A23D/a23d-3d-models-sample

A23D 3D Models — Sample for AI/ML Training

A sample from the A23D corpus of 400,000+ human-authored 3D models. This sample is provided for AI teams to inspect the geometry, material quality, and metadata consistency of A23D assets before licensing the full corpus.

Every model is authored in-house to a single production standard — giving consistency across topology, scale, material setup, naming, and metadata that open-source and marketplace asset sets do not provide.

Key highlights

Complete provenance. Every asset is professionally authored and owned by A23D. No web scraping, marketplace aggregation, or AI-generated content.

Single production standard. Every asset follows the same production pipeline, with consistent topology, scale, materials, naming conventions, and metadata across the corpus.

Rich structured metadata. Every model includes hierarchical categories, natural-language captions, product attributes, physical dimensions, and measured geometry statistics.

Component-level semantics. Materials are linked to the geometry they describe, providing structured information about how each object is constructed, not just how it looks.

Physically based materials. Production-quality PBR materials with structured texture data support physically accurate rendering, simulation, and downstream AI workflows.

Enterprise-ready licensing. Commercial licensing, custom dataset curation, and flexible delivery for foundation models, robotics, embodied AI, world models, digital twins, and simulation.

What's in the sample

Models	25
Format	GLB (glTF 2.0), single file per model, textures embedded
Texture resolution	4096 × 4096, metallic-roughness workflow
Scale	Metres, Y-up, bounding box published per model
Preview renders	25 × 750 × 750 PNG
Captions	25 natural-language descriptions, 93–112 words each
Taxonomy	10 level-2 categories, 21 level-3
Total size	2.54 GB

Categories represented: Animals & People, Army & Weapons, Building Elements, Foliage & Landscaping, Furniture, Kitchen & Food, Lighting Fixtures, Structures & Roadside, Toilet, Transport.

Repository structure

metadata.jsonl          one row per model, 40 columns — loads directly
metadata/<SKU>.json     full record per model, grouped into blocks
models/<SKU>.glb        the model — glTF 2.0 binary, textures embedded
previews/<SKU>.png      750 × 750 studio render on a transparent background

Paths in metadata.jsonl are relative to the repository root, and every path is constructible from the SKU alone.

Models in this sample

SKU	Name	Category	Triangles	Dimensions (m)	Materials	Size (MB)
A23DMOD001858A	Wooden Table Chair Set	Furniture	105,600	1.84 × 0.97 × 1.85	4	69.8
A23DMOD006097A	Panel Door	Building Elements	18,186	1.01 × 2.09 × 0.17	3	7.3
A23DMOD009439A	Gazebo	Structures & Roadside	109,972	4.75 × 3.85 × 4.12	8	217.4
A23DMOD012703A	Sports Car	Transport	1,764,221	1.89 × 1.41 × 4.15	28	166.4
A23DMOD013625A	Elegant Hanging Light	Lighting Fixtures	19,600	1.06 × 1.93 × 1.00	4	46.5
A23DMOD015025A	Double Bed	Furniture	643,421	3.55 × 1.15 × 2.55	11	367.2
A23DMOD017743A	Upholstered Wooden Armchair	Furniture	610,382	2.17 × 0.95 × 1.21	4	151.2
A23DMOD019821A	Ornamental Houseplants	Foliage & Landscaping	1,829,416	1.04 × 0.90 × 0.85	10	270.9
A23DMOD019825A	Palm Plant	Foliage & Landscaping	467,985	1.88 × 2.31 × 1.65	6	143.1
A23DMOD020802A	Wooden Bunk Bed	Furniture	167,596	2.03 × 1.72 × 0.88	5	105.0
A23DMOD037547A	Apple Rack	Kitchen & Food	99,108	0.45 × 0.61 × 0.45	4	34.6
A23DMOD042822A	Bicycle	Transport	133,660	0.80 × 1.21 × 2.05	8	53.0
A23DMOD042855A	Arched Garden Footbridge	Foliage & Landscaping	104,858	2.39 × 0.89 × 0.89	2	93.0
A23DMOD048998A	Domestic Cat	Animals & People	63,528	0.99 × 0.68 × 0.98	1	26.0
A23DMOD063106A	Wooden Desk Lamp	Lighting Fixtures	99,232	0.63 × 0.77 × 0.61	5	31.3
A23DMOD064450A	Washbasin Cabinet	Toilet	36,800	2.06 × 2.07 × 0.52	12	46.7
A23DMOD075483A	Wooden Windsor Armchair	Furniture	59,932	0.59 × 1.02 × 0.54	1	30.5
A23DMOD088058A	Upholstered Dining Armchair	Furniture	105,176	0.61 × 0.85 × 0.60	3	27.3
A23DMOD088680A	Two-Handle Basin Mixer	Toilet	43,952	0.31 × 0.21 × 0.18	3	37.0
A23DMOD116229A	Writing Desk	Furniture	19,176	1.30 × 0.75 × 0.60	3	25.0
A23DMOD117389A	Hanging Utensil Rack	Kitchen & Food	44,616	0.76 × 0.48 × 0.10	3	4.2
A23DMOD120766A	Armoured Fantasy Warrior	Animals & People	8,999	0.81 × 1.72 × 0.47	4	67.6
A23DMOD137661A	Buttoned Tub Armchair	Furniture	114,602	0.85 × 0.78 × 0.80	3	71.0
A23DMOD196237A	Scoped Revolver	Army & Weapons	659,202	0.04 × 0.18 × 0.31	9	158.2
A23DMOD208693A	Village House	Structures & Roadside	9,796	3.86 × 5.77 × 4.15	8	160.2

Metadata

metadata.jsonl loads directly with datasets or pandas. One flat row per model, in this order:

Field	Type	Description
image	image	Preview render, decoded by the viewer
sku	string	Stable asset ID. Primary key across the full corpus
name	string	Short human-readable title
category_l1 … category_l4	string	Category hierarchy, deepest level varies by asset
attr_material, attr_type, attr_shape, attr_size, attr_back, attr_side, attr_storage, attr_usage	list	Structured product attributes. Every one is a list, since a model can carry more than one value — empty where the catalogue records nothing
caption	string	Natural-language description of the model
human_authored	bool	Provenance attestation — true for every asset in the corpus
format, gltf_version, up_axis, units	string	Format and coordinate declarations
width_m, height_m, depth_m	float	World-space bounding box in metres
triangles, vertices, polygons, objects, meshes, uv_sets	int	Measured geometry counts
has_animations, has_skeleton	bool	false throughout this sample
materials_count, pbr_workflow	int, string	Material count and shading model
textures_count, texture_resolution, textures_embedded	int, string, bool	Texture count, resolution and packaging
total_size_mb	float	Measured size of the GLB
model_path	string	Path to the GLB
metadata	string	Path to the full per-model record

metadata/<SKU>.json carries the same facts grouped rather than flattened — category, attributes, geometry, materials, textures and files — so each block holds one kind of fact. image arrives as file_name in the raw file and is decoded on load.

Usage

from datasets import load_dataset

ds = load_dataset("A23D/a23d-3d-models-sample", split="train")
ds[0]["caption"]    # natural-language description
ds[0]["image"]      # 750x750 preview render

Text–image pairs for contrastive or generative training:

pairs = [(r["caption"], r["image"]) for r in ds]

The GLB is referenced by path:

import os
import trimesh
from huggingface_hub import snapshot_download

root = snapshot_download("A23D/a23d-3d-models-sample", repo_type="dataset")
scene = trimesh.load(os.path.join(root, ds[0]["model_path"]))
scene.extents       # matches width_m / height_m / depth_m

pygltflib, trimesh, Blender, Unreal, Unity, Omniverse and three.js all read these files directly. The Hub's dataset viewer decodes the preview image only; GLB files are downloaded, not previewed inline.

The full corpus

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

• 400,000+ professionally authored 3D assets
• Broad coverage across architecture, furniture, consumer products, transportation, vegetation, characters, and more
• Consistent topology, metadata, naming conventions, and production standards
• Complete provenance through A23D's in-house production pipeline
• Flexible delivery in customer-required formats and specifications
• Bulk licensing, custom curation, and category-specific collections

Available for licensing for AI and machine learning applications, including foundation models, generative 3D, robotics, embodied AI, world models, digital twins, simulation, synthetic data generation, spatial AI, and other enterprise use cases.

Get in touch:
Website: www.a23d.co/ai
Email: enterprise@a23d.co

License

This sample is released under the A23D Sample Dataset 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

@misc{a23d_models_sample_dataset_2026,
  title        = {A23D 3D Models Sample Dataset for AI/ML Training},
  author       = {A23D},
  year         = {2026},
  publisher    = {A2VR Technologies LLP},
  url          = {https://huggingface.co/datasets/A23D/a23d-3d-models-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.