Use the following commands with Anaconda to create and activate your environment:
conda env create -f environment.yaml
conda activate cinegs
Note: Our installation method is the same as that of 3D Gaussian Splatting. If you encounter installation issues, please refer to the issues section of it.
For real dataset:
python train.py -s data_path -m output_path --fd_path .. --ap_path .. --exp_path .. --length_focal .. --blur
For rendering dataset:
python train.py -s data_path -m output_path --length_focal .. --start_checkpoint pretrained_path/chkpnt7000.pth --blur
please change the base_path to your own dataset path
For real dataset:
python render.py -m model_path -s data_path --fd_path .. --ap_path .. --exp_path .. --length_focal ..
For rendering dataset:
python render.py -m model_path -s data_path
The --blur flag should be disabled during rendering.
python render_taf.py -m model_path -s data_path --fd_path .. --ap_path .. --exp_path .. --length_focal .. --taf
The aperture size, focus distance, and exposure can be adjusted post-training. You can set appropriate parameters to view the editing results. (The default parameters are for the real dataset scene_3.)
@article{wang2024cinematic,
title={Cinematic Gaussians: Real-Time HDR Radiance Fields with Depth},
author={Wang, Chao and Wolski, Krzysztof and Kerbl, Bernhard and Serrano, Ana
and Bemana, Mojtaba and Seidel, Hans-Peter and Myszkowski, Karol and Leimk{\"u}hler, Thomas},
booktitle={Computer Graphics Forum},
volume={43},
number={7},
pages={1--13},
year={2024},
organization={Blackwell-Wiley}
}
}
This source code is derived from the (https://github.com/graphdeco-inria/gaussian-splatting/tree/main), and the depth rendering part is referred to (https://github.com/leo-frank/diff-gaussian-rasterization-depth). We really appreciate the contributions of the authors to that repository.