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 and many foundational robotics companies.
A new standard for 3D data
While generative AI continues to advance, most available training data platforms remain limited — and crucially, not 3D-native. Large-scale collections like Objaverse, Google Scanned Objects, and ShapeNet provide volume but fall short on realism, consistency, and physical accuracy.
ALLSIDES bridges this gap with reality-grade, relightable 3D data purpose-built for machine learning applications. Capturing real-world objects at industrial scale, our dataset doesn’t approximate reality — it preserves it.
Experience it yourself. Download a selection of raw scans, straight from our capture pipeline.
Physically Grounded 3D Digital Twins
A digital archive of physical objects spanning a wide range of categories and materials, providing reality-grade 3D digital twins with measured geometry, measured materials and physical properties for computer vision, AI research, simulation and content creation.
✓ MEASURED MATERIALS
✓ MEASURED GEOMETRY
✓ MASS
✓ FRICTION COEFFICIENTS
✓ COLLIDERS
+ ADVANCED PHYSICS: Articulation and deformation
+ GROUND-TRUTH VIDEOS: For world model and simulation calibration
+ MULTIPLE FORMAT: GLB, USDZ, OBJ
+ STRUCTURED METADATA Material, category, dimensions and physical properties
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.
Comparison Table
Existing Solutions | Our Solution | |
| QUALITY | Synthetic data or low-fidelity assets. | High quality real-world objects: sub-mm geometric accuracy, HD pbr textures. Physically accurate. |
| LIGHT | Lighting is often baked into textures. | All assets are relightable — enabling fully controllable rendering in any scene or lighting condition. |
| GEOMETRY | Low poly with poor surface details and broken topology. | Clean and watertight meshes, with high-fidelity surface. |
| METADATA | Inconsistent metadata. | Objects are tagged with structured metadata (category, size, reference images, physical properties, etc.). |
| DIVERSITY | Limited variety across real world object categories, textures, shapes... | Real consumer goods (food, natural items, accessories, kitchenware...) spanning different shapes, materials, and use cases. |
Dataset Benchmark by Stanford and Meta
Description paragraph: Meta and Stanford in their Dataset Benchmark paper, ranked our 3D data as the highest quality compared to industry standards such as ShapeNet, ABO and Objaverse.
Get a glimpse of our 3D models
We’re actively onboarding select research and enterprise partners. Reach out to discuss licensing, collaboration, or use cases.