Course Information

We live in a world that spans 3 dimensions. Cameras and sensors image the 3D world by projecting to a 2D plane. How can we recover the 3D world back from these images? What techniques can we use to process 3D data? In this course we will study computer vision and machine learning techniques to recover 3D information of the world from images, and process and understand 3D data. We will learn about classical computer vision techniques but focus on cutting-edge deep learning methods. The techniques we will study are widely used, for instance, in self-driving cars and smartphone AR face filter apps.

Class Time: Tu Th, 1:00-2:20pm, Online


  • Office hours (aka paper discussion hours) and course structure have been updated.
  • Class starts on Thursday, Sep 10. Please see Canvas for the online meeting link.
  • Please register/request override directly on Courses @ Brown with a short note on how you meet the recommended background preparation.

Learning Goals

This course has two main learning goals. Students are expected to actively participate in class including discussions and group activities.

  1. Learn about the state of the art in 3D computer vision and machine learning. We will do this by reading a curated list of research papers on relevant topics.
  2. Understand research practice in computer science, with specific focus on the computer vision and ML communities. We will learn how to effectively read papers, write reviews, present papers, critique and discuss research, and do a group research project.

Recommended Background

This is an advanced course but students at all levels are welcome to participate if they have the necessary background. We recommend that you take one or more of the following courses or their equivalents before enrolling.


If you would like to take this course, please register/request override directly on Courses @ Brown. If you don't meet the recommended background preparation, please reach out to the instructor. Access code for Piazza will be provided to enrolled students separately. The online meeting link for all office hours are the same as the lecture.

Instructor: Srinath Sridhar

Office Hours: Tue, 2:30 3:30pm

UTA: Vikas Thamizharasan

Office Hours: : Wednesday, 1:30-2:30pm

UTA: Yiheng Xie

Office Hours: Monday, 11:00am-12:00noon


Parts of this course are inspired by conversations with/courses taught by James Tompkin, Judy Hoffman, Leo Guibas, Daniel Ritchie, and Christian Theobalt. Please feel free to re-use materials from this course for your own.