Olbedo：面向大规模户外环境的反照率与光照数据集
Olbedo: An Albedo and Shading Aerial Dataset for Large-Scale Outdoor Environments
为户外本征图像分解提供了带多视图一致 albedo/shading、度量深度、法线与 HDR 天空穹顶的真实航空监督，可直接用于微调室内合成数据训练的 CNN/扩散 IID 模型，并支撑 3D 资产重光照与材质编辑。
原文依据 fine-tuning on Olbedo significantly improves single-view outdoor albedo prediction on the MatrixCity benchmark
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论户外场景的本征图像分解（IID）对重光照、编辑以及大规模环境理解至关重要，但由于缺乏具有可靠反照率与光照监督的真实数据集，相关进展一直受限。我们提出 Olbedo，一个面向野外户外反照率—光照分解的大规模航空数据集。Olbedo 包含 5,664 张无人机图像，覆盖四种地貌类型、多个年份以及多样的光照条件。每个视角都配有跨视图一致的反照率图与光照图、度量深度、表面法线、太阳与天空光照分量、相机位姿，并且对于较近期的飞行还提供了实测 HDR 天空穹顶。这些标注由一套逆渲染精化流程得到，该流程基于多视图立体重建与标定后的天空光照，并同时给出逐像素置信度掩码。我们证明 Olbedo 能使最优的 IID 模型（包括最初在合成室内数据上训练的 CNN 架构与扩散架构）泛化到真实户外图像：在 Olbedo 上微调可显著提升 MatrixCity 基准上的单视图户外反照率预测。我们进一步展示了经 Olbedo 训练的模型在 3D 资产的多视图一致重光照、材质编辑以及面向城市数字孪生的场景变化分析中的应用。我们发布该数据集、基线模型以及一套评测协议，以支持户外本征分解与光照感知航空视觉的未来研究。
英文原文摘要 1530 字符
Intrinsic image decomposition (IID) of outdoor scenes is crucial for relighting, editing, and understanding large-scale environments, but progress has been limited by the lack of real-world datasets with reliable albedo and shading supervision. We introduce Olbedo, a large-scale aerial dataset for outdoor albedo--shading decomposition in the wild. Olbedo contains 5,664 UAV images captured across four landscape types, multiple years, and diverse illumination conditions. Each view is accompanied by multi-view consistent albedo and shading maps, metric depth, surface normals, sun and sky shading components, camera poses, and, for recent flights, measured HDR sky domes. These annotations are derived from an inverse-rendering refinement pipeline over multi-view stereo reconstructions and calibrated sky illumination, together with per-pixel confidence masks. We demonstrate that Olbedo enables state-of-the-art IID models, including both CNN-based and diffusion-based architectures originally trained on synthetic indoor data, to generalize to real outdoor imagery: fine-tuning on Olbedo significantly improves single-view outdoor albedo prediction on the MatrixCity benchmark. We further illustrate applications of Olbedo-trained models to multi-view consistent relighting of 3D assets, material editing, and scene change analysis for urban digital twins. We release the dataset, baseline models, and an evaluation protocol to support future research in outdoor intrinsic decomposition and illumination-aware aerial vision.