This is the official implementation of LumiGauss: Relightable Gaussian Splatting in the Wild, accepted at WACV2025.
Preprocessed and used data are available here. Place the data in data subfolder. All configs for evaluation with gt test envmaps are provided in eval_files folders (only paths to envmap jpgs need to be adjusted). run_all.sh script uses these configs now.
Clone the Repository:
git clone https://github.com/joaxkal/lumigauss.git --recursive
cd lumigaussCreate the Environment:
conda env create --file environment.yml
conda activate lumigaussThe environment.yml file includes pinned versions of key dependencies. However, please note that certain packages may require adjustments depending on your specific CUDA version and GPU setup.
For users on different hardware setups, it's possible to create a base environment using the original 2DGS repository and then manually install missing dependencies.
We use data from NERF-OSR. Required dataset structure:
data/
├── images/ # Undistorted images
├── masks/ # Corresponding masks
├── sparse/ # COLMAP sparse reconstruction data
└── lk2_split.csv # Train/test split file
*_split.csv format:
filename;split
02-05_19_30_DSC_2599.jpg;train
25-08_19_30_IMG_0421.JPG;test
...
Refer to run_all.sh for scripts to train, render, and test. We provide two implementations: one using MLP and another with direct SH_env optimization. Initial tests showed significantly worse results with direct optimization, so the parameters were not further tuned. If you choose to use this version, additional fine-tuning may be required.
Use environment maps (JPG) and test masks provided by NERF-OSR. We follow the evaluation protocol from SOL-NERF:
- Align the test environment map with scene reconstruction from COLMAP (match ground).
- Assume the environment map aligns with the ground; adjust only the Y-axis rotation.
- Estimate the sun direction using shadows and lighting in ground-truth images.
- Configure the test_config file for your alignment and angle setup. Render images within the guessed angle range.
- Evaluate results using PSNR and select the angle with the highest PSNR.
We don't optimize sky Gaussians separately; they are treated like other objects in the scene — oriented toward the camera and illuminated by the environment map. Training environment maps work well for sky synthesis because they are optimized for such sky orientation. However, using external maps may introduce artifacts. For more realistic synthesis, consider optimizing segmented sky regions independently.
We acknowledge the following useful resources and repositories we built upon while developing LumiGauss:
-
2D Gaussian Splatting for Geometrically Accurate Radiance Fields
https://github.com/hbb1/2d-gaussian-splatting -
NeRF for Outdoor Scene Relighting
https://github.com/r00tman/NeRF-OSR -
Spherical Harmonics Repository
https://github.com/chalmersgit/SphericalHarmonics -
Precomputed Radiance Transfer by Jan Kautz
https://jankautz.com/courses/ShadowCourse/09-RadianceTransfer.pdf -
Spherical Harmonic Lighting: The Gritty Details by Robin Green
https://3dvar.com/Green2003Spherical.pdf -
Stupid Spherical Harmonics Tricks by Peter-Pike Sloan
https://www.ppsloan.org/publications/StupidSH36.pdf
If you find this work useful, please cite
@misc{kaleta2024lumigaussrelightablegaussiansplatting,
title={LumiGauss: Relightable Gaussian Splatting in the Wild},
author={Joanna Kaleta and Kacper Kania and Tomasz Trzcinski and Marek Kowalski},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
year = {2025}
}