depth-aware-3DGS

Code for "Few-shot Novel View Synthesis using Depth Aware 3D Gaussian Splatting" (ECCV 24 workshops)

Installations

git clone https://github.com/raja-kumar/depth-aware-3DGS --recursive
conda create -n depth_aware_3dgs python=3.10
conda activate depth_aware_3dgs
conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia
pip install -r requirements.txt
pip install submodules/diff-gaussian-rasterization
pip install submodules/simple-knn

Dataset

Colmap

Follow the instruction in the original 3DGS repo.

depth data

we use GLPN for depth estimation. Use below script to generate depth data. (change the path variables)

python estimate_depth.py

Sample data

Preprocessed data can be accessed here. This can be used directly for training.

Training

python train.py -s ./data/fern --scene fern

Citation

If you found our work useful for your research, Please cite our paper

@article{kumar2024few,
  title={Few-shot Novel View Synthesis using Depth Aware 3D Gaussian Splatting},
  author={Kumar, Raja and Vats, Vanshika},
  journal={arXiv preprint arXiv:2410.11080},
  year={2024}
}

Attributions

The repo uses the code from gaussian-splatting and diff-gaussian-rasterization. We would like to thank the authors for their amazing work.