Course Description
Any autonomous agent we develop must perceive and act in a 3D world. The ability to infer, model, and utilize 3D
representations is therefore of central importance in AI, with applications ranging from robotic manipulation
and self-driving to virtual reality and image manipulation. While 3D understanding has been a longstanding goal
in computer vision, it has witnessed several impressive advances due to the rapid recent progress in (deep)
learning techniques. The goal of this course is to explore this confluence of 3D Vision and Learning-based
methods. In particular, this course will cover topics including -
Format and Prerequisites
The course will be lecture-based, and the grades will primarily be determined by assignments and a final project. The course will require as background good coding skills, and an understanding of basics in Computer Vision (e.g. image formation, ray optics) and Machine Learning (e.g. optimization, neural networks).
Course Staff
Please use the course Piazza page for all communication with course staff
Course Instructor
Teaching Assistants
Related Courses
If you found this course useful, you may also be interested in the following related courses:
Learning for 3D Vision by Angjoo Kanazawa, UC Berkeley
3D Vision by Derek Hoiem, UIUC
Physics-based Rendering by Ioannis (Yannis) Gkioulekas, CMU
Machine Learning for Inverse Graphics by Vincent Sitzmann, MIT
Geometry-based Methods in Vision, CMU
Machine Learning meets Geometry by Hao Su, UCSD
Previous offerings
Learning for 3D Vision by Shubham Tulsiani, Spring 2022