From evidence to a usable twin
Three paths through camera reconstruction, the work of turning it into a usable visual twin, and the Omniverse platform changes behind interactive OpenUSD applications.
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Each path has its own starting point and sequence.
From camera evidence to reconstruction · 3 pieces
How I chose what kind of truth to preserve, built the reconstruction workflow, and learned what 24 Gaussian-splat jobs could and could not prove.
Start with What Kind of Truth Does Your Digital Twin Need?From reconstruction to a visual twin · 3 pieces
Where a convincing Gaussian reconstruction stops, how I built an editable OpenUSD workflow around it, and what the measured Garden run actually established.
Start with A Gaussian Splat Is Not Yet a Digital TwinFrom Omniverse platform to browser application · 3 pieces
The industrial platform model, the move from Kit-first applications to libraries, and the browser viewer I built to test that approach.
Start with Omniverse Is Not a Rendering ToolMore in Simulation & Digital Twins
3 pieces not currently in a reading path.
A Gaussian Splat Is Not Yet a Digital Twin
A Gaussian splat can reproduce a place convincingly while knowing almost nothing about its identity, scale or behavior. This article separates reconstruction, visual readiness, operational readiness and simulation readiness—and defines the boundary SplatStage is designed to cross.
Building SplatStage: From Edited Gaussians to OpenUSD
SplatStage turns a Gaussian reconstruction into a versioned editing workflow, exports the selected PLY as an OpenUSD Gaussian ParticleField, and composes it with replaceable engineered assets. The hardest bug was at the seam between editing and export.
What 5.8 Million Gaussians Taught Me About Building a Visual Twin
One Garden scene made the promises and gaps in SplatStage measurable: 2.07 million Gaussians removed, an edited OpenUSD particle field, three composed assets, a dependency-complete stage—and no measured scale, colliders or external runtime proof.
What Kind of Truth Does Your Digital Twin Need?
Every digital twin needs a usable digital starting point. CAD, LiDAR, photogrammetry and Gaussian splatting preserve different kinds of truth, so the representation should follow the decision. Part 1 of a three-part series.
From Camera Media to Gaussian Splats: Building ReconStudio
ReconStudio turns camera media into Gaussian splats and OpenUSD through a browser and job API. I built it to expose reconstruction stages, retain experiment evidence and test where on-demand GPU execution fits. Part 2 of a three-part series.
What I Learned from 24 Gaussian Splat Jobs
I built ReconStudio, then used 24 job records across six scenes to test its evidence. Metric bugs, repeated runs and paired-frame comparisons changed what I could claim about training, capture and GPU capacity. Part 3 of a three-part series.
NVIDIA's Hardware Line Looks Like a Catalog. It's One Architecture in Many Boxes.
People keep asking me a version of the same question: what NVIDIA hardware should I be thinking about for a digital twin, from a developer build at my desk all the way to production? I always had an answer but no clean model behind it, just a pile of product names, three of which are confusingly all called DGX. So I built the model I wanted: NVIDIA develops one architecture family a year and expresses it across many kinds of computer, from a gaming card to a liquid-cooled rack. These are my rough notes, in case you're coming at it from the software side and asking the same thing.
Testing the Robot Training Loop I Drew
A month ago I drew a five-stage loop for a robot training center and admitted most of it was an educated guess I hadn't tested. So I built the loop as a real, orchestrated pipeline on one GPU and carried two tasks around it — a pole that balances by trial and error, and a Franka arm that learns to stack cubes by copying demonstrations. This is the environment, the stack, the architecture, and what actually ran. Part 1 of two.
I Built a Live USD Viewer That Never Launches Kit
A browser-based USD viewer, RTX-rendered server-side and streamed over WebRTC, built to see if the library-first pitch holds up under real interactivity.
You No Longer Need to Build an App to Use Omniverse
Kit and libraries used to be the same choice for building on Omniverse. They're not anymore, and that's a bigger change than it looks, with a real before/after from a Kit extension I built.
The Omniverse Licensing Change You Might Have Missed
Since May 2026, Omniverse is free for production, not just development. It's a clean, sensible change, but it landed quietly, and I keep meeting customers and partners who don't know it happened yet. The paid-production requirement is gone; the license and optional enterprise support remain.
Omniverse Is Not a Rendering Tool
From the industrial systems I know, rendering is only one part of Omniverse. The harder job is connecting source data, giving teams a shared context, and reusing it for visibility, simulation, optimization and Physical AI.