SimReady 3D Data Platform for Physical AI
We've built the largest platform and library of SimReady 3D data for Physical AI, thus solving the Real2Sim2Real gap. Trusted customers and partners include Meta, NVIDIA, Google, Mistral, Amazon and many foundational robotics companies.
A new standard for 3D data
As Physical AI, robotics and Generative 3D AI continue to advance, the demand for high-quality, physically grounded 3D data is growing. Existing datasets often lack the geometric accuracy, material fidelity, consistency and physical properties required to represent real-world objects reliably.
ALLSIDES addresses this gap by transforming real-world objects into physically grounded 3D digital twins, combining accurate geometry, measured PBR materials and physical properties at industrial scale.
Experience the quality firsthand. Download our sample 3D assets.
Physically grounded 3D Digital Twins
A growing catalog of real-world objects across hundreds of categories, transformed into physically grounded 3D digital twins. Each asset combines measured geometry, PBR materials and simulation-relevant physical properties.
MEASURED
✓ SUB-MILLIMETER ACCURATE GEOMETRY
✓ FULLY RELIGHTABLE PBR MATERIALS
✓ MASS
✓ COLLISION GEOMETRY
AGENTICALLY DERIVED & VALIDATED
✓ FRICTION COEFFICIENTS
✓ CENTER OF MASS & INERTIA TENSOR
✓ GRASP VECTORS
✓ RESTITUTION
Comparison Table
Existing Solutions | Our Solution | |
| QUALITY | Synthetic data or low-fidelity assets. | Real-world objects captured with sub-millimeter geometric accuracy, measured PBR materials, and SimReady physical properties. |
| LIGHT | Lighting is often baked into textures. | Measured, fully relightable PBR materials for physically consistent rendering under different lighting conditions. |
| GEOMETRY | Low poly with poor surface details and broken topology. | High-fidelity geometry preserving fine surface details, with accurate collision representations for contact-rich robotic manipulation and simulation. |
| METADATA & DIVERSITY | Inconsistent metadata and varying coverage across object categories. | Structured metadata across hundreds of real-world object categories, materials, shapes, and physical properties. |
| PHYSICAL PROPERTIES | Often limited to visual appearance, with missing or approximated physical attributes. | Measured mass, derived and validated friction coefficients, inertia tensors, collision geometry, and restitution. |
The Digital Twin Catalog by Meta
We partnered with Meta to release the Digital Twin Catalog, the most photorealistic open-source 3D dataset to date. Our scanners powered the object capture pipeline behind the dataset.
The Catalog was then introduced and evaluated in a research paper by Stanford University and Meta.
Dataset Benchmark by Stanford and Meta
Meta and Stanford in their Dataset Benchmark paper, ranked our 3D data as the highest quality compared to industry standards such as Google Scanned Objects, ABO and Objaverse.
Download 100 samples of our physically grounded 3D Digital Twins
Explore a curated selection from our 3D Data Platform, captured in our 3D Digital Twin Factory. Fill in the form to get the samples and see the data quality for yourself.
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