CMSC 848F - 3D Vision

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.

Links to the lecture slides can be found on ELMS .

Course Information

All concepts will be covered during in class lecture. However, we also recommend the following books as good references:

  • Computer Vision: Algorithms and Applications, Richard Szeliski, Springer, 2020 Online version
  • Computer Vision: A Modern Approach: D. Forsythe and J. Ponce, Prentice-Hall, 2003 (available online)
  • Digital Image Processing, Prentice Hall, Rafael Gonzalez, and Richard Woods, 2008.
  • Multiple View Geometry in Computer Vision, Cambridge University Press, Richard Hartley, and Andrew Zisserman, 2003.

Schedule and Readings

Lectures (Tentative Schedule)


Instructor: Jia-Bin Huang (jbhuang at

Office: IRB 4236
Office Hours: By appointment using this link

Teaching Assistants

Name Email Office hours
Hadi Alzayer hadi at TBD
Yiran Xu yiranx at TBD
Yi-Ting Chen ytchen at TBD

Class Resources

Online Course Tools
  • ELMS - This is where you can find links to Zoom lectures, find recorded lectures, and see final grades.
  • Piazza - This is the place for class discussions. Please do not post homework/project solutions here.

Background Material
The following web pages provide some background and other helpful information.

  • Computer Vision Compendium CVonline
  • Fundamentals on image processing pdf
  • Recognizing and avoiding plagiarism pdf

Homeworks and Programming Assigments

Posted homeworks and programming assignments can be found on ELMS.


Thanks to Shubham Tusalini, and Chris Metzler who provided most of the slides, assignments, and material that serve as the basis for this course.