README: dataset credits/citations + msplat usage

README.md

@@ -1,3 +1,132 @@
 ---
-license: mit
+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.