armlabstanford/GPIS

Gaussian Process Implicit Surface (GPIS) Implementation for Touch-GS: Visual-Tactile Supervised 3D Gaussian Splatting

🏛️ Dependencies

git clone https://github.com/armlabstanford/GPIS.git
cd GPIS

# create conda environment
conda create -n GPIS python=3.8
conda activate GPIS
conda install pytorch torchvision torchaudio pytorch-cuda=11.8 cudatoolkit=11.8 -c pytorch -c nvidia
pip install -r requirements.txt

💾 Datasets

The dataset used in our paper is available at Google Drive. This dataset includes the combined point cloud and normals from DenseTact for real data. For synthetic data we include the raw depth images rendered from blender. Download and extract the data to the data folder. If you don't have one, make it with mkdir data.

├── data
│   ├── real_bunny
│   │   ├── ... 
│   ├── syn_bunny
│   │   ├── ...

🚀 Usage

Real Data

python real_scene.py {PARAM_FILE}

Syn Data

python syn_scene.py {PARAM_FILE}

For example

python real_scene.py data/real_bunny/params_bunny.json

Data is outputted to output/{depth, var} and will include both GPIS estimated depths and uncertainties.

📡 Citation

@article{swann2024touchgs,
  author    = {Aiden Swann and Matthew Strong and Won Kyung Do and Gadiel Sznaier Camps and Mac Schwager and Monroe Kennedy III},
  title     = {Touch-GS: Visual-Tactile Supervised 3D Gaussian Splatting},
  journal   = {arXiv},
  year      = {2024},
}

armlabstanford / GPIS