Reconstruct a Heritage Facade with Structure-from-Motion

FreeVerified credential2 weeksAdvanced

Overview

What this challenge is about.

Reconstruct a Heritage Facade with Structure-from-Motion. Advanced challenge in code. Writing production code that solves real engineering problems, earn a b...

CredentialBlockchain-anchored

ShareableLinkedIn-ready

LanguageEnglish

PaceSelf-paced

The Brief

What you'll do, and what you'll demonstrate.

Deliver a metric-scaled, textured 3D reconstruction of a heritage facade from phone photos, with documented sub-2cm accuracy.

This is not a coding exercise. It is the work a software engineer does between a Jira ticket and a merged PR. That distinction matters to every hiring manager who has seen candidates solve LeetCode problems and none who have shipped production code under real constraints.

When you finish, you will have something most graduates do not: a real-world deliverable, verified by Ewance, that you can show to a hiring manager and say "I did this. Here is the proof."

Earning criteria — what you'll demonstrate

Execute a full SfM + MVS pipeline on a real outdoor dataset
Apply ground control points to convert arbitrary-scale reconstructions to metric units
Diagnose reconstruction failure modes (texture-less regions, repeated patterns)
Communicate geometric uncertainty to a non-vision audience

Program Fit

Where this fits in your program.

Sharpens the same skills your degree expects you to demonstrate.

Aligned coursework coming soon.

Skills

Skills you'll demonstrate.

Each one shows up on your verified credential.

Careers

Career paths this challenge builds toward

Completing this challenge demonstrates skills that transfer directly to these roles:

Career paths this builds toward

Canonical roles

Computer Vision Engineer
Teaching a machine to see is harder than it sounds and more interesting than it looks. Computer vision engineers shape the systems that read documents, navigate self-driving cars, screen medical images, and answer questions about photos. The role mixes the math of multi-view geometry with the engineering grind of getting models small and fast enough to run where they're needed — sometimes on a phone, sometimes on a robot. Good work here looks like a pipeline that holds up in real lighting, real motion, and real failure modes. Students grow into this path by getting hands-on with OpenCV and PyTorch early, then learning the harder craft of optimizing models without quietly destroying their accuracy.
AI Engineering

One more thing

You can put a credential on your CV by Friday.

Start this challenge