| ### **Standard Installation** |
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| Clone the repository locally |
| ``` |
| git clone https://github.com/lkeab/gaussian-grouping.git |
| cd gaussian-grouping |
| ``` |
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| Our default, provided install method is based on Conda package and environment management: |
| ```bash |
| conda create -n gaussian_grouping python=3.8 -y |
| conda activate gaussian_grouping |
| |
| conda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 cudatoolkit=11.3 -c pytorch |
| pip install plyfile==0.8.1 |
| pip install tqdm scipy wandb opencv-python scikit-learn lpips |
| |
| pip install submodules/diff-gaussian-rasterization |
| pip install submodules/simple-knn |
| ``` |
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| (Optional) If you want to prepare masks on your own dataset, you will also need to prepare [DEVA](https://github.com/hkchengrex/Tracking-Anything-with-DEVA) environment. |
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| ```bash |
| cd Tracking-Anything-with-DEVA |
| pip install -e . |
| bash scripts/download_models.sh # Download the pretrained models |
| |
| git clone https://github.com/hkchengrex/Grounded-Segment-Anything.git |
| cd Grounded-Segment-Anything |
| export AM_I_DOCKER=False |
| export BUILD_WITH_CUDA=True |
| python -m pip install -e segment_anything |
| python -m pip install -e GroundingDINO |
| |
| cd ../.. |
| ``` |
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| (Optional) If you want to inpaint on your own dataset, you will also need to prepare [LaMa](https://github.com/advimman/lama) environment. |
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| ```bash |
| cd lama |
| pip install -r requirements.txt |
| cd .. |
| ``` |