README.md

---
license: other
license_name: a23d-sample-dataset-license
license_link: LICENSE
task_categories:
- text-to-image
- image-to-image
- text-to-3d
- image-to-3d
language:
- en
tags:
- ai
- 3d
- pbr
- textures
- materials
- physically-based-rendering
- tileable
- seamless-textures
- photorealistic
- computer-graphics
- rendering
- robotics
- embodied-ai
- world-models
- digital-twins
- spatial-ai
size_categories:
- n<1K
configs:
- config_name: default
  drop_labels: true
---


# A23D PBR Materials — Sample for AI/ML Training

A sample from the **A23D** corpus of **100,000+ human-authored PBR materials**, spanning a wide range of real-world material categories. This sample is provided for AI teams to inspect the quality, consistency, and per-map ground truth of A23D materials before licensing the full corpus.

Every material is authored in-house to a single production standard — giving consistency across resolution, channel packing, map conventions, naming, and metadata that open-source and marketplace texture sets do not provide.

## Key highlights

**Human-authored, single production standard.** Every material is created in-house to one specification. No web-scraped, user-generated, or AI-generated content in the corpus.

**Complete, explicit PBR ground truth.** Each material ships a full set of physically based maps with explicit color space, bit depth, channel count, and normal-map convention — the per-map metadata AI training pipelines need, not just flat image files.

**Rich, structured metadata.** Every asset carries a natural-language caption, category hierarchy, material attributes, resolution, tiling and workflow declarations, and a machine-readable list of exactly which maps it ships.

**Consistent at scale.** The same schema, naming and conventions hold across all 100,000+ materials. What you validate on these 25 sets is what you get on the rest.

## What's in the sample

| | |
|---|---|
| Material sets | 25 |
| Images | 268 — 219 maps, 24 previews, 25 thumbnails |
| Texture resolution | 4096 × 4096, seamless, power-of-two |
| PBR workflow | metallic-roughness, DirectX normal convention |
| Captions | 25 natural-language descriptions, 76–121 words each |
| Taxonomy | 10 level-2 categories, 17 level-3 |
| Total size | 2.05 GB |

Categories represented: Wood, Fabric, Metal, Plaster, Brick, Stone, Rock, Tiles, Glass, Wicker.

## Repository structure

```
metadata.jsonl          one row per material, 26 columns — loads directly
metadata/<SKU>.json     full record per material, including per-bitmap detail
maps/<SKU>/             the PBR map stack — Albedo.png, Normal.png, Height.exr, …
previews/<SKU>.png      1920×1080 render, material tiled on a flat plane
thumbnails/<SKU>.png    750×750 render, material on a sphere
```

Paths in `metadata.jsonl` are relative to the repository root, and every path is constructible from the SKU alone. No filename contains a space.

## Map stack

Eight base maps ship with **every** material, so ingestion never has to branch on which channels exist. Additional maps appear where the material calls for them — the transmissive glass set carries IOR and translucency channels.

| Map | Sets | Format | Colour space | Bit depth | Channels |
|---|---|---|---|---|---|
| `Albedo` | 25 | PNG | sRGB | 8 | 3 |
| `Normal` | 25 | PNG | Linear | 8 | 3 |
| `Roughness` | 25 | PNG | Linear | 8 | 1 |
| `Metallic` | 25 | PNG | Linear | 8 | 1 |
| `Ambient Occlusion` | 25 | PNG | Linear | 8 | 1 |
| `ORM` | 25 | PNG | Linear | 8 | 3 |
| `Opacity` | 25 | PNG | Linear | 8 | 1 |
| `Height` | 25 | EXR | Linear | 16 / 32 float | 1 |
| `Specular Level` | 13 | PNG | Linear | 8 | 1 |
| `Specular` | 3 | PNG | Linear | 8 | 1 |
| `IOR` | 1 | PNG | Linear | 8 | 1 |
| `Translucency` | 1 | PNG | Linear | 8 | 1 |
| `Translucency Roughness` | 1 | PNG | Linear | 8 | 1 |

### Conventions

Declared explicitly so results are reproducible and no team has to guess:

- **`ORM`** packs Occlusion, Roughness and Metallic into one RGB image, in that channel order.
- **Colour management** — only `Albedo` is display-referred. Every other map is data and must be sampled linearly; treating them as sRGB produces physically wrong shading.
- **Normal maps** follow the **DirectX** convention, green channel +Y down. Flip green for OpenGL or Blender.
- **`Height`** is single-channel float OpenEXR, stored as displacement — higher value means higher surface. Bit depth is declared per file in each material's record, read from the file itself.
- **Tiling** — every material is seamless and power-of-two, declared per record as `tileable` and `power_of_two`.

## Metadata

`metadata.jsonl` loads directly with `datasets` or `pandas`; `metadata/<SKU>.json` carries the same record plus full per-bitmap technical detail.

| Field | Type | Description |
|---|---|---|
| `sku` | string | Stable asset ID. Primary key across the full corpus |
| `name` | string | Short human-readable title |
| `human_authored` | bool | Provenance attestation — `true` for every asset in the corpus |
| `caption` | string | Natural-language description of the material |
| `category_l1` … `category_l4` | string | Three-level category hierarchy |
| `material_type`, `pattern`, `finish`, `surface_texture`, `effects`, `color`, `condition` | string | Structured material attributes |
| `resolution` | string | Texture resolution |
| `tileable`, `power_of_two` | bool | Tiling guarantees |
| `pbr_workflow` | string | `metallic-roughness` |
| `normal_convention` | string | `DirectX` |
| `maps`, `maps_count` | list, int | Machine-readable declaration of which maps this material ships |
| `map_paths` | list | Paths parallel to `maps` |
| `total_size_mb` | float | Measured size of the map stack |
| `image`, `preview` | image | Thumbnail and preview render, decoded by the viewer |
| `metadata` | string | Path to the full per-material record |

Per-bitmap detail in `metadata/<SKU>.json` gives `colorspace`, `bit_depth`, `channels`, `format` and `resolution` for every individual file.

## Usage

```python
from datasets import load_dataset

ds = load_dataset("A23D/a23d-pbr-materials-sample", split="train")
ds[0]["caption"]    # natural-language description
ds[0]["preview"]    # 1920x1080 preview render
ds[0]["image"]      # 750x750 thumbnail
```

Text–image pairs for contrastive or generative training:

```python
pairs = [(r["caption"], r["preview"]) for r in ds if r["preview"] is not None]
```

The map stack is referenced by path rather than decoded, since each material carries 8–12 maps:

```python
import os
from PIL import Image
from huggingface_hub import snapshot_download

root = snapshot_download("A23D/a23d-pbr-materials-sample", repo_type="dataset")
row = ds[0]
stack = {m: os.path.join(root, p) for m, p in zip(row["maps"], row["map_paths"])}
albedo = Image.open(stack["Albedo"])
```

`Height.exr` requires an OpenEXR-capable reader — `opencv-python` with `OPENCV_IO_ENABLE_OPENEXR=1`, or OpenImageIO. It will not open with Pillow.

## The full corpus

This sample is a small slice of the complete A23D library:

- **100,000+ human-authored PBR materials**
- Broad coverage across architectural surfaces, natural materials, fabrics, metals, wood, stone, ground and terrain, and many more
- Uniform schema and production standard across every material
- Complete provenance — authored by our in-house team
- Bulk delivery, custom curation, and category-targeted subsets

Available for licensing for AI and ML training, including generative model development, and for robotics, embodied AI, world models, digital twins, synthetic data and spatial AI.

**Get in touch:**  
Website: [www.a23d.co/ai](https://www.a23d.co/ai)  
Email: enterprise@a23d.co

## License

This sample is released under the [A23D Sample Dataset License](https://huggingface.co/datasets/A23D/a23d-pbr-materials-sample/blob/main/LICENSE) for evaluation.

You may use it internally to assess the corpus — inspecting, rendering, validating against your pipelines, computing embeddings and benchmarks, and limited-scale fine-tuning to judge suitability. You may not redistribute it, use it commercially, build derivative datasets from it, or deploy or distribute any model trained on it.

Production training rights are granted under a commercial license for the full corpus.

## Citation

```bibtex
@misc{a23d_materials_sample_dataset_2026,
  title        = {A23D PBR Materials Sample Dataset for AI/ML Training},
  author       = {A23D},
  year         = {2026},
  publisher    = {A2VR Technologies LLP},
  url          = {https://huggingface.co/datasets/A23D/a23d-pbr-materials-sample}
}
```

## About A23D

A23D develops enterprise-scale 3D assets and PBR material datasets for artificial intelligence, robotics, simulation and digital content creation — trusted, high-quality, human-authored 3D data for the next generation of AI systems.

*One concept, one vision, one structure.*

A23D is a brand of **A2VR Technologies LLP**.  
© 2026 A2VR Technologies LLP. All rights reserved.