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base_model:
- zheng95z/rgb-to-x
- prs-eth/marigold-iid-appearance-v1-1
- prs-eth/marigold-iid-lighting-v1-1
datasets:
- GDAOSU/Olbedo
---
# Olbedo: An Albedo and Shading Aerial Dataset for Large-Scale Outdoor Environments
[](https://gdaosu.github.io/olbedo/)
[](https://huggingface.co/spaces/GDAOSU/olbedo)
[](https://huggingface.co/GDAOSU/olbedo)
[](https://huggingface.co/datasets/GDAOSU/Olbedo)
[](https://arxiv.org/abs/2602.22025)
This repository contains the official implementation and inference code for **Olbedo**.
## 🔗 Resources
We provide a comprehensive suite of resources for this project:
* **Project Page:** [https://gdaosu.github.io/olbedo/](https://gdaosu.github.io/olbedo/)
* **Interactive Demo:** [Hugging Face Spaces](https://huggingface.co/spaces/GDAOSU/olbedo)
* **Pre-trained Models:** [Hugging Face Model Hub](https://huggingface.co/GDAOSU/olbedo)
* **Dataset:** [Hugging Face Datasets](https://huggingface.co/datasets/GDAOSU/Olbedo)
* **Paper:** [arXiv:2602.22025](https://arxiv.org/abs/2602.22025)
## 🚀 Usage
We provide Docker support to ensure a consistent environment for running inference.
### 1. Build the Environment
First, clone this repository and build the Docker image. This will set up all necessary dependencies.
```bash
bash build_docker.sh
```
### 2. Run Inference
To run inference on your own images, use the run_inference.sh script. You must specify the input directory containing your images and the output directory where results will be saved.
```bash
bash run_inference.sh <input_directory> <output_directory>
```
## 📂 Data & Models
If you wish to use the data or models separately, they are hosted on Hugging Face:
| Resource | Link | Description |
| :--- | :--- | :--- |
| **Model Weights** | [Download Here](https://huggingface.co/GDAOSU/olbedo) | Pre-trained checkpoints for the Olbedo architecture. |
| **Dataset** | [Download Here](https://huggingface.co/datasets/GDAOSU/Olbedo) | The dataset used for training and evaluation. |
## 📝 Citation
If you find this project useful for your research, please consider citing our work:
```
@misc{song2026olbedoalbedoshadingaerial,
title={Olbedo: An Albedo and Shading Aerial Dataset for Large-Scale Outdoor Environments},
author={Shuang Song and Debao Huang and Deyan Deng and Haolin Xiong and Yang Tang and Yajie Zhao and Rongjun Qin},
year={2026},
eprint={2602.22025},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={[https://arxiv.org/abs/2602.22025](https://arxiv.org/abs/2602.22025)},
}
```
## 🙏 Acknowledgements
This codebase is largely built upon the following excellent projects:
* **[Marigold](https://github.com/prs-eth/Marigold)**
* **[RGBX](https://github.com/zheng95z/rgbx)**
We thank the authors for their open-source contributions.
## 🎫 License
The dataset associated with this work is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0) (as defined in [LICENSE-DATA](LICENSE-DATA.txt)).
This code of this work is licensed under the Apache License, Version 2.0 (as defined in the [LICENSE](LICENSE.txt)).
The Marigold pretrained and fine-tuned models are licensed under RAIL++-M License (as defined in the [LICENSE-MODEL](LICENSE-MODEL.txt)).
The RGBX pretrained and fine-tuned models are licensed under ADOBE RESEARCH LICENSE(as defined in the [LICENSE-ADOBE](LICENSE-ADOBE.txt)).
By downloading and using the dataset, code and models you agree to the terms in [LICENSE-DATA](LICENSE-DATA.txt), [LICENSE](LICENSE.txt), [LICENSE-MODEL](LICENSE-MODEL.txt), and [LICENSE-ADOBE](LICENSE-ADOBE.txt) respectively. |