cd custom
git clone https://github.com/DSaurus/threestudio-3dgs.git
cd threestudio-3dgs
git clone --recursive https://github.com/ashawkey/diff-gaussian-rasterization
git clone https://github.com/DSaurus/simple-knn.git
pip install ./diff-gaussian-rasterization
pip install ./simple-knn

cd custom
git clone https://github.com/DSaurus/threestudio-3dgs.git
cd threestudio-3dgs
git clone [email protected]:graphdeco-inria/gaussian-splatting.git --recursive
cd gaussian-splatting/submodules
python -m pip install diff-gaussian-rasterization/.
python -m pip install simple-knn/

# If you want to export mesh, please install pymeshlab
pip install pymeshlab

# Native Gaussian Splatting + SDS Loss
python launch.py --config custom/threestudio-3dgs/configs/gaussian_splatting.yaml  --train --gpu 0 system.prompt_processor.prompt="a delicious hamburger"

# Advanced Gaussian Splatting with background + SDS Loss
python launch.py --config custom/threestudio-3dgs/configs/gaussian_splatting_background.yaml  --train --gpu 0 system.prompt_processor.prompt="a delicious hamburger"

# Advanced Gaussian Splatting with background and shading + SDS Loss
python launch.py --config custom/threestudio-3dgs/configs/gaussian_splatting_shading.yaml  --train --gpu 0 system.prompt_processor.prompt="a delicious hamburger"

# Advanced Gaussian Splatting with background and shading + MVDream
python launch.py --config custom/threestudio-3dgs/configs/gaussian_splatting_mvdream.yaml  --train --gpu 0 system.prompt_processor.prompt="an astronaut riding a horse"

# Advanced Gaussian Splatting + Zero-123
python launch.py --config custom/threestudio-3dgs/configs/gaussian_splatting_zero123.yaml --train --gpu 0 data.image_path=./load/images/anya_front_rgba.png

# resume training from the last checkpoint, you may replace last.ckpt with any other checkpoints
python launch.py --config path/to/trial/dir/configs/parsed.yaml --train --gpu 0 resume=path/to/trial/dir/ckpts/last.ckpt

# load from Gaussian Splatting ply file
python launch.py --config custom/threestudio-3dgs/configs/gaussian_splatting.yaml  --train --gpu 0 system.prompt_processor.prompt="a delicious hamburger" system.geometry.geometry_conver_from=path/to/poinc_cloud.ply

# only load points position and color from ply file
python launch.py --config custom/threestudio-3dgs/configs/gaussian_splatting.yaml  --train --gpu 0 system.prompt_processor.prompt="a delicious hamburger" system.geometry.geometry_conver_from=path/to/poinc_cloud.ply system.geometry.load_ply_only_vertex=true

# load from shap-e initialization
python launch.py --config custom/threestudio-3dgs/configs/gaussian_splatting.yaml  --train --gpu 0 system.prompt_processor.prompt="a delicious hamburger" system.geometry.geometry_convert_from="shap-e:a delicious hamburger"

# load from lrm initialization
python launch.py --config custom/threestudio-3dgs/configs/gaussian_splatting.yaml  --train --gpu 0 system.prompt_processor.prompt="a delicious hamburger" system.geometry.geometry_convert_from="lrm:a delicious hamburger"

python launch.py --config path/to/config --export --gpu 0 system.prompt_processor.prompt="a delicious hamburger" resume=path/to/last.ckpt

@Article{kerbl3Dgaussians,
    author       = {Kerbl, Bernhard and Kopanas, Georgios and Leimk{\"u}hler, Thomas and Drettakis, George},
    title        = {3D Gaussian Splatting for Real-Time Radiance Field Rendering},
    journal      = {ACM Transactions on Graphics},
    number       = {4},
    volume       = {42},
    month        = {July},
    year         = {2023},
    url          = {https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/}
}

@article{tang2023dreamgaussian,
    title={DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation},
    author={Tang, Jiaxiang and Ren, Jiawei and Zhou, Hang and Liu, Ziwei and Zeng, Gang},
    journal={arXiv preprint arXiv:2309.16653},
    year={2023}
}

@article{GaussianDreamer,
    title={GaussianDreamer: Fast Generation from Text to 3D Gaussian Splatting with Point Cloud Priors},
    author={Taoran Yi and Jiemin Fang and Guanjun Wu and Lingxi Xie and Xiaopeng Zhang and Wenyu Liu and Qi Tian and Xinggang Wang},
    journal={arxiv:2310.08529},
    year={2023}
}