| --- |
| license: cc-by-nc-4.0 |
| pretty_name: DeepWheel |
| size_categories: |
| - 1K<n<10K |
| task_categories: |
| - tabular-regression |
| - image-to-3d |
| tags: |
| - engineering-design |
| - automotive |
| - wheel |
| - wheel-design |
| - 3d |
| - mesh |
| - cad |
| - depth-estimation |
| - surrogate-modeling |
| - modal-analysis |
| --- |
| |
| <div align="center"> |
| <img src="assets/banner.png" alt="DeepWheel — generated 3D wheel designs" width="100%"> |
| </div> |
|
|
| <h1 align="center">DeepWheel</h1> |
| <p align="center"><b>Generating a 3D Synthetic Wheel Dataset for Design and Performance Evaluation</b></p> |
|
|
| <p align="center"> |
| <a href="https://doi.org/10.1115/1.4069899"><img src="https://img.shields.io/badge/J.%20Mech.%20Des.-10.1115%2F1.4069899-2ea44f.svg" alt="Journal of Mechanical Design"></a> |
| <a href="https://arxiv.org/abs/2504.11347"><img src="https://img.shields.io/badge/arXiv-2504.11347-b31b1b.svg" alt="arXiv"></a> |
| <img src="https://img.shields.io/badge/license-CC--BY--NC%204.0-blue.svg" alt="License: CC-BY-NC 4.0"> |
| </p> |
|
|
| <p align="center"><b>Published in ASME Journal of Mechanical Design</b> (2026) 148(5): 051702 · KAIST SmartDesignLab / Narnia Labs</p> |
|
|
| --- |
|
|
| ## Contents |
| - [Overview](#overview) |
| - [The data, qualitatively](#the-data-qualitatively) |
| - [Dataset structure](#dataset-structure) |
| - [Usage](#usage) |
| - [Applications](#applications) |
| - [Citation](#citation) |
| - [License](#license) |
|
|
| --- |
|
|
| ## Overview |
|
|
| > **DeepWheel** is a **synthetic automotive wheel** dataset for **design and performance evaluation**, |
| > generated with a generative-AI framework: 2D renders are synthesized with **Stable Diffusion**, lifted to |
| > 3D via **2.5D depth estimation + reconstruction**, and **structurally simulated** to extract engineering |
| > performance. It provides **6,000+ photo-realistic images** and **900+ structurally-analyzed 3D models**, |
| > each as multi-view images, depth maps, a reconstructed surface mesh, and a parametric CAD model, paired with |
| > **performance labels** (mass and modal natural frequencies) — coupling **3D geometry ↔ performance** for |
| > data-driven design, surrogate modelling, and image-to-3D research. |
|
|
| <div align="center"> |
| <img src="assets/teaser.gif" alt="A generated wheel spinning about its axle" width="42%"> |
| <br><sub>A reconstructed wheel design, spinning about its axle.</sub> |
| </div> |
|
|
| | | | |
| |---|---| |
| | **Domain** | automotive wheels (rims) | |
| | **Scale** | 6,000+ rendered images · 900+ structurally-analyzed 3D models | |
| | **Modalities** | rendered images · depth maps · 3D meshes · CAD models | |
| | **Performance labels** | mass · modal frequencies (Mode 7, Mode 11) | |
| | **Paper** | *J. Mech. Des.* (2026) 148(5):051702 · [doi:10.1115/1.4069899](https://doi.org/10.1115/1.4069899) · [arXiv:2504.11347](https://arxiv.org/abs/2504.11347) | |
|
|
| --- |
|
|
| ## The data, qualitatively |
|
|
| <div align="center"> |
| <img src="assets/banner.png" alt="Variety of generated wheel designs" width="100%"> |
| <br><sub><b>Generated wheel-design variety</b> — reconstructed 3D rim meshes spanning a range of spoke geometries.</sub> |
| </div> |
|
|
| --- |
|
|
| ## Dataset structure |
|
|
| ``` |
| DeepWheel/ |
| ├── 1_rendered_images.zip # multi-view renders of each design |
| ├── 2_predicted_depth_maps.zip # predicted depth maps |
| ├── 3_3D_recon_meshes.zip # reconstructed surface meshes (.stl) |
| ├── 4_3D_cad_models.zip # parametric CAD models |
| ├── deepwheel_sim_results.csv # performance labels (per design) |
| └── readme.docx # official documentation (incl. file-matching instructions) |
| ``` |
|
|
| **Modalities** |
|
|
| | Modality | File | Content | |
| |---|---|---| |
| | Rendered images | `1_rendered_images/…` | multi-view RGB renders | |
| | Depth maps | `2_predicted_depth_maps/…` | predicted per-view depth | |
| | 3D meshes | `3_3D_recon_meshes/*.stl` | reconstructed surface meshes | |
| | CAD models | `4_3D_cad_models/…` | parametric CAD geometry | |
| | Performance labels | `deepwheel_sim_results.csv` | `file_name, Mass, Mode7 Freq, Mode11 Freq` | |
|
|
| --- |
|
|
| ## Usage |
|
|
| ```bash |
| huggingface-cli download KAIST-SmartDesignLab/DeepWheel --repo-type dataset --local-dir DeepWheel |
| cd DeepWheel && for f in *.zip; do unzip -q "$f"; done |
| ``` |
|
|
| ```python |
| import pandas as pd, trimesh |
| labels = pd.read_csv("deepwheel_sim_results.csv").set_index("file_name") |
| case = labels.index[0] |
| mesh = trimesh.load(f"stl/{case}.stl") # reconstructed wheel |
| mass, f7, f11 = labels.loc[case, ["Mass", "Mode7 Freq", "Mode11 Freq"]] |
| ``` |
|
|
| --- |
|
|
| ## Applications |
|
|
| - **Surrogate modelling** — predict mass and modal frequencies from 3D geometry or rendered views. |
| - **Image-to-3D** — reconstruct wheel geometry from images / depth (paired renders + meshes + CAD). |
| - **Generative & inverse design** — generate manufacturable wheel rims with target performance. |
| - **Multimodal learning** — images ↔ depth ↔ mesh ↔ CAD ↔ performance. |
|
|
| --- |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{deepwheel2026, |
| title = {DeepWheel: Generating a 3D Synthetic Wheel Dataset for Design and Performance Evaluation}, |
| journal = {Journal of Mechanical Design}, |
| volume = {148}, |
| number = {5}, |
| pages = {051702}, |
| year = {2026}, |
| doi = {10.1115/1.4069899} |
| } |
| ``` |
|
|
| --- |
|
|
| ## License |
|
|
| Released under **CC BY-NC 4.0** (Creative Commons Attribution-NonCommercial 4.0 International). Use, |
| modification, and redistribution are permitted for **non-commercial** purposes with attribution; |
| commercial use of the dataset or derivative models is prohibited. See `readme.docx` and the paper |
| ([doi:10.1115/1.4069899](https://doi.org/10.1115/1.4069899)) for details. DeepWheel — KAIST Smart Design Lab / Narnia Labs. |
|
|
