Search result: Catalogue data in Autumn Semester 2024
Computer Science Master  | |||||||||||||||||||||||||||||||||
 Majors | |||||||||||||||||||||||||||||||||
| Major in Visual and Interactive Computing | |||||||||||||||||||||||||||||||||
| Elective Courses | |||||||||||||||||||||||||||||||||
| Number | Title | Type | ECTS | Hours | Lecturers | ||||||||||||||||||||||||||||
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| 227-0560-00L | Computer Vision and Artificial Intelligence for Autonomous Cars  Up until FS2022 offered as Deep Learning for Autonomous Driving | W | 6 credits | 3V + 2P | C. Sakaridis | ||||||||||||||||||||||||||||
| Abstract | This course introduces the core computer vision techniques and algorithms that autonomous cars use to perceive the semantics and geometry of their driving environment, localize themselves in it, and predict its dynamic evolution. Emphasis is placed on techniques tailored for real-world settings, such as multi-modal fusion, domain-adaptive and outlier-aware architectures, and multi-agent methods. | ||||||||||||||||||||||||||||||||
| Learning objective | Students will learn about the fundamentals of autonomous cars and of the computer vision models and methods these cars use to analyze their environment and navigate themselves in it. Students will be presented with state-of-the-art representations and algorithms for semantic, geometric and temporal visual reasoning in automated driving and will gain hands-on experience in developing computer vision algorithms and architectures for solving such tasks. After completing this course, students will be able to: 1. understand the operating principles of visual sensors in autonomous cars 2. differentiate between the core architectural paradigms and components of modern visual perception models and describe their logic and the role of their parameters 3. systematically categorize the main visual tasks related to automated driving and understand the primary representations and algorithms which are used for solving them 4. critically analyze and evaluate current research in the area of computer vision for autonomous cars 5. practically reproduce state-of-the-art computer vision methods in automated driving 6. independently develop new models for visual perception | ||||||||||||||||||||||||||||||||
| Content | The content of the lectures consists in the following topics: 1. Fundamentals (a) Fundamentals of autonomous cars and their visual sensors (b) Fundamental computer vision architectures and algorithms for autonomous cars 2. Semantic perception (a) Semantic segmentation (b) Object detection (c) Instance segmentation and panoptic segmentation 3. Geometric perception and localization (a) Depth estimation (b) 3D reconstruction (c) Visual localization (d) Unimodal visual/lidar 3D object detection 4. Robust perception: multi-modal, multi-domain and multi-agent methods (a) Multi-modal 2D and 3D object detection (b) Visual grounding and verbo-visual fusion (c) Domain-adaptive and outlier-aware semantic perception (d) Vehicle-to-vehicle communication for perception 5. Temporal perception (a) Multiple object tracking (b) Motion prediction The practical projects involve implementing complex computer vision architectures and algorithms and applying them to real-world, multi-modal driving datasets. In particular, students will develop models and algorithms for: 1. Semantic segmentation and depth estimation 2. Sensor calibration for multi-modal 3D driving datasets 3. 3D object detection using lidars | ||||||||||||||||||||||||||||||||
| Lecture notes | Lecture slides are provided in PDF format. | ||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Students are expected to have a solid basic knowledge of linear algebra, multivariate calculus, and probability theory, and a basic background in computer vision and machine learning. All practical projects will require solid background in programming and will be based on Python and libraries of it such as PyTorch. | ||||||||||||||||||||||||||||||||
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| 252-0546-00L | Physically-Based Simulation in Computer Graphics | W | 5 credits | 2V + 1U + 1A | S. Coros, B. Thomaszewski | ||||||||||||||||||||||||||||
| Abstract | This lecture provides an introduction to physically-based animation in computer graphics and gives an overview of fundamental methods and algorithms. The practical exercises include three assignments which are to be solved in small groups. In an addtional course project, topics from the lecture will be implemented into a 3D game or a comparable application. | ||||||||||||||||||||||||||||||||
| Learning objective | This lecture provides an introduction to physically-based animation in computer graphics and gives an overview of fundamental methods and algorithms. The practical exercises include three assignments which are to be solved in small groups. In an addtional course project, topics from the lecture will be implemented into a 3D game or a comparable application. | ||||||||||||||||||||||||||||||||
| Content | The lecture covers topics in physically-based modeling, such as particle systems, mass-spring models, finite difference and finite element methods. These approaches are used to represent and simulate deformable objects or fluids with applications in animated movies, 3D games and medical systems. Furthermore, the lecture covers topics such as rigid body dynamics, collision detection, and character animation. | ||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Fundamentals of calculus and physics, basic concepts of algorithms and data structures, basic programming skills in C++. Knowledge on numerical mathematics as well as ordinary and partial differential equations is an asset, but not required. | ||||||||||||||||||||||||||||||||
| 263-5905-00L | Mixed Reality | W | 5 credits | 3G + 1A | Z. Bauer, C. Holz, M. Pollefeys | ||||||||||||||||||||||||||||
| Abstract | 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. | ||||||||||||||||||||||||||||||||
| Learning objective | After attending this course, students will: 1. Understand the foundations of 3D graphics, Computer Vision, and Human-Machine Interaction 2. Have a clear understanding on how to build mixed reality apps 3. Have a good overview of state-of-the-art Mixed Reality 4. Be able to critically analyze and asses current research in this area. | ||||||||||||||||||||||||||||||||
| Content | The course introduces latest mixed reality technology and provides introductory elements for a number of related fields including: Introduction to Mixed Reality / Augmented Reality / Virtual Reality Introduction to 3D Computer Graphics, 3D Computer Vision. This will take place in the form of short lectures, followed by student presentations discussing the current state-of-the-art. The main focus of this course are student projects on mixed reality topics, where small groups of students will work on a particular project with the goal to design, develop and deploy a mixed reality application. The project topics are flexible and can reach from proof-of-concept vision/graphics/HMI research, to apps that support teaching with interactive augmented reality, or game development. The default platform will be Microsoft HoloLens in combination with C# and Unity3D - other platforms are also possible to use, such as tablets and phones. | ||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Prerequisites include: - Good programming skills (C# / C++ / Java etc.) - Computer graphics/vision experience: Students should have taken, at a minimum, Visual Computing. Higher level courses are recommended, such as Introduction to Computer Graphics, 3D Vision, Computer Vision. | ||||||||||||||||||||||||||||||||
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