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# SplatFields: Neural Gaussian Splats for Sparse 3D and 4D Reconstruction
[Project page](https://markomih.github.io/SplatFields/) | [Paper](https://arxiv.org/pdf/XXX.XXX) <br>
This repo contains the official implementation for the paper "SplatFields: Neural Gaussian Splats for Sparse 3D and 4D Reconstruction".
SplatFields regularizes 3D Gaussian Splatting (3DGS) [29] by predicting the splat features and locations via neural fields to improve the reconstruction under unconstrained sparse views.
Our approach effectively handles static and dynamic scenes.
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## Installation
We tested on a server configured with Ubuntu 18.04, cuda 11.6 and gcc 9.4.0. Other similar configurations should also work, but we have not verified each one individually.
### 1. Clone this repo:
```bash
git clone https://github.com/markomih/SplatFields.git
cd SplatFields
```
### 2. Install dependencies
```bash
conda env create --file environment.yml
conda activate SplatFields
# install 3DGS renderer
pip3 install -e git+https://github.com/ingra14m/depth-diff-gaussian-rasterization@f2d8fa9921ea9a6cb9ac1c33a34ebd1b11510657#egg=diff_gaussian_rasterization
pip3 install -e git+https://gitlab.inria.fr/bkerbl/simple-knn.git@44f764299fa305faf6ec5ebd99939e0508331503#egg=simple_knn
pip3 install -e git+https://github.com/open-mmlab/mmgeneration@f6551e1d6ca24121d1f0a954c3b3ac15de6d302e#egg=mmgen
```
## Static Reconstruction
### Blender Dataset
### DTU Dataset
## Dynamic Reconstruction
### Owlii Dataset
## Citation
If you find our work helpful, please consider citing:
```bibtex
@inproceedings{SplatFields,
title={{SplatFields}: SplatFields: Neural Gaussian Splats for Sparse 3D and 4D Reconstruction},
author={Mihajlovic, Marko and Prokudin, Sergey and Tang, Siyu and Maier, Robert and Bogo, Federica and Tung, Tony and Boyer, Edmond},
booktitle={European Conference on Computer Vision (ECCV)},
year={2024},
organization={Springer}
}