README.md

---
license: other
license_name: per-source-dataset-licenses
license_link: https://huggingface.co/datasets/alexmkwizu/gaussian_training_datasets
pretty_name: Gaussian Training Datasets (COLMAP) for msplat
task_categories:
  - image-to-3d
tags:
  - 3d-gaussian-splatting
  - gaussian-splatting
  - nerf
  - colmap
  - apple-silicon
  - msplat
---

# Gaussian Training Datasets (COLMAP) for msplat

COLMAP-format multi-view scenes for training **3D Gaussian Splatting** models,
packaged for **[msplat](https://github.com/SeedeXR/msplat)** — a Metal-native 3DGS
trainer for Apple Silicon. Also includes pre-trained `.ply` splats under
`tested_outputs/`.

> **All scenes are redistributed from third-party datasets. Full credit goes to
> their original authors — see [Licensing & credits](#licensing--credits) and please
> cite the original papers.** This repo only repackages them in COLMAP layout for
> convenience.

## Contents

```
mipnerf360/{bicycle,bonsai,counter,garden,kitchen,room,stump}/   # Mip-NeRF 360
tandt/{train,truck}/                                             # Tanks & Temples
db/{drjohnson,playroom}/                                         # Deep Blending
    └── images/  +  sparse/0/{cameras,images,points3D}.bin       # COLMAP layout

tested_outputs/                # pre-trained 3DGS .ply splats (+ SUMMARY.md, RESULTS.md)
```

## Usage with msplat

```bash
pip install -U "huggingface_hub[cli]"

# Download everything into ./datasets/
hf download alexmkwizu/gaussian_training_datasets --repo-type dataset --local-dir datasets

# Or a single scene
hf download alexmkwizu/gaussian_training_datasets --repo-type dataset \
    --include "tandt/truck/*" --local-dir datasets

# Train (pick -d by native image size: Mip-NeRF 360 ~16 MP -> -d 4; T&T/DB ~1 MP -> -d 1)
msplat datasets/mipnerf360/garden -n 7000 -d 4 --eval
msplat datasets/tandt/truck      -n 7000 -d 1 --eval
```

### Pre-trained splats (`tested_outputs/`)

Standard 3DGS binary PLYs trained with msplat (7000 iters) on an M4 / 16 GB MacBook
Pro. Indoor scenes reach PSNR 27–30. Drag any `.ply` into a web viewer such as
**[SuperSplat](https://superspl.at/editor)** to view. See `tested_outputs/SUMMARY.md`.

## Licensing & credits

This dataset **redistributes** scenes from the following works. Each retains the
license/terms of its original source — consult the original project pages, and if
you use these scenes, **cite the original papers**.

### Mip-NeRF 360 — `mipnerf360/`
Scenes from the Mip-NeRF 360 dataset (Google Research). Project page & terms:
https://jonbarron.info/mipnerf360/

```bibtex
@inproceedings{barron2022mipnerf360,
  title     = {Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields},
  author    = {Barron, Jonathan T. and Mildenhall, Ben and Verbin, Dor and
               Srinivasan, Pratul P. and Hedman, Peter},
  booktitle = {CVPR},
  year      = {2022}
}
```

### Tanks and Temples — `tandt/` (train, truck)
From the Tanks and Temples benchmark (Intel). COLMAP-preprocessed version as
distributed by Inria GRAPHDECO. Project: https://www.tanksandtemples.org/

```bibtex
@article{Knapitsch2017,
  title   = {Tanks and Temples: Benchmarking Large-Scale Scene Reconstruction},
  author  = {Knapitsch, Arno and Park, Jaesik and Zhou, Qian-Yi and Koltun, Vladlen},
  journal = {ACM Transactions on Graphics},
  volume  = {36}, number = {4}, year = {2017}
}
```

### Deep Blending — `db/` (drjohnson, playroom)
From Deep Blending for Free-Viewpoint Image-Based Rendering (UCL / Inria).
COLMAP-preprocessed version as distributed by Inria GRAPHDECO.

```bibtex
@article{hedman2018deep,
  title   = {Deep Blending for Free-Viewpoint Image-Based Rendering},
  author  = {Hedman, Peter and Philip, Julien and Price, True and Frahm, Jan-Michael
             and Drettakis, George and Brostow, Gabriel},
  journal = {ACM Transactions on Graphics (SIGGRAPH Asia)},
  volume  = {37}, number = {6}, year = {2018}
}
```

### COLMAP preprocessing (Tanks & Temples + Deep Blending)
The COLMAP versions of the Tanks & Temples and Deep Blending scenes are those
distributed with the 3D Gaussian Splatting project, Inria GRAPHDECO:
https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/

```bibtex
@article{kerbl3Dgaussians,
  title   = {3D Gaussian Splatting for Real-Time Radiance Field Rendering},
  author  = {Kerbl, Bernhard and Kopanas, Georgios and Leimk{\"u}hler, Thomas and
             Drettakis, George},
  journal = {ACM Transactions on Graphics}, volume = {42}, number = {4}, year = {2023}
}
```

### COLMAP (Structure-from-Motion)
Camera poses / sparse points were produced with COLMAP (Schönberger & Frahm,
CVPR 2016; Schönberger et al., ECCV 2016): https://colmap.github.io/

---

Trained-splat outputs in `tested_outputs/` were generated by
[msplat](https://github.com/SeedeXR/msplat) (Apache-2.0). The input scenes remain
under their original licenses as above.