| Module Code | CSU44054 |
| Module Name | Augmented Reality |
| ECTS Weighting [1] | 5 ECTS |
| Semester Taught | Semester 1 |
| Module Coordinator/s | Gareth Young & Binh-Son Hua |
Module Learning Outcomes
On successful completion of this module, students will be able to:
- To gain background knowledge at the intersection of computer graphics, computer vision, and machine learning tailored for XR
- To learn the fundamental and state-of-the-art techniques in modern XR hardware, software, and applications
- To develop a fully functional XR project
Module Content
This course covers fundamentals, state-of-the-art augmented reality, and related extended reality (XR) technology areas. Theoretical background, as well as practical solutions and applications, will be presented in the lectures. Students will get direct exposure to the latest research from the Graphics and Vision discipline.
In their work, students will be asked to develop their project in various stages, from brainstorming new ideas, trying their implementations, testing and evaluating, and demonstrating their final documentation via presentation. In this way, they will experience the entire lifecycle of a practical project as done in academic and industry research.
Specific topics addressed in this module will include:
- Graphics pipeline
- 3D reconstruction
- Perceptual system
- Rendering
- HMDs
- IMUs
- Pose tracking
- 3D content creation
- Immersive Audio
Teaching and Learning Methods
2 x Lectures per week: Two weekly lectures covering a wide range of topics and the state-of-the-art in XR.
1 x Lab session per week: The lab session provides hands-on materials for students to get familiar with different XR techniques, which will be helpful in their assignments and projects.
2 x Programming assignment: Two take-home programming assignments for equipping students with the relevant coding skills for XR applications.
1 x Thematic final project: The most exciting part of the second half of the course is for students to develop a novel XR project by leveraging newly acquired skills learned over the module.
Assessment Details
| Assessment Component | Brief Description | Learning Outcomes Addressed | % of Total | Week Set | Week Due |
| Project | See above | LO1, LO2, LO3 | 100% | N/A | N/A |
Reassessment Details
Project.
Contact Hours and Indicative Student Workload
| Contact Hours (scheduled hours per student over full module), broken down by: | 33 hours |
| Lecture | 22 hours |
| Laboratory | 11 hours |
| Tutorial or seminar | 0 hours |
| Other | 0 hours |
| Independent study (outside scheduled contact hours), broken down by: | 72 hours |
| Preparation for classes and review of material (including preparation for examination, if applicable) | 22 hours |
| Completion of assessments (including examination, if applicable) | 50 hours |
| Total Hours | 105 hours |
Recommended Reading List
Fundamentals of Computer Vision in any form, e.g.:
- Computer Vision: Algorithms and Applications, Richard Szeliski, September 3, 2010 draft, 2010 Springer. http://szeliski.org/Book/drafts/SzeliskiBook_20100903_draft.pdf
- Multiple View Geometry in Computer Vision, Second Edition, Richard Hartley and Andrew Zisserman, Cambridge University Press, March 2004. http://www.robots.ox.ac.uk/~vgg/hzbook/Interaction
- Design, 5th Edition, 2019 by Sharp, Preece & Rogers.
Module Pre-requisites
Prerequisite modules: Background in fundamentals of computer vision and graphics (CS7GV1, CS7GV6) or (CSU44052, CSU44053) will be very helpful but not necessarily required.
Other/alternative non-module prerequisites: Knowledge in Python, OpenCV, Unity, Vuforia, ARCore, ARKit will be helpful but not necessarily required.
Module Co-requisites
N/A