Royalvice/DirectLStudio-Demo-Scenes

DirectLStudio Demo Scenes

This public dataset contains three trained 3D Gaussian Splatting scenes in the runtime-ready SOG format used by DirectLStudio. It contains inference assets only: no source photographs, training images, camera training sets, checkpoints, optimizer state, or training code are included.

Files

Scene	Runtime file	Size	SHA-256	Terms
Lego	scenes/lego/scene.sog	5,241,797 bytes	1628DE34FA07E01960E4CD178BE7A67504C8B14A7FF46C0E396B01256EBB2756	CC BY-NC 4.0
Garden	scenes/garden/scene.sog	82,435,453 bytes	2F8DDF2D7AC131584D3AD9144C56AE6BB0D65C065D3E47EAA328636C61401910	CC BY 4.0
Bicycle	scenes/bicycle/scene.sog	82,698,972 bytes	94FEDED46A825E5DEA6C7B8D49A68DBFE9445E41947322A16E07F718F91C38AD	CC BY 4.0

The machine-readable counterpart is manifest.json. The mixed per-file terms are authoritative in LICENSES.md; the repository-level license: other metadata intentionally does not flatten them into a single license.

Download

Install the Hugging Face CLI, then download one scene:

hf download Royalvice/DirectLStudio-Demo-Scenes `
  --repo-type dataset `
  --include "scenes/lego/*" `
  --local-dir .\DirectLStudio-Demo-Scenes

DirectLStudio also provides a hash-pinned helper:

python .\tools\download_assets.py --scene lego --output .\assets\scenes
python .\tools\download_assets.py --scene garden --output .\assets\scenes
python .\tools\download_assets.py --scene bicycle --output .\assets\scenes

Provenance

• Lego derives from the NeRF Synthetic Lego scene published with NeRF. A trained 3DGS representation was converted to SOG for DirectLStudio inference. The source asset attribution and non-commercial restriction remain in force after training and format conversion.
• Garden and Bicycle derive from the Mip-NeRF 360 dataset. Their trained 3DGS representations were converted to SOG for DirectLStudio inference.

These runtime files are provided for renderer evaluation and demonstration. They do not transfer rights beyond the terms listed in LICENSES.md.

Citations

@inproceedings{mildenhall2020nerf,
  title={NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis},
  author={Mildenhall, Ben and Srinivasan, Pratul P. and Tancik, Matthew and Barron, Jonathan T. and Ramamoorthi, Ravi and Ng, Ren},
  booktitle={ECCV},
  year={2020}
}

@article{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},
  journal={CVPR},
  year={2022}
}