Overview
What this challenge is about.
Multi-View Pose Estimation for a Sports-Analytics Startup. Advanced challenge in code. Writing production code that solves real engineering problems, earn a ...
The Brief
What you'll do, and what you'll demonstrate.
Build a multi-view 3D pose pipeline with MPJPE under 8 cm and lay out the cost + accuracy trade-offs for a 20-stadium scale-up.
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
- Apply 2D pose estimation as a building block for 3D reconstruction
- Use camera calibrations + triangulation for 3D joint recovery
- Evaluate pose estimation with MPJPE and visual sanity checks
- Reason about scaling a vision pipeline across many fixed-camera sites
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.
- Pose Estimation
Apply pose estimation to solve real industry problems and demonstrate production-level capability.
- Multi View Geometry
Apply multi view geometry to solve real industry problems and demonstrate production-level capability.
- 3d Reconstruction
Apply 3d reconstruction to solve real industry problems and demonstrate production-level capability.
- Pytorch
Apply pytorch to solve real industry problems and demonstrate production-level capability.
- Evaluation
Apply evaluation to solve real industry problems and demonstrate production-level capability.
- Ml Pipelines
Apply ml pipelines to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
Computer Vision Engineer
Multi-view 3D pose pipelines are exactly the work CV engineers ship at sports-analytics, AR, and motion-capture companies.
This challenge sharpens
- pose-estimation
- multi-view-geometry
- 3d-reconstruction
Machine Learning Engineer
End-to-end pipelines with proper evaluation are the MLE habit that turns research code into product code.
This challenge sharpens
- pytorch
- ml-pipelines
- evaluation
Applied AI Scientist
Translating a pose pipeline into a scale-up plan with cost trade-offs is the applied-AI-scientist craft of bridging research and business.
This challenge sharpens
- evaluation
- pose-estimation
- 3d-reconstruction