Olbedo: An Albedo and Shading Aerial Dataset for Large-Scale Outdoor Environments

TL;DR

Olbedo is a comprehensive aerial dataset designed for outdoor albedo and shading decomposition, enabling improved real-world intrinsic image analysis and applications like relighting and scene editing.

Contribution

The paper introduces Olbedo, a large-scale, multi-view aerial dataset with reliable annotations, facilitating advances in outdoor intrinsic image decomposition and related tasks.

Findings

Fine-tuning diffusion models on Olbedo improves outdoor albedo prediction.

Olbedo enables multi-view consistent relighting and material editing.

The dataset supports generalization of indoor-trained models to outdoor scenes.

Abstract

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 diffusion-based IID models, 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.