license: cc-by-sa-4.0
tags:
- olat
pretty_name: >-
Dataset for BiGS: Bidirectional Primitives for Relightable 3D Gaussian
Splatting
BiGS Dataset
The OLAT dataset used in the paper BiGS: Bidirectional Primitives for Relightable 3D Gaussian Splatting (3DV 2025). Check out our project page.
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 (1.8 ~ 3.2 GB) 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 for how to use the dataset provided here to train BiGS, and our paper (arxiv) 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. 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.