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 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 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 — 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
• Stats: 7,952 meshes (1 failed conversion). Face count ranges from 20 to 11.5M (median 18K).

ShapeNetCore v2

• Origin: ShapeNet — 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 — 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 — 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. See the Objaverse license page 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 — 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 — 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

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

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

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

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.

Citation

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

@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:

Source dataset citations

ABO:

@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:

@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:

@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:

@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}
}

Related

• MeshLex-Research — The research project that produced this dataset
• MeshLex-Patches — Pre-segmented patch dataset derived from earlier Objaverse+ShapeNet processing