Challenges of failing Computer Vision System projects

Inadequate hardware
Inadequate Hardware
Time / rare defects
Time / Rare defects
Poor data quality/amount
Poor data (quality/amount)
Goal vs ROI
Goal VS ROI
Related cost
Related cost

Game changer, Synigen

Underlying magic

What makes it work

Photorealistic rendering

Realistic rendering

Controllable optics, lighting & materials for lifelike data.

Procedural defects

Auto defect integration

Add 2D/3D defects manually or procedurally (here: e.g. blisters).

AI module

AI module / Annotation

Run inference inside Synigen to shorten fine-tuning loops.

Added Value

Speed

Speed

Batch rare defects quickly and safely, ready for training.

Flexibility

Flexibility

Iterate models and regenerate data without costly delays.

Accuracy

Accuracy

Zero manual labeling means no bias or annotation errors.

Value

Value

From dollars to cents per image with synthetic pipelines.

Demo

Would you like a live demo?

Contact
  • Curf is participating in the ESA Business Incubation Centre Belgium.