3D to
digital twin pipeline
Factory and warehouse digital twins need every object to behave physically, conveyors move packages, forklifts carry loads, robots pick items. Rigyd automates the physics layer so your twin actually simulates operations, not just renders geometry.
The problem
Why existing workflows fall short.
Visual twins aren't operational twins
Importing BIM or 3D CAD into a game engine gives you a pretty render, not a physics simulation. Every object still needs mass, friction, and collision data before it can simulate process flows.
Thousands of objects = thousands of manual steps
A mid-sized warehouse twin has 10,000+ unique objects. At 4 engineer-hours per object for physics setup, that's 40,000 hours before the twin even runs its first simulation.
Live updates are impractical
Real facilities change daily, new racking, new SKUs, new equipment. If the twin takes months to build and more months to update, it's already stale before it's deployed.
How Rigyd helps
AI-native infrastructure that automates the hard parts.
BIM/CAD to SimReady OpenUSD
Rigyd ingests standard 3D formats (.glb, .fbx, .obj, IFC-derived geometry) and emits OpenUSD with full physics schemas, ready for Omniverse, Unity, Unreal, and Isaac Sim digital twin platforms.
API-driven continuous sync
Enterprise API plugs into your asset pipeline. New or modified objects get physics annotations automatically as they enter the twin, keeping the simulation aligned with the real facility.
Validated at digital-twin scale
Rigyd has processed multi-thousand-object catalogs for real warehouse and factory twins. Physics values are calibrated for the material distributions these environments actually contain.
unique objects in a typical warehouse digital twin
saved per 1,000-object twin build
cost reduction vs manual physics annotation
Turn your 3D models into operational digital twins
Upload CAD or BIM assets and get a physics-enabled digital twin foundation in days, not months.
Starts at $29/month. 30 credits included.
Frequently asked questions
What's the difference between a visual twin and an operational twin?
A visual twin renders geometry; an operational twin simulates physics. Rigyd adds the physics layer (mass, collision, friction) that lets your twin simulate conveyor movement, forklift loads, and robot picking, not just display them.
Can Rigyd convert BIM and CAD models at warehouse scale?
Yes. Enterprise API has processed 10,000+ unique objects for warehouse and factory twins. Each asset emerges as physics-enabled OpenUSD, ready for Omniverse, Unity, Unreal, or Isaac Sim twin platforms.
How do digital twins stay in sync with real facilities?
API-driven continuous sync. New or modified objects get physics annotations as they enter your asset pipeline, keeping simulation aligned with the physical facility without manual updates.
Does Rigyd integrate with BIM systems like Revit and Navisworks?
Rigyd ingests standard 3D formats (.glb, .fbx, .obj, IFC-derived meshes), which BIM tools like Revit and Navisworks export to natively. Most digital-twin programs run Revit → IFC → glTF → Rigyd as the standard pipeline, with per-object physics annotation happening after the BIM-to-mesh conversion. Direct IFC ingestion is on the roadmap for Enterprise customers with high-frequency BIM updates.
What's the typical time-to-deployment for a 10,000-object digital twin with Rigyd?
Roughly 4-8 weeks for a 10,000-SKU twin, compared to 12-24 months with fully manual SimReady authoring. Bottlenecks shift from per-object physics labor to upstream CAD/BIM ingestion and simulator integration. The compute itself is fast, automated processing runs the entire catalog in days, parallelizable. Most program time goes to validation, integration with existing simulators, and stakeholder iteration.
Related reading
In-depth guides on robotics simulation, OpenUSD, and SimReady assets.
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Digital twin creation pipeline for manufacturing
A factory digital twin needs every object to behave physically, not just render. This is the end-to-end pipeline: CAD intake, BIM merge, physics layer, semantic labeling, simulation runtime, at the asset volumes (10K+ unique SKUs) real factories actually contain.
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How to set up mass, friction, and joint properties for robot training
The three pillars of robot physics setup, mass, friction, joints, determine whether your trained policy transfers to real hardware. Here's the calibration target for each, the schemas, and the common mistakes that quietly break training.
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