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e2c3d21 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 | ### **Standard Installation**
Clone the repository locally
```
git clone https://github.com/lkeab/gaussian-grouping.git
cd gaussian-grouping
```
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
```
(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.
```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 ../..
```
(Optional) If you want to inpaint on your own dataset, you will also need to prepare [LaMa](https://github.com/advimman/lama) environment.
```bash
cd lama
pip install -r requirements.txt
cd ..
``` |