Search result: Catalogue data in Spring Semester 2023
Computer Science Master  | |||||||||||||||
 Minors | |||||||||||||||
| Minor in Computer Graphics | |||||||||||||||
| Number | Title | Type | ECTS | Hours | Lecturers | ||||||||||
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| 252-0538-00L | Shape Modeling and Geometry Processing | W | 8 credits | 2V + 1U + 4A | O. Sorkine Hornung | ||||||||||
| Abstract | This course covers the fundamentals and developments in geometric modeling and geometry processing. Topics include surface modeling based on point clouds and polygonal meshes, mesh generation, surface reconstruction, mesh fairing and parameterization, discrete differential geometry, interactive shape editing, topics in digital shape fabrication. | ||||||||||||||
| Learning objective | The students will learn how to design, program and analyze algorithms and systems for interactive 3D shape modeling and geometry processing. | ||||||||||||||
| Content | Recent advances in 3D geometry processing have created a plenitude of novel concepts for the mathematical representation and interactive manipulation of geometric models. This course covers the fundamentals and some of the developments in geometric modeling and geometry processing. Topics include surface modeling based on point clouds and triangle meshes, mesh generation, surface reconstruction, mesh fairing and parameterization, discrete differential geometry, interactive shape editing and digital shape fabrication. | ||||||||||||||
| Lecture notes | Slides and course notes | ||||||||||||||
| Prerequisites / Notice | Prerequisites: Visual Computing, Computer Graphics or an equivalent class. Experience with C++ programming. Solid background in linear algebra and analysis. Some knowledge of differential geometry, computational geometry and numerical methods is helpful but not a strict requirement. | ||||||||||||||
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| 252-5706-00L | Mathematical Foundations of Computer Graphics and Vision | W | 5 credits | 2V + 1U + 1A | T. Aydin, A. Djelouah | ||||||||||
| Abstract | This course presents the fundamental mathematical tools and concepts used in computer graphics and vision. Each theoretical topic is introduced in the context of practical vision or graphic problems, showcasing its importance in real-world applications. | ||||||||||||||
| Learning objective | The main goal is to equip the students with the key mathematical tools necessary to understand state-of-the-art algorithms in vision and graphics. In addition to the theoretical part, the students will learn how to use these mathematical tools to solve a wide range of practical problems in visual computing. After successfully completing this course, the students will be able to apply these mathematical concepts and tools to practical industrial and academic projects in visual computing. | ||||||||||||||
| Content | The theory behind various mathematical concepts and tools will be introduced, and their practical utility will be showcased in diverse applications in computer graphics and vision. The course will cover topics in sampling, reconstruction, approximation, optimization, robust fitting, differentiation, quadrature and spectral methods. Applications will include 3D surface reconstruction, camera pose estimation, image editing, data projection, character animation, structure-aware geometry processing, and rendering. | ||||||||||||||
| 263-5701-00L | Scientific Visualization | W | 5 credits | 2V + 1U + 1A | M. Gross, T. Günther | ||||||||||
| Abstract | This lecture provides an introduction into visualization of scientific and abstract data. | ||||||||||||||
| Learning objective | This lecture provides an introduction into the visualization of scientific and abstract data. The lecture introduces into the two main branches of visualization: scientific visualization and information visualization. The focus is set onto scientific data, demonstrating the usefulness and necessity of computer graphics in other fields than the entertainment industry. The exercises contain theoretical tasks on the mathematical foundations such as numerical integration, differential vector calculus, and flow field analysis, while programming exercises familiarize with the Visualization Tool Kit (VTK). In a course project, the learned methods are applied to visualize one real scientific data set. The provided data sets contain measurements of volcanic eruptions, galaxy simulations, fluid simulations, meteorological cloud simulations and asteroid impact simulations. | ||||||||||||||
| Content | This lecture opens with human cognition basics, and scalar and vector calculus. Afterwards, this is applied to the visualization of air and fluid flows, including geometry-based, topology-based and feature-based methods. Further, the direct and indirect visualization of volume data is discussed. The lecture ends on the viualization of abstract, non-spatial and multi-dimensional data by means of information visualization. | ||||||||||||||
| Prerequisites / Notice | Fundamentals of differential calculus. Knowledge on numerical mathematics, computer algebra systems, as well as ordinary and partial differential equations is an asset, but not required. | ||||||||||||||
| 263-5806-00L | Digital Humans Previously Computational Models of Motion and Virtual Humans | W | 8 credits | 3V + 2U + 2A | S. Coros, S. Tang | ||||||||||
| Abstract | This course covers the core technologies required to model and simulate motions for digital humans and robotic characters. Topics include kinematic modeling, physics-based simulation, trajectory optimization, reinforcement learning, feedback control for motor skills, motion capture, data-driven motion synthesis, and ML-based generative models. They will be richly illustrated with examples. | ||||||||||||||
| Learning objective | Students will learn how to estimate human pose, shape, and motion from videos and create basic human avatars from various visual inputs. Students will also learn how to represent and algorithmically generate motions for digital characters and their real-life robotic counterparts. The lectures are accompanied by four programming assignments (written in python or C++) and a capstone project. The deadline to cancel/deregister from the course is May 1st. Deregistration after the deadline will lead to fail. | ||||||||||||||
| Content | - Basic concept of 3D representations - Human body/hand models - Human motion capture; - Non-rigid surface tracking and reconstruction - Neural rendering - Optimal control and trajectory optimization - Physics-based modeling for multibody systems - Forward and inverse kinematics - Rigging and keyframing - Reinforcement learning for locomotion | ||||||||||||||
| Prerequisites / Notice | Experience with python and C++ programming, numerical linear algebra, multivariate calculus and probability theory. Some background in deep learning, computer vision, physics-based modeling, kinematics, and dynamics is preferred. | ||||||||||||||
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