TrAC 3D Vision - NeRFs & INRs
The 3D Vision - NeRFs & INRs digital badge focuses on the basics of 3D Vision and how to use state-of-the-art 3D vision algorithms such as Neural Radiance Fields, Gaussian Splats, Implicit Neural Representations. The 3D Vision - NeRFs & INRs course is offered by Iowa State University's Translational AI Center (TrAC) and is part of a larger Advanced AI Techniques pathway program and intended for audiences within the spectrum of the software and technology industry, including software engineers, data scientists, data engineers, data analysts, research scientists, and software developers.
Skills / Knowledge
- Artificial Intelligence
- 3D Vision
- Neural Radiance Fields (NERF)
- Gaussian Splats
Earning Criteria
Required
The 3D Vision - NeRFs & INRs Badge is earned after successful completion of a 4-week, asynchronous, self-paced online course consisting of 4 modules. The 4 modules cover essential topics including the use of state-of-the-art 3D vision algorithms such as Neural Radiance Fields, Gaussian Splats, Implicit Neural Representations.
This course offers a blend of hands-on activities, assignments, video lectures and tutorials.
Learning Outcomes:
Practice the fundamentals of 3D vision, rendering pipelines, and differentiable rendering
Apply 3D scene reconstruction techniques and solve common challenges in practical scenarios
Implement advanced 3D vision algorithms like Neural Radiance Fields (NeRFs) and Gaussian Splats
Develop end-to-end pipelines for capturing, reconstructing, and rendering 3D scenes
Assessment:
Participants will be assessed on:
Engagement with each module
One exercise in setting up a computing environment for NeRFStudio
One coding assignment centered on reconstructing a scene capture from a smartphone
Two quizzes demonstrating basic knowledge of 3D Vision and the different INR and NeRF algorithms
About TrAC
The Translational AI Center will break down disciplinary silos to bring together core Iowa State artificial intelligence researchers and subject matter experts interested in applying new technologies to their work. The center will initially focus on conducting core artificial intelligence research, as well as pursuing five application areas of artificial intelligence:
Materials design and manufacturing
Biology, healthcare, and quality of life
Autonomy, intelligent transportation, and smart infrastructure
Food, energy, and water
Ethics, fairness, and adoption.
In addition to serving as a scientific hub for translational artificial intelligence, the center will organize research seminars, host workshops, training, and onboarding programs, offer seed funding for research projects, and serve as an intermediary between private industry partners seeking research services and appropriate university faculty.