Synthetic data for real-world perception
Your next model doesn’t need more of the same data. It needs the cases you’re missing. Generate targeted vision datasets for the conditions your drones and robots will actually face.
Target the gaps. Control the conditions. Keep your training workflow.
Start with clear visibility. Then explore what changes.
The cost of missing data
Another collection trip. Another labeling queue. Another blind spot found during testing. Real footage matters—but it can’t give you every combination of weather, distance, and visibility on demand.
Clear-day examples won’t teach every low-light, distant, or partially obscured view. Create targeted variations around the conditions your current dataset underrepresents.
Build coverage around the failure—not another generic dataset.
When evaluation exposes a gap, change the scenario and generate the next set of examples. Explore difficult combinations without coordinating every experiment around field access, crews, and weather.
Spend your next iteration on the model, not the logistics.
Inspect generation plans, reference renders, optional visual QA, and sample provenance. Export the dataset and its records to your existing pipeline, so your team can review what it trains on.
Make synthetic data a reviewable part of your workflow.
From perception gap to targeted data
Simbotfly combines 3D scene generation with generative AI to give you control over the conditions—not a simulation pipeline to build from scratch.
Actual demo-run artifacts. The mask belongs to the reference render.
Describe your recognition task and the conditions that challenge it. Set weather, lighting, camera viewpoint, distance, and visibility.
Create scene variations and inspect the plan, reference renders, and synthesized images. Review optional visual QA before using the results.
Export images, reference masks, and generation records. Train in your own pipeline, evaluate on real footage, and target the next gap.
Built for specialized vision
For teams building task-specific vision systems where the right examples are expensive, seasonal, or difficult to capture.
InfrastructureExplore defects and hazards across viewing angles, distance, and lighting—before the next inspection brings a new surprise.
Utilities · Energy · Infrastructure
AgricultureBuild examples for crops, weeds, obstacles, and changing field conditions without waiting for every season to come around.
Agronomy · Robotics · Growers
LogisticsExplore recognition of packages, pallets, and obstacles across cluttered aisles, partial views, and changing camera positions—without staging every combination on the warehouse floor.
Fulfillment · Mobile robotics · Automation
ManufacturingBuild targeted examples for component recognition and assembly checks across viewing angles, lighting, and occlusion. Evaluate inspection coverage before changing the production setup.
Electronics · Assembly · Machine visionValue you can evaluate
Synthetic images are only useful if they help your task. Start with a focused use case, compare against your existing baseline, and measure performance on held-out real footage.
Discuss a focused pilot
Agree on the recognition task, missing conditions, and evaluation criteria.
Compare your current training data with a mix that includes the synthetic cases.
Look at real-footage performance, dataset creation effort, and the cost of covering the gap.
See the fit for your team
Tell us where your perception system struggles. We’ll walk through the generation workflow and discuss what a focused evaluation could look like for your team.
For robotics, autonomy, and computer vision teams. A specific challenge is all you need to start the conversation.
