---
license: other
license_name: mixed-source
license_link: LICENSE
task_categories:
  - text-to-3d
  - image-to-3d
tags:
  - 3d
  - mesh
  - glb
  - geometry
  - objaverse
  - shapenet
  - abo
  - 3d-front
  - meshlex
size_categories:
  - 100K<n<1M
---

# MeshLex-Data-Source

A large-scale collection of **158,588 geometry-only GLB meshes** (281 GB) from four major 3D datasets, unified under a single sharded directory structure. Built as the source data layer for the [MeshLex](https://github.com/Pthahnix/MeshLex-Research) research project, but broadly useful for any 3D mesh generation, reconstruction, or analysis research.

## Overview

| | Files | Size | Categories | Median Faces | Median Vertices |
|---|---:|---:|---:|---:|---:|
| **ABO** | 7,952 | 6.4 GB | — | 18,239 | 10,990 |
| **ShapeNet** | 52,472 | 35.9 GB | 55 | 7,037 | 6,586 |
| **Objaverse** | 45,975 | 155.1 GB | 1,156 | 14,956 | 11,775 |
| **3D-Front** | 52,189 | 84.1 GB | 19,121 | 44,347 | 54,227 |
| **Total** | **158,588** | **281.5 GB** | **20,332** | **18,584** | **17,288** |

All meshes are stored as **geometry-only GLB** files — materials, textures, and non-geometry metadata have been stripped. Each file contains only vertices and faces, loaded via [trimesh](https://trimesh.org/) with `force="mesh"`.

## Directory Structure

```
data-abo/
  00/                      # shard 0: indices 0–9999
    00000-of-07952.glb
    00001-of-07952.glb
    ...
data-shapenet/
  00/                      # shard 0: indices 0–9999
  01/                      # shard 1: indices 10000–19999
  ...
  05/                      # shard 5: indices 50000–52471
data-objaverse/
  00/ ... 04/
data-3d-front/
  00/ ... 05/
```

**Naming convention:** `{index:05d}-of-{total:05d}.glb`

**Sharding:** Files are split into subdirectories of up to 10,000 files each (`shard = index // 10000`) to stay within HuggingFace's per-directory file limit.

**Local flat layout:** When downloaded, the original flat filenames follow the pattern `{source}-{index:05d}-of-{total:05d}.glb` (e.g., `shapenet-00123-of-52472.glb`).

## Data Sources

### Amazon Berkeley Objects (ABO)

- **Origin:** [ABO Dataset](https://amazon-berkeley-objects.s3.amazonaws.com/index.html) — real product 3D models from Amazon catalog listings
- **Processing:** Downloaded GLBs → geometry extraction via trimesh → degenerate mesh filtering (< 4 faces removed)
- **License:** [CC-BY 4.0](https://creativecommons.org/licenses/by/4.0/)
- **Stats:** 7,952 meshes (1 failed conversion). Face count ranges from 20 to 11.5M (median 18K).

### ShapeNetCore v2

- **Origin:** [ShapeNet](https://shapenet.org/) — large-scale 3D model repository organized by WordNet synsets
- **Processing:** OBJ models → trimesh load with `force="mesh"` → geometry-only GLB export
- **License:** [ShapeNet Terms of Use](https://shapenet.org/terms) — research and educational purposes only
- **Stats:** 52,472 meshes across 55 categories. Top categories: table (8,436), chair (6,778), airplane (4,045), car (3,514), sofa (3,173).

### Objaverse-LVIS

- **Origin:** [Objaverse](https://objaverse.allenai.org/) — massive crowd-sourced 3D asset collection, filtered to the LVIS subset (objects with LVIS category annotations)
- **Processing:** Downloaded via `objaverse` Python package → GLB conversion → geometry extraction → degenerate mesh filtering
- **License:** Individual objects carry their own licenses; the majority are [CC-BY 4.0](https://creativecommons.org/licenses/by/4.0/). See the [Objaverse license page](https://objaverse.allenai.org/objaverse-1.0) for details.
- **Stats:** 45,975 meshes across 1,156 LVIS categories. Top categories: chair (453), seashell (370), antenna (174), shield (146), snowman (145).

### 3D-FRONT

- **Origin:** [3D-FRONT](https://tianchi.aliyun.com/specials/promotion/alibaba-3d-scene-dataset) — large-scale indoor scene dataset with professionally designed room layouts and furniture
- **Processing:** Concatenated tar.gz parts → streaming extraction via `tarfile` → per-furniture model deduplication (UUID-based) → geometry-only GLB conversion
- **License:** [3D-FRONT Terms of Use](https://tianchi.aliyun.com/specials/promotion/alibaba-3d-scene-dataset) — academic and research purposes only
- **Stats:** 52,189 unique furniture models deduplicated from scene data, across 19,121 model categories. Top categories: Cabinet (5,041), Sofa (1,928), Lighting (1,795), Chair (1,357).

## Usage

### Quick Start

```python
from huggingface_hub import hf_hub_download
import trimesh

# Download a single mesh
path = hf_hub_download(
    repo_id="Pthahnix/MeshLex-Data-Source",
    filename="data-shapenet/00/00123-of-52472.glb",
    repo_type="dataset",
)
mesh = trimesh.load(path, force="mesh")
print(f"Vertices: {len(mesh.vertices)}, Faces: {len(mesh.faces)}")
```

### Browse by Source

```python
from huggingface_hub import HfApi

api = HfApi()

# List all files under a source directory
files = api.list_repo_tree(
    "Pthahnix/MeshLex-Data-Source",
    path_in_repo="data-objaverse/00",
    repo_type="dataset",
    recursive=True,
)
glb_files = [f.rfilename for f in files if f.rfilename.endswith(".glb")]
print(f"Found {len(glb_files)} GLBs in shard 00")
```

### Bulk Download

```python
from huggingface_hub import snapshot_download

# Download an entire source (e.g., ShapeNet — 35.9 GB)
snapshot_download(
    repo_id="Pthahnix/MeshLex-Data-Source",
    repo_type="dataset",
    allow_patterns="data-shapenet/**",
    local_dir="./meshlex-data",
)
```

### Load and Inspect

```python
import trimesh
from pathlib import Path

data_dir = Path("./meshlex-data/data-shapenet")
for glb in sorted(data_dir.rglob("*.glb"))[:5]:
    mesh = trimesh.load(str(glb), force="mesh")
    print(f"{glb.name}: {len(mesh.faces)} faces, {len(mesh.vertices)} vertices")
```

## Mesh Statistics

### Face Count Distribution

| Source | Min | Median | Mean | Max |
|---|---:|---:|---:|---:|
| ABO | 20 | 18,239 | 42,448 | 11,540,224 |
| ShapeNet | 16 | 7,037 | 30,046 | 4,443,092 |
| Objaverse | 4 | 14,956 | 153,404 | 20,818,039 |
| 3D-Front | 4 | 44,347 | 59,642 | 3,361,058 |

### Vertex Count Distribution

| Source | Min | Median | Mean | Max |
|---|---:|---:|---:|---:|
| ABO | 56 | 10,990 | 24,386 | 5,870,562 |
| ShapeNet | 20 | 6,586 | 26,913 | 6,163,387 |
| Objaverse | 8 | 11,775 | 127,680 | 15,398,448 |
| 3D-Front | 6 | 54,227 | 74,556 | 5,206,898 |

### Category Breakdown (Top 10 across all sources)

| Category | Source | Count |
|---|---|---:|
| table | ShapeNet | 8,436 |
| chair | ShapeNet | 6,778 |
| Cabinet | 3D-Front | 5,041 |
| airplane | ShapeNet | 4,045 |
| car | ShapeNet | 3,514 |
| sofa | ShapeNet | 3,173 |
| Sofa | 3D-Front | 1,928 |
| Lighting | 3D-Front | 1,795 |
| Others | 3D-Front | 1,726 |
| Chair | 3D-Front | 1,357 |

## Processing Pipeline

This dataset was produced by the MeshLex v5.1 pipeline:

1. **Download** raw 3D assets from each source (GLB, OBJ, or tar.gz)
2. **Load** via trimesh with `force="mesh"` to collapse scene graphs into single meshes
3. **Strip** materials, textures, normals, and UV coordinates — retain only vertices and faces
4. **Filter** degenerate meshes (< 4 faces)
5. **Deduplicate** (3D-Front only: UUID-based model deduplication across scenes)
6. **Export** as geometry-only GLB
7. **Upload** in sharded batches to HuggingFace (500 files per commit)

## Limitations

- **Geometry only:** All material, texture, and color information has been removed. These meshes are not suitable for rendering without re-texturing.
- **No decimation applied:** Meshes retain their original polygon counts, which vary widely (4 to 20M faces). Downstream pipelines should apply their own decimation strategy.
- **Mixed quality:** Source datasets have varying levels of mesh quality. Some meshes may be non-manifold, have self-intersections, or contain disconnected components.
- **Category coverage:** ABO meshes lack category labels in this release (marked as "unknown").

## License

This dataset aggregates meshes from multiple sources, each with its own license:

| Source | License | Commercial Use |
|---|---|---|
| ABO | CC-BY 4.0 | Yes |
| ShapeNet | ShapeNet Terms of Use | No (research only) |
| Objaverse | Per-object (mostly CC-BY 4.0) | Varies |
| 3D-Front | 3D-FRONT Terms of Use | No (research only) |

**Important:** Due to ShapeNet and 3D-Front restrictions, this dataset as a whole should be treated as **research and educational use only**. If you need commercial-use data, filter to ABO and Objaverse subsets with compatible licenses.

The processing pipeline code is licensed under [Apache 2.0](https://github.com/Pthahnix/MeshLex-Research/blob/main/LICENSE).

## Citation

If you use this dataset in your research, please cite:

```bibtex
@misc{meshlex-data-source-2026,
  title={MeshLex-Data-Source: A Unified Collection of Geometry-Only 3D Meshes},
  author={Pthahnix},
  year={2026},
  howpublished={\url{https://huggingface.co/datasets/Pthahnix/MeshLex-Data-Source}},
}
```

Please also cite the original datasets:

<details>
<summary>Source dataset citations</summary>

**ABO:**
```bibtex
@inproceedings{collins2022abo,
  title={ABO: Dataset and Benchmarks for Real-World 3D Object Understanding},
  author={Collins, Jasmine and Goel, Shubham and Deng, Kenan and Lutber, Achleshwar and
          Xu, Leon and Gundogdu, Erhan and Zhang, Xi and Vicente, Tomas F. Yago and
          Dideriksen, Thomas and Arber, Himanshu and Metez, Govind and Bikber, Matthew},
  booktitle={CVPR},
  year={2022}
}
```

**ShapeNet:**
```bibtex
@article{chang2015shapenet,
  title={ShapeNet: An Information-Rich 3D Model Repository},
  author={Chang, Angel X. and Funkhouser, Thomas and Guibas, Leonidas and Hanrahan, Pat and
          Huang, Qixing and Li, Zimo and Savarese, Silvio and Savva, Manolis and Song, Shuran and
          Su, Hao and Xiao, Jianxiong and Yi, Li and Yu, Fisher},
  journal={arXiv preprint arXiv:1512.03012},
  year={2015}
}
```

**Objaverse:**
```bibtex
@inproceedings{deitke2023objaverse,
  title={Objaverse: A Universe of Annotated 3D Objects},
  author={Deitke, Matt and Schwenk, Dustin and Salvador, Jordi and Weihs, Luca and
          Michel, Oscar and VanderBilt, Eli and Schmidt, Ludwig and Ehsani, Kiana and
          Kembhavi, Aniruddha and Farhadi, Ali},
  booktitle={CVPR},
  year={2023}
}
```

**3D-FRONT:**
```bibtex
@inproceedings{fu20213dfront,
  title={3D-FRONT: 3D Furnished Rooms with layOuts and fUrNiTure},
  author={Fu, Huan and Cai, Bowen and Gao, Lin and Zhang, Ling-Xiao and Wang, Jiaming and
          Li, Cao and Zeng, Qixun and Sun, Chengyue and Jia, Rongfei and Zhao, Binqiang and
          Zhang, Hao},
  booktitle={ICCV},
  year={2021}
}
```

</details>

## Related

- **[MeshLex-Research](https://github.com/Pthahnix/MeshLex-Research)** — The research project that produced this dataset
- **[MeshLex-Patches](https://huggingface.co/datasets/Pthahnix/MeshLex-Patches)** — Pre-segmented patch dataset derived from earlier Objaverse+ShapeNet processing