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
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a video lecture: Links to online video lectures along with reading material covering the topics for each lecture.
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a Q&A session: A live Q&A session.
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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