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The project structure follows the origianl 3DGS repository.
### Blender Dataset
To run SplatFields on the Blender dataset, download the origianl [NeRF synthetic dataset](https://drive.google.com/drive/folders/128yBriW1IG_3NJ5Rp7APSTZsJqdJdfc1) and follow the instructions in the `run_blender.sh` script.
Make sure that the `DATASET_ROOT` variable is set to the directory where the Blender dataset is downloaded.
### DTU Dataset
To run our method on the DTU dataset, you could directly download the pre-processed subset released in the [2DGS repo](https://drive.google.com/drive/folders/1SJFgt8qhQomHX55Q4xSvYE2C6-8tFll9).
We provide the bash script `run_dtu.sh` to run SplatFields on the DTU sequences. Update the `DATASET_ROOT` in the script to the path of the downloaded dataset.
## Dynamic Reconstruction
SplatFields straightforwardly extends to dynamic scenes.
### Owlii Dataset
The pre-processed Owlii dataset for the dynamic sparse view reconstruction is available [here](https://drive.google.com/file/d/1OdqwXKmvnpxFI4LC8ckI0eV6r9EZ64ZV/view?usp=sharing)(or download it via `gdown 1OdqwXKmvnpxFI4LC8ckI0eV6r9EZ64ZV && unzip DATA_OWLII`). Then, run the script `run_owlii.sh` to train our model on varying configurations (set the data directory appropriately).
## 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},
title={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)},