Code for "Few-shot Novel View Synthesis using Depth Aware 3D Gaussian Splatting" (ECCV 24 workshops)
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
Update: The diff-gaussian-rasterization module is not linked correctly. You can install the diff-gaussian-rasterization from this repo https://github.com/ashawkey/diff-gaussian-rasterization.
Follow the instruction in the original 3DGS repo.
we use GLPN for depth estimation. Use below script to generate depth data. (change the path variables)
python estimate_depth.py
Preprocessed data can be accessed here. This can be used directly for training.
python train.py -s ./data/fern --scene fern
If you found our work useful for your research, Please cite our paper
@inproceedings{kumar2025few,
title={Few-shot novel view synthesis using depth aware 3d gaussian splatting},
author={Kumar, Raja and Vats, Vanshika},
booktitle={European Conference on Computer Vision Workshop},
pages={1--13},
year={2025},
organization={Springer}
}
The repo uses the code from gaussian-splatting and diff-gaussian-rasterization. We would like to thank the authors for their amazing work.