Markus Gross: Catalogue data in Autumn Semester 2023 |
Name | Prof. Dr. Markus Gross |
Field | Informatik (Computergraphik) |
Address | Institut für Visual Computing ETH Zürich, CNB G 109 Universitätstrasse 6 8092 Zürich SWITZERLAND |
Telephone | +41 44 632 71 14 |
Fax | +41 44 632 11 72 |
grossm@inf.ethz.ch | |
Department | Computer Science |
Relationship | Full Professor |
Number | Title | ECTS | Hours | Lecturers | ||||||||||||||||||||||||||||||||||||||||||||
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252-0206-00L | Visual Computing ![]() | 8 credits | 4V + 3U | M. Gross, M. Pollefeys | ||||||||||||||||||||||||||||||||||||||||||||
Abstract | This course acquaints students with core knowledge in computer graphics, image processing, multimedia and computer vision. Topics include: Graphics pipeline, perception and camera models, transformation, shading, global illumination, texturing, sampling, filtering, image representations, image and video compression, edge detection and optical flow. | |||||||||||||||||||||||||||||||||||||||||||||||
Learning objective | 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. | |||||||||||||||||||||||||||||||||||||||||||||||
Content | Course topics will include: Graphics pipeline, perception and color models, camera models, transformations and projection, projections, lighting, shading, global illumination, texturing, sampling theorem, Fourier transforms, image representations, convolution, linear filtering, diffusion, nonlinear filtering, edge detection, optical flow, image and video compression. In theoretical and practical homework assignments students will learn to apply and implement the presented concepts and algorithms. | |||||||||||||||||||||||||||||||||||||||||||||||
Lecture notes | A scriptum will be handed out for a part of the course. Copies of the slides will be available for download. We will also provide a detailed list of references and textbooks. | |||||||||||||||||||||||||||||||||||||||||||||||
Literature | Markus Gross: Computer Graphics, scriptum, 1994-2005 | |||||||||||||||||||||||||||||||||||||||||||||||
252-0543-01L | Computer Graphics ![]() | 8 credits | 3V + 2U + 2A | M. Gross, M. Papas | ||||||||||||||||||||||||||||||||||||||||||||
Abstract | This course covers fundamental and advanced concepts of modern computer graphics. Students will learn the fundamentals of digital scene representations, advanced physically-based light transport algorithms for generating photorealistic images from these scene representations, and inverse rendering methods for recovering digital scene representations from captured images. | |||||||||||||||||||||||||||||||||||||||||||||||
Learning objective | At the end of the course, the students will be able to build a rendering system based on path-tracing algorithms. The students will learn the principles of physically-based rendering and computer graphics. In addition, the course is intended to stimulate the student's curiosity to explore the field of computer graphics in subsequent classes or on their own. | |||||||||||||||||||||||||||||||||||||||||||||||
Content | We will begin with an introduction to light emission and radiometric quantities, followed by an exploration of geometry representations and texture mapping. Next, we will mathematically formulate the physics of light transport and appearance modeling. Subsequently, we will introduce relevant concepts from Monte Carlo integration and develop path-tracing algorithms to solve these equations by simulating light transport for direct and global illumination due to hard surfaces and participating media, such as fog, smoke, and translucent objects. Moreover, we will present techniques for significantly improving path-tracing efficiency, including importance sampling, multiple importance sampling, stratified sampling, denoising, and acceleration data structures. The course lectures will conclude with an overview of image-based capture and rendering methods. Topics covered will include geometry reconstruction, material acquisition, differentiable rendering, and image-based rendering. | |||||||||||||||||||||||||||||||||||||||||||||||
Lecture notes | no | |||||||||||||||||||||||||||||||||||||||||||||||
Literature | Books: Physically Based Rendering: From Theory to Implementation High Dynamic Range Imaging: Acquisition, Display, and Image-Based Lighting Multiple view geometry in Computer Vision | |||||||||||||||||||||||||||||||||||||||||||||||
Prerequisites / Notice | Prerequisites: Fundamentals of calculus and linear algebra, basic concepts of algorithms and data structures, programming skills in C++, and the Visual Computing course are recommended. The programming assignments will be in C++. This will not be taught in the class. | |||||||||||||||||||||||||||||||||||||||||||||||
Competencies![]() |
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263-5702-00L | Seminar on Digital Humans ![]() ![]() The deadline for deregistering expires at the end of the third week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar. | 2 credits | 2S | M. Gross, B. Solenthaler, O. Sorkine Hornung, S. Tang, R. Wampfler | ||||||||||||||||||||||||||||||||||||||||||||
Abstract | This seminar covers advanced topic in digital humans with a focus on the latest research results. Topics include estimating human pose and motion from images, human motion synthesis, learning-based human avatar creation, learning neural implicit representations for humans, modeling, animations, artificial intelligence for digital characters, and others. A collection of research papers is selected. | |||||||||||||||||||||||||||||||||||||||||||||||
Learning objective | The goal is to get an overview of actual research topics in the field of digital humans and to improve presentation and critical analysis skills. | |||||||||||||||||||||||||||||||||||||||||||||||
Content | This seminar covers advanced topics in digital humans including both seminal research papers as well as the latest research results. A collection of research papers are selected covering topics such as estimating human pose and motion from images, human motion synthesis, learning-based human avatar creation, learning neural implicit representations for humans, modeling, animations, artificial intelligence for digital characters, and others. Each student presents one paper to the class and leads a discussion about the paper. All students read the papers and participate in the discussion. | |||||||||||||||||||||||||||||||||||||||||||||||
Literature | Individual research papers are selected each term. See https://vlg.inf.ethz.ch/, https://igl.ethz.ch/, and http://graphics.ethz.ch/ for example papers. | |||||||||||||||||||||||||||||||||||||||||||||||
Competencies![]() |
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264-5800-22L | Doctoral Seminar in Visual Computing (HS23) ![]() | 1 credit | 1S | D. B. Baráth, M. Gross, M. Pollefeys, B. Solenthaler, O. Sorkine Hornung, S. Tang | ||||||||||||||||||||||||||||||||||||||||||||
Abstract | In this doctoral seminar, current research at the Institute for Visual Computing will be presented and discussed. The goal is to learn about current research projects at our institute, to strengthen our expertise in the field, to provide a platform where research challenges caThis graduate seminar provides doctoral students in computer science a chance to read and discuss current research papers. | |||||||||||||||||||||||||||||||||||||||||||||||
Learning objective | In this doctoral seminar, current research at the Institute for Visual Computing will be presented and discussed. The goal is to learn about current research projects at our institute, to strengthen our expertise in the field, to provide a platform where research challenges can be discussed, and also to practice scientific presentations. | |||||||||||||||||||||||||||||||||||||||||||||||
Content | Current research at the IVC will be presented and discussed. | |||||||||||||||||||||||||||||||||||||||||||||||
Prerequisites / Notice | This course requires solid knowledge in the area of Computer Graphics and Computer Vision as well as state-of-the-art research. |