依据 a controlled-yet-real benchmark for illumination-robust 3D reconstruction
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论光照不一致是多视图三维重建中的一个根本性挑战。太阳方向、云层覆盖与阴影的变化，破坏了经典多视图立体（MVS）与运动恢复结构（SfM）流程以及近期神经渲染方法所依赖的恒定光照假设，从而导致几何漂移、颜色不一致与阴影烙印。该问题在基于无人机（UAV）的重建中尤为关键，因为长航时与户外环境使光照变化不可避免。然而，现有数据集要么将采集限制在较短时间窗口内，因而缺乏有意义的光照多样性；要么跨越数月与多个季节，其中几何与语义变化会混淆对光照鲁棒性的单独研究。我们提出 UAVLight，一个受控而真实的、面向光照鲁棒三维重建的基准。每个场景均沿可重复、带地理参考的飞行路径在一天中的多个固定时刻采集，从而在几何、标定与视角保持一致的前提下产生自然的光照变化。UAVLight 提供跨光照条件的标准化评测协议，为开发与评测在真实户外环境中保持一致、忠实且可重光照的重建方法提供了可靠基础。
英文原文摘要 1303 字符
Illumination inconsistency is a fundamental challenge in multi-view 3D reconstruction. Variations in sunlight direction, cloud cover, and shadows break the constant-lighting assumption underlying both classical multi-view stereo (MVS) and structure from motion (SfM) pipelines and recent neural rendering methods, leading to geometry drift, color inconsistency, and shadow imprinting. This issue is especially critical in UAV-based reconstruction, where long flight durations and outdoor environments make lighting changes unavoidable.However, existing datasets either restrict capture to short time windows, thus lacking meaningful illumination diversity, or span months and seasons, where geometric and semantic changes confound the isolated study of lighting robustness.We introduce UAVLight, a controlled-yet-real benchmark for illumination-robust 3D reconstruction. Each scene is captured along repeatable, geo-referenced flight paths at multiple fixed times of day, producing natural lighting variation under consistent geometry, calibration, and viewpoints. With standardized evaluation protocols across lighting conditions, UAVLight provides a reliable foundation for developing and benchmarking reconstruction methods that are consistent, faithful, and relightable in real outdoor environments.