ETH Zurich - D-INFK - IVC - CVG - Lectures


Computer Vision

The goal of this course is to provide students with a good understanding of computer vision and image analysis techniques. The main concepts and techniques will be studied in depth and practical algorithms and approaches will be discussed and explored through the exercises and a course project.

Visual Computing

This course provides an in-depth introduction to the core concepts of computer graphics, image processing, multimedia, and computer vision. The course forms a basis for the specialization track Visual Computing of the CS master program at ETH.

Advanced Topics in Computer Graphics and Vision

This seminar covers advanced topics in visual computing, including both seminal research papers as well as the latest research results. Topics include data-driven modeling and animation, image based rendering, real-time vision & graphics, physical simulation and generative models, visual perception, computational photography, video synthesis, and others.

Mixed Reality Lab

The goal of this course is an introduction and hands-on experience on latest mixed reality technology at the cross-section of 3D computer graphics and vision, human machine interaction as well as gaming technology. Small groups of students will realize a particular software project and deploy it to an MR/AR device such as Microsoft HoloLens or a tablet or smartphone.

Computational Regularity (Autumn Semester 2016)

Regularity is an essential and ubiquitous concept in nature, science and art. Numerous biological, natural or man-made structures exhibit regularities, abstracted by symmetries,as a fundamental design principle or as an essential aspect of their function. Whether by evolution or by design, symmetry implies potential structural efficiencies that make it universally appealing. Much of our understanding of the world is based on the perception and recognition of recurring structures (in space and/or time), and so is our sense of beauty. With increasing amount and variety of digitized data,seeking for patterns systematically has become increasingly pertinent and necessary. This course concentrates on rigorous theory (group theory), keen observations and computational (automatic)discovery of patterns in various data forms in our daily life and our research. We aim to develop effective computational treatments of regularity to capture real world regular or near-regular patterns in spite of uncertainty.

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