After successful completion of the course, students are able to understand the generation of 3D digital models and representations as a requirement for applying deep learning methods, and to analyze surface properties for running simulations. Reconstruction from images or sensors, simulations on surfaces, and procedural modeling: In this course, you will practice processing meshes with machine learning algorithms, run simulations, and generate entire cities procedurally.

Following topics will be treated in this course among others: Polygonal Meshes, Curves and Surfaces Representations and Properties, Reconstruction of 3D Models from Scans, Parametrization, Simulations on Surfaces, Procedural Modeling.

Lectures, excercises and paper presentations.

ECTS Breakdown (3 ECTS = 75 hours):

Lectures:
8 units of 1.5h each (approx. 12 hours)

Assignments (approx. 63 hours):
4 homework assignments with a load of approx. 51h
1 paper presentation with a load of approx. 12h

Course dates & timeline:
09.03.2023 (Thu), 15:30-17:00 Introduction
14.03.2023, 13:00-14:30 Lecture 1: Meshes, Assignment #1
21.03.2023, 13:00-14:30 Lecture 2: Curves & Surfaces
28.03.2023, 13:00-14:30 Lecture 3: Scan Pipeline, Assignment #2
18.04.2023, 10:00-12:00 [in FAV 01A!] Lecture 4: Parametrization, Assignment #1 due
25.04.2023, 13:00-14:30 Lecture 5: Applications 1, Assignment #2 due
02.05.2023, 13:00-14:30 Lecture 6: Applications 2, Assignment #3
16.05.2023, 13:00-14:30 Lecture 7: Procedural Modeling 1, Assignment #4
23.05.2023, 13:00-14:30 Lecture 8: Procedural Modeling 2
06.06.2023 Assignments #3 due
13.06.2023, 13:00-15:00 Paper presentations
27.06.2023 Assignments #4 due

(Deadlines are always midnight)

Homework and a presentation with a total workload of approx. 63 hours.

Basic programming and math skills recommended (MATLAB, Python and linear algebra, calculus, differential equations will be used).