Advanced Deep Learning for Computer Vision: Visual Computing (ADL4CV) (IN2390)

Welcome to the Advanced Deep Learning for Computer Vision course offered in winter semester 2025/2026!

General Course Structure

The lecture will be held in person on Wednesdays 10am-12pm. Lecture slides will be published on this website. For the meantime, we keep the lecture slides and recordings from the previous semesters on the website as well, as a reference.

In addition, the class will feature practical projects. At the end of this course, all students are expected to deliver complete projects as teams of two persons. The project presentations during the semester and at the end of the semester will be held in person. Each team/project will be aligned with a TA, who will be responsible for all project-related questions.

We will cover the class structure and planning in more detail in the first lecture at the beginning of the course.

Lecture

See lecture links below.

Lecturer: Prof. Dr. Matthias Niessner

ECTS: 8

SWS: 5

Project

In order to secure the steady progress of each project, we will schedule two presentations during the semester. The current project status and intermediate results are required in the presentation.

In addition to the regular presentations, we will also arrange a final poster presentation at the end of the course, where every team is expected to present their project with a poster as in top-tier computer vision conferences

Every team is required to submit a 4-page project report written with the CVPR paper template.

Topics:

• 3D Scene Understanding
• 3D Reconstrunction
• Neural Rendering
• Vision and Language
• ...

Presentation dates: Timeline is available in Moodle.

Teaching Assistants: Ziya Erkoç, Lukas Höllein, Nicolas von Lützow and Jonathan Schmidt

Lecture Slides and Recordings

Slides (WS 25/26)

• Week 1 - Lecture 1: I2DL Recap, DL Best Practices Recap & Visualization
• Week 2 - Lecture 2: 3D Deep Learning
• Week 3 - Lecture 3: Neural Fields
• Week 4 - Lecture 4: NeRF
• Week 5 - Lecture 5: 3D Gaussian Splatting
• Week 6 - Lecture 6: GANs 1
• Week 7 - Lecture 7: GANs 2
• Week 8 - Lecture 8: Sequence Models
• Week 9 - Lecture 9: Large Reconstruction Models

• Week 1 - Lecture 1: I2DL Recap, DL Best Practices Recap & Visualization
• Week 2 - Lecture 2: 3D Deep Learning
• Week 3 - Lecture 3: Neural Fields
• Week 4 - Lecture 4: NeRF
• Week 5 - Lecture 5: 3D Gaussian Splatting
• Week 6 - Lecture 6: Large Reconstruction Models
• Week 7 - Lecture 7: GANs 1
• Week 8 - Lecture 8: GANs 2
• Week 9 - Lecture 9: Diffusion Models

• Week 1 - Lecture 1: I2DL Recap, DL Best Practices Recap & Visualization
• Week 2 - Lecture 2: 3D Deep Learning
• Week 3 - Lecture 3: Neural Fields
• Week 4 - Lecture 4: NeRF
• Week 5 - Lecture 5: GANs 1
• Week 6 - Lecture 6: GANs 2
• Week 7 - Lecture 7: Diffusion Models
• Week 8 - Lecture 8: Sequence Models

• Week 1 - Lecture 1: I2DL Recap, DL Best Practices Recap & Visualization - Recording
• Week 2 - Lecture 2: Siamese Neural Networks & Similarity Learning - Recording
• Week 3 - Lecture 3: Autoencoders, Variational Autoencoders & Style Transfer - Recording
• Week 4 - Lecture 4: Representation Learning - Recording
• Week 5 - Lecture 5: Sequence Models - Recording
• Week 6 - Lecture 6: GANs 1 - Recording
• Week 7 - Lecture 7: GANs 2 - Recording

Previous Slides and Recodings (SS 20 - SS 22)

• Week 1 - Lecture 1: Visualization and Interpretability - Recording
• Week 2 - Lecture 2: Siamese Neural Networks and Similarity Learning - Recording
• Week 3 - Lecture 3: Autoencoders and VAE - Recording
• Week 4 - Lecture 4: Deep Learning on graphs - Recording
• Week 5 - Lecture 5: Generative Neural Networks - Recording
• Week 6 - Lecture 6: GAN Architectures and Conditional GANs - Recording
• Week 7 - Lecture 7: Videos and Autoregression - Recording
• Week 8 - Lecture 8: Neural Rendering - Recording
• Week 9 - Lecture 9: Deep Learning in Higher Dimensions - Recording

Prerequisites

• Introduction to Deep Learning.
• Strong mathematical background: linear algebra, calculus.
• Previous knowledge of Python is needed. This includes scripting skills such as operating system calls.

Forum

We will use Moodle for announcements, discussions and course-related information.

Contact us

If you have any questions regarding the organization of the course, do not hesitate to contact us at: adl4cv-i28@in.tum.de. Please refrain from using the personal email addresses.

For questions on the syllabus, exercises or any other questions on the content of the lecture, we will use the forum discussion board.