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

2026-02-24

AI Summary

Outdoor intrinsic image decomposition is hindered by the scarcity of large-scale real-world datasets with high-fidelity albedo and shading annotations. To address this limitation, this work proposes Olbedo—the first large-scale aerial intrinsic image dataset tailored for outdoor scenes—comprising 5,664 drone-captured images under diverse times of day and lighting conditions. Leveraging multi-view stereo reconstruction, inverse rendering optimization, and HDR sky illumination modeling, Olbedo provides pixel-wise reliable ground-truth annotations for albedo, shading, depth, surface normals, and lighting parameters. Models fine-tuned on Olbedo significantly improve single-view albedo prediction performance on MatrixCity and demonstrate practical utility in 3D asset relighting, material editing, and change analysis within urban digital twins.

Technology Category

• Computer Vision: Computational Photography, Image & Video Synthesis
• Machine Learning: Multi-instance/Multi-view Learning
• Humans and AI: Crowd Sourcing and Human Computation

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Large pretrained models with web data
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Intrinsic Image Decomposition
• Albedo
• Shading
• Outdoor Scenes
• Aerial Dataset

Innovation

• intrinsic image decomposition
• albedo-shading decomposition
• aerial dataset
• inverse rendering
• outdoor relighting