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CalibrationCard™ — Physical Reference Card + Mobile App

A credit-card-sized, durable reference card printed with multiple precisely-manufactured geometric shapes (circles, squares, grids at known dimensions) that users place in their photograph next to the object being measured. A companion mobile app uses computer vision to auto-detect the calibration card, establish real-world scale, then lets users draw measurement lines on the object—the app converts pixels to actual dimensions automatically.

PHYSICAL_PRODUCT

30 weeks • 70% confidence

Value Proposition

Eliminates manual calibration math entirely. No focal length knowledge needed. Works with any smartphone camera. Card is reusable, durable, fits in a pocket. App processes locally (no cloud dependency, works offline). 10× faster than manual pixel-to-unit conversion.

Target Audience

Field engineers, insurance adjusters, construction inspectors, real estate appraisers, maintenance technicians who take photos on jobsites and need quick dimension verification without carrying rulers or laser measures.

Key Features

Laser-cut physical card with 5 precision reference shapes (2cm, 5cm, 10cm circles and grids) in matte finish to reduce glare
Mobile app auto-detects card orientation and position in any photo
Draw-to-measure UI: users tap two points on object, app calculates real distance instantly
And more, with full implementation detail...

Tech Stack

TensorFlow Lite (card detection model) Swift (iOS) / Kotlin (Android) Firebase or Supabase (optional cloud backup) Stripe or RevenueCat (subscription billing)

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Original Problem

Converting 2D image measurements to real-world physical dimensions without specialized equipment

Engineers, researchers, and technical professionals need to measure physical object dimensions from photographs but lack straightforward methods to convert pixel measurements into actual real-world units (cm, meters, etc.). Current solutions require manual camera calibration knowledge, understanding of focal length parameters (fx, fy), and complex mathematical conversions, making this inaccessible to non-computer-vision experts who just need quick, accurate measurements from images.

Score: 17.5%