Course information

Course Contents
[b]Learning Objectives:[/b]

This course introduces the fundamental challenges and technologies of computer vision that are essential for Augmented Reality (AR) and Virtual Reality (VR). The focus is on how real environments are perceived, analyzed, and seamlessly overlaid or represented with virtual content. The methods covered range from classical camera calibration and 3D reconstruction to modern, data-driven approaches such as neural pose estimation.

[b]Course Content:[/b]

The lecture provides in-depth knowledge in the following core areas:
[list]
[*][i]Fundamentals of Perception:[/i] Camera geometry, calibration, and 3D transformations.
[*][i]Environment Capture:[/i] Real-time 3D reconstruction, sensor fusion, and material reconstruction.
[*][i]Tracking & Pose Estimation:[/i] Feature-based tracking, geometric as well as neural methods for object and camera pose estimation.
[*][i]Visualization & Perception:[/i] Fundamentals of 3D rendering, perception of visualizations in VR, and principles of immersion.
[*][i]Interaction & Systems:[/i] Interaction techniques in AR/VR, device technologies, and practical application examples.
[/list]

[b]Diploma Supplement:[/b]
real-time rendering, color systems, light simulation, virtual reality, augmented reality, camera calibration

Literature
Computer Vision: Algorithms and Applications https://szeliski.org/Book/, Further readings will be provided during the course.

Preconditions
[b]Prerequisites:[/b] Advise: Computer Vision 1