| --- |
| 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. |
|
|
