Update README.md

README.md

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-license: cc-by-sa-4.0
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+---
+license: cc-by-sa-4.0
+tags:
+- olat
+pretty_name: 'Dataset for BiGS: Bidirectional Primitives for Relightable 3D Gaussian Splatting'
+---
+
+The OLAT dataset used in the paper _BiGS: Bidirectional Primitives for Relightable 3D Gaussian Splatting_ (3DV 2025). Check out our [project page](https://desmondlzy.me/publications/bigs/).
+
+## Dataset
+
+We provide 7 synthetic scenes in the dataset, featuring various complex light transport effects, such as subsurface scattering, fuzzy surfaces, and iridescent reflection. 
+
+Each scene consists of:
+- 40 training OLAT conditions (`olat_1` - `olat_40`) with 48 views per light condition;
+- 58 test OLAT conditions (`olat_41` - `olat_98`) with 1 view per light condition;
+- 1 all-light-on conditions (`olat_all`) with 48 views per light conditions.
+
+Each light condition includes `.exr` images, object masks, transforms with camera poses, light positions and intensities. 
+
+Please refer to our [github repo](https://github.com/desmondlzy/bigs) for how to use the dataset provided here to train BiGS, 
+and our [paper (arxiv)](https://www.arxiv.org/abs/2408.13370) for details of BiGS.
+
+## Citation
+
+If you use our dataset in your research, please consider citing us with the below bibtex entry:
+```
+@misc{zhenyuan2024bigs,
+      title={BiGS: Bidirectional Primitives for Relightable 3D Gaussian Splatting}, 
+      author={Liu Zhenyuan and Yu Guo and Xinyuan Li and Bernd Bickel and Ran Zhang},
+      year={2024},
+      eprint={2408.13370},
+      url={https://arxiv.org/abs/2408.13370}, 
+}
+```
+
+## Acknowledgments
+
+Our synthetic data is generated using [Mitsuba](https://mitsuba.readthedocs.io/en/stable/).
+We thank the 3D models' creators: 
+Keenan Crane for _Spot_;
+Stanford Computer Graphics Laboratory for the models _Dragon_ and _Bunny_; 
+Wenzel Jakob for the model _Mistuba Ball_.
+Special thanks to Changxi Zheng for supporting the internship program at Tencent Pixel Lab.