Define the scene.
Set the target, environment, camera perspective, and approach. Generate RGB sequences for the situations you need to cover.
ORION Data Engine / Synthetic infrared video
Thermal data for a threat
you can’t film.
Build infrared video coverage for the targets, environments, and approach geometries your dataset is missing. Explore generated counter-UAS scenes, translated from RGB into thermal infrared.
Six scenarios. The same scene in two modalities.
Coastal swarms, low passes, industrial backgrounds, and formations under cloud.
Explore the output
One scene. Two modalities.
Every frame aligned.
Choose a scenario, press play, and drag the divider to compare the generated RGB scene with its thermal translation. Then run the detector to see where it fails.
Translated thermal IRGenerated RGB
From scenario to adaptation
Turn a coverage gap
into the next training cycle.
Connect scene generation and sensor translation with ORION’s model testing and automated fine-tuning.
Change the sensor.
Translate the scene into infrared video while preserving its geometry. Compare aligned frames across both modalities.
Expose the gaps.
Evaluate your vision model across the conditions that matter. Use failure analysis to identify where more data or adaptation is needed.
Train for what’s next.
Generate targeted examples, automate fine-tuning, and evaluate again. Let each round of testing guide the next.
Sensor adaptation in action
Explore sensor adaptationThe thermal translation model used for these examples was adapted with maritime imagery, then applied to generated counter-UAS scenes. This demonstrates how sensor adaptation can extend generation to new targets and environments.
Let’s put ORION to work
Bring us your mission.
We’ll show you the workflow.
Walk through your data, model, and operational challenge with the team building ORION and ATLAS.