3D Computer Vision

This series of lectures will focus on 3D computer vision. We will start with an introduction on 3D representations and explore state-of-the-art models for 3D deep learning.

Each lecture will consist of

• a video lecture: Links to online video lectures along with reading material covering the topics for each lecture.

• a Q&A session: A live Q&A session.

• a lab: A practical lab session followed by a short 1-page report.

After the end of the week,

• a quiz: A set of multiple-choice questions covering the material taught throughout the week.

Lecture 1: 3D representations & 3D shape from images

Lecturer: Georgia Gkioxari

In the first lecture, we will introduce some of the most common 3D representations and dive into state-of-the-art models for 3D shape inference from a single image

• LECTURE1.md: Reading material and video lectures
• LAB1.md: Lab session and short report assignment (deadline: Sunday, April 18)

Lecture 2: Differentiable Rendering

Lecturer: Nikhila Ravi

In the second lecture, we will dive into an in-depth analysis of differentiable rendering.

• LECTURE2.md: Reading material and video lectures
• LAB2.md: Lab session and short report assignment (deadline: Sunday, April 18)

Lecture 3: More Topics in 3D Deep Learning

Lecturer: Georgia Gkioxari

In the third lecture, we will cover more topics and applications on 3D deep learning.

• LECTURE3.md: Reading material and video lectures

Quiz

• QUIZ.md: A test with multiple-choice questions on the material (deadline: Sunday, April 18)

Schedule

The schedule for the class can be found in SCHEDULE.md