I worked with a founder to develop and launch the core technology behind his trading card grading platform. Collectable trading cards are a big market, and the cards are sometimes worth a lot. Collectors trust specialized grading services to assess the condition and value of trading cards during transactions, and our goal was to automate part of this ecosystem.
We released an iOS app, backed by a machine learning pipeline, that lets collectors scan their card using the phone's camera from various angles, and receive a grade and detailed damage report. The kinds of damage we assess are scratches and dents on the surface, squashed or rounded corners, and frayed edges.
To produce a unified map of damage on the card in 3D from 52 separate 2D images, I built a camera calibration system using differentiable rendering. By learning the camera pose of all 52 views, we can collate multiple views of the same damage into a unified instance in 3D space, thereby allowing us to accurately count the number and severity of damage on the card.
After launching the app and machine learning backend, I worked with the founder again to build an auto-scaling system to save thousands of dollars in monthly compute bills.
