DeepWheel is a multi-modal dataset of generatively designed wheels created by KAIST Smart Design Lab and Narnia Labs. It includes rendered images, predicted depth maps, reconstructed 3D meshes, CAD models, and simulation results. The dataset was last updated on June 10, 2026.
Use Cases
- Training generative AI models for wheel design based on rendered images and CAD models.
- Evaluating 3D reconstruction algorithms using predicted depth maps and reconstructed meshes.
- Simulating mechanical performance of generative designs using the included simulation results.
- Benchmarking multi-modal learning systems across image, depth, mesh, and CAD data formats.
Strengths
- Dataset includes multiple complementary data modalities: images, depth maps, 3D meshes, CAD models, and simulation results.
- Data is sourced from a known research institution (KAIST Smart Design Lab) and a collaborating lab (Narnia Labs).
Limitations
- Column-level documentation is absent; field semantics must be inferred after download.
- Row count is unknown, which may limit suitability assessment.
- Description metadata is limited; actual data quality requires manual inspection after download.
Provenance
- Source
- KAIST Smart Design Lab / Narnia Labs
- Freshness
- Last updated 2026-06-10 15:48:56; freshness should be verified.