---
license: other
license_name: cgaxis-ai-data-eval-license
license_link: https://cgaxis.com/ai-data-license/
pretty_name: CGAxis 3D Models - Free Sample (Furniture / Chairs)
task_categories:
- image-to-3d
- text-to-3d
tags:
- 3d
- 3d-models
- mesh
- glb
- usdz
- text-to-3d
- image-to-3d
- 3d-generation
- 3d-reconstruction
- furniture
- real-world-scale
- croissant
- licensed-data
size_categories:
- n<1K
---

# CGAxis 3D Models - Free Sample (Furniture / Chairs)

A **free, licensed** sample of human-authored 3D models from [CGAxis](https://cgaxis.com), a 3D content studio operating since 2008. This sample is a taster of the full **CGAxis AI Data** corpus (4,390 3D models + 7,794 PBR material sets) available for commercial AI-training licenses.

Every model ships as **GLB and USDZ**, with geometry statistics, **real-world scale in centimetres**, semantic tags, a natural-language caption, per-file SHA-256 and a machine-readable compliance block, plus a dataset-level **MLCommons Croissant** file for direct loading into ML pipelines.

## Why this dataset is different

Most 3D training data is scraped from the open web (objects pulled under contested or unknown licenses, with no one to indemnify you when the model ships). This one is not:

- **Single owner, clean provenance.** Human-authored in-house by CGAxis since 2008. No scraping, no unknown rights, no user-generated content.
- **Real-world scale.** Each model carries its bounding box in centimetres, derived from the source geometry - not normalised-to-unit-cube like most 3D datasets. Essential for physically consistent generation, simulation and robotics.
- **Production-ready formats.** GLB (the ML ingestion standard) **and** USDZ (USD / Apple / simulation pipelines) for every model. Clean UVs.
- **AI-native metadata.** Per-model `metadata.json` (geometry stats, bbox, tags, caption, per-file SHA-256, compliance) plus a loadable `croissant.json` and a deterministic train/test split.

## This sample

| Property | Value |
|---|---|
| Models | 40 (furniture - chairs, dining chairs, armchairs, lounge) |
| Formats | GLB + USDZ (both, every model) |
| With real-world scale (bbox in cm) | 40 / 40 |
| UV-mapped | 40 / 40 |
| Triangles (min / median / max) | 2,732 / 39,544 / 1,256,392 |
| Preview render | 40 / 40 (WebP / JPG) |
| Per-file SHA-256 | 40 / 40 |
| Caption + semantic tags | 40 / 40 |
| Split | 38 train / 2 test (deterministic, seed `cgaxis-3d-v1`) |

## Intended uses

Training and evaluating geometry- and material-aware 3D models: **text-to-3D**, **image-to-3D**, single- and multi-view **3D reconstruction**, mesh/shape generative models, 3D asset retrieval, and (via the baked PBR textures inside each GLB) material-aware 3D generation. The real-world scale also makes the set useful as **licensed assets for simulation / embodied-AI** scenes.

## Load the metadata (Croissant)

```python
import mlcroissant as mlc
ds = mlc.Dataset("croissant.json")
records = list(ds.records(record_set="models"))
```

Or read `models_index.json` directly (full per-model records, including geometry, bbox, tags, caption and the `split` field).

## Structure

```
/                                  # one folder per model
  <model_id>/
    <Model>.glb                    # glTF binary (geometry + baked PBR textures)
    <Model>_USDZ.zip               # USDZ, packaged in a zip
    <model_id>.render.webp         # preview render
    <model_id>.metadata.json       # geometry, bbox_cm, tags, caption, per-file sha256, compliance
croissant.json                     # MLCommons Croissant 1.0 (loadable)
models_index.json / models_index.csv   # all models (JSON has the split field; CSV backs Croissant)
splits.json + train.txt / test.txt     # deterministic train/test split
DATASET_CARD.md                    # plain-text summary
LICENSE.txt                        # this sample's license (evaluation only)
```

## Compliance (EU AI Act)

Every model carries a machine-readable `compliance` block (vendor assertions): `human_authored=true`, `ai_generated=false`, `contains_pii=false`, `contains_trademarks_or_logos=false`, `web_scraped=false`, plus a `training_data_summary` - so training-data documentation is trivial. CGAxis is an EU company.

## License

Free for **internal evaluation and prototyping** under the CGAxis AI Data Free Sample License (see `LICENSE.txt`). This sample is for assessing quality, format and metadata, and building internal proofs of concept - **not** for training models you ship or sell, and it comes **without** warranty or indemnification.

## License the full corpus (4,390 3D models + 7,794 PBR sets)

Production training - with an **IP warranty and full indemnification** - is licensed per Category Pack, Full Corpus or Exclusive tier.

- 3D models: GLB + USDZ, real-world scale in cm, clean UVs, Croissant + per-file SHA-256.
- PBR materials: metallic-roughness and specular-glossiness, GL + DX normals, 16-bit displacement, real-world tile size.
- EU AI Act training-data summary, deterministic train/test splits.

**Request a quote or a larger sample:** [cgaxis.com/ai-training-data](https://cgaxis.com/ai-training-data/)

## Citation

```
@misc{cgaxis_ai_data_3d_2026,
  title  = {CGAxis AI Data: Licensed 3D Models and PBR Materials for AI Training},
  author = {CGAxis (DTS S.C.)},
  year   = {2026},
  url    = {https://cgaxis.com/ai-training-data/}
}
```