Lecture Course: 3D Deep Learning, Winter Term 2023/24

Illustration: serveral Visualisations based on Deep Learning applications

Objectives

Machine Learning can be found in almost all fields of computer science. This course teaches basic concepts of machine learning and how they are applied to computer graphics. This course covers the whole process of developping, training neural nets and also adapting complex models to new datasets. Learning from 3D points aka. point clouds as it is covered in this course, is a current research topic in the field of computer graphics. Students will thus first learn how to solve standard machine learning problems, before applying their know how to 3D data. All practical realizations will be made in Tensorflow, which is also introduced in the course.

We assume previous knowledge in computer science, but not necessarily in machine learning.

Content

Introduction to 3D Deep Learning
3D Primer
Deep Learning Primer
Advanced Architectures
Learning on Volumes
Learning on Point Clouds
Learning on Meshes
Differentiable Rendering
Neural Radiance Fields
Generative Models
Future Trends

Lecturer

Prof. Dr. Timo Ropinski

Supervisors

Dominik Engel
Poonam

Lecture

The lecture will take place digitally through Moodle and Zoom. New chapters on Differentiable & Neural Rendering, as well as Neural Scene Representations will be held in the "hybrid" format, i.e. there will be in-person lectures with recordings available online.