Synthetic data for maritime autonomy
All-weather, all-sea-state perception data for autonomous vessels, USVs, and coastal surveillance. Generate the rare and dangerous scenarios you can never safely collect at sea, labeled to the pixel across EO, IR, and SWIR.
Working model in one week. Zero sea trials.
The data that matters most is the data you cannot safely collect
Maritime AI fails for one reason above all: bad data. Real collection at sea is slow, costly, and weather-dependent, and it skews to calm, common, benign conditions. The cases that decide a mission are exactly the ones you cannot stage: heavy sea states, night, fog, glint, crowded ports, fast inbound small craft.
Stardust generates them on demand. Built on Unreal Engine and a learned rendering stack, it produces photorealistic, fully labeled maritime scenes across the full operational envelope, with EO, IR, and SWIR in registration, and physics-based marine radar in development. Closed-loop simulation then lets your autonomy stack act in the scene and be scored against ground truth, so perception and planning are validated before the first sea trial.
Why real data falls short
// collecting at sea today
- ✕Collecting labeled data at sea is slow, costly, and weather-dependent
- ✕The dangerous cases are exactly the ones you cannot stage: heavy seas, night, fog, glint, near-collisions
- ✕EO alone misses targets that IR and SWIR would catch
- ✕Open-loop data cannot validate autonomy: without closed-loop control, perception and planning failures stay hidden until deployment
Photorealistic data, perfectly labeled
EO, IR, and SWIR in perfect registration, matched to your fusion stack. Physics-based marine radar in development.
Inside the maritime data
Explore a real maritime scene, frame by frame
Every Stardust scene exports to Rerun. Pan and zoom the 3D world, scrub the timeline, and toggle sensor streams in registration. Live, interactive, real Stardust output.
Interactive viewer · loads a live recording from Rerun
What teams build with it
Speaks your domain
The vocabulary, sensors, and benchmarks maritime teams actually use.
“We hit great real-world performance almost immediately. But even more impressive, Bifrost’s 3D metadata let us develop AI capabilities that just are not possible with real data.”
Perception lead, defense technology company valued over $30B
Trusted by the teams building autonomy on the water.
Questions teams ask
How do you train a USV to avoid collisions without sea trials?
Generate thousands of labeled collision and COLREGS scenarios across sea states, weather, and traffic, then close the loop so the autonomy stack acts and is scored against ground truth.
What sensors do you need for maritime ATR?
EO/RGB, thermal IR, and marine radar. Stardust generates EO and IR today with pixel-perfect labels and 3D metadata, with physics-based marine radar in development.
Can you simulate marine radar?
Marine radar is in active development, in partnership with Ansys. It will support configurable carrier frequency, beamwidth, polarization, and antenna pattern for radar system validation.
How do you cover the long tail at sea?
Find the gaps and biases in your real data, then generate targeted synthetic data patches to fill them, around 100x faster than collecting more at sea.
What is closed-loop maritime simulation?
A step-through simulation where your autonomy stack observes synthetic sensor frames and issues controls each timestep, so perception and planning can be regression-tested in CI.
Validate maritime autonomy before the first sea trial
Tell us what you are building and the scenarios you need. We will get you access.