| --- |
| license: other |
| task_categories: |
| - image-to-image |
| tags: |
| - brdfusion |
| - inverse-rendering |
| - relighting |
| - urban-scenes |
| - synthetic-data |
| - waymo-open-dataset |
| --- |
| |
| # BRDFusion Preprocessed Data |
|
|
| This repository contains the preprocessed evaluation and training data for the paper **BRDFusion: Physics Meets Generation for Urban Scene Inverse Rendering**. |
|
|
| [Project Page](https://shigon255.github.io/brdfusion-page/) | [GitHub](https://github.com/shigon255/BRDFusion) | [arXiv](https://arxiv.org/abs/2606.17049) |
|
|
| BRDFusion combines physics-based inverse rendering with generative modeling for high-quality urban scene relighting. It decomposes urban videos into geometry, materials, and HDR lighting for novel-view rendering, relighting, and scene-edit applications. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{liu2026brdfusionphysicsmeetsgeneration, |
| title={BRDFusion: Physics Meets Generation for Urban Scene Inverse Rendering}, |
| author={Yi-Ruei Liu and Jie-Ying Lee and Zheng-Hui Huang and Yu-Lun Liu and Chih-Hao Lin}, |
| year={2026}, |
| eprint={2606.17049}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CV}, |
| url={https://arxiv.org/abs/2606.17049}, |
| } |
| ``` |
|
|
