Undergraduate Course: Computer Graphics: Geometry and Simulation (INFR11241)
Course Outline
| School | School of Informatics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Year 4 Undergraduate) |
Availability | Available to all students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | This course introduces classic and state-of-the-art methodology in computer graphics. We will focus on methods and best practices in geometry, physical simulation, and geometric deep learning, which are the basic building blocks for downstream applications such as animation, industrial design, game engineering, structural analysis, AR/VR, and medical imaging. Our curriculum will cover basic representations of shapes, geometric optimization, analysis, and principles of robust digital simulation of physical scenes. The techniques employed will involve classical numerical analysis up to deep learning.
The course will include programming tasks to implement a few key algorithms in geometry processing, geometric learning, and physical simulation, to the extent that they can independently run and be analysed on modest open-source data.
This course (CGGS) and Computer Graphics: Rendering (CGR) are both courses that require no previous knowledge of computer graphics. These two courses may be taken independently or together. CGGS focusses on the representation, processing and dynamics of 3D objects in the virtual world while CGR focusses on the rendering of virtual worlds as photo-realistic images. |
| Course description |
Delivery Method:
The course will be delivered through a combination of: (1) live lectures, (2) practical labs, (3) tutorials, and (4) an online discussion forum.
Content / Syllabus:
The exact set of methods and algorithms explored in the course will vary slightly from year to year, but will include many of the following topics:
- Overview: geometry and simulation in digital applications.
- Elemental digital representations of geometry: simplicial meshes, point clouds, voxelizations, implicit functions, neural fields
- Elementary principles of discrete simulation: strain and stress tensors, force equations, time integration.
- Geometry acquisition and reconstruction: classical (least-squares based) and modern (neural-network based) algorithms.
- Discrete shape analysis: curvatures, topology, differential operators.
- Finite-element spaces for simulation and analysis, including basic PDEs like elasticity, Stokes equation, and Poisson equation.
- Simulation of rigid bodies with collisions.
- Modern deep-learning techniques for geometry and simulation, such as physics-informed neural networks, graph neural networks, and implicit representations (e.g., signed distance fields)
- Practical aspects of implementation and debugging in geometry: we will discuss how to identify, critically analyse, and improve performance in geometric methods, with emphasis on pitfalls and basic principles in implementation and design.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | Enrolled students are assumed to have:
- Basic algebra and geometry (e.g., vectors, rotations, trigonometry etc.). We will publish a concrete list of recommended concepts.
- Physics to understand Newton's Laws of Motion.
Students should be comfortable with programming in Python. |
Information for Visiting Students
| Pre-requisites | Enrolled students are assumed to have:
- Basic algebra and geometry (e.g., vectors, rotations, trigonometry etc.). We will publish a concrete list of recommended concepts.
- Physics to understand Newton's Laws of Motion.
Students should be comfortable with programming in Python. |
| High Demand Course? |
Yes |
Course Delivery Information
| Not being delivered |
Learning Outcomes
On completion of this course, the student will be able to:
- identify and isolate geometric problems and produce an algorithm to fit
- implement basic method in geometry and simulation which would be adequate for either further re-search or as an initial knowledge to find work in the relevant industry (example of advanced industry that uses this core knowledge: 3D printing, architectural design, medical imaging, weather simulations, robotics)
- use software and tools (e.g., Python and C++) to implement geometric algorithms and test their results
- identify, fix, and test for possible issues with geometric algorithms in a way that transcends just 'soft-ware bugs' but rather problems with a geometric context
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Reading List
The course will be self-contained with no required books;
A list of useful resources:
1. Polygon Mesh Processing (http://www.pmp-book.org/)
2. A Sampler of Useful Computational Tools for Applied Geometry, Computer Graphics, and Image Processing (https://www.routledge.com/A-Sampler-of-Useful-Computational-Tools-for-Applied-Geometry-Computer/Cohen-Or-Greif-Ju-Mitra-Shamir-Sorkine-Hornung-Zhang/p/book/9781498706285#googlePreviewContainer).
3. Physics for Game Developers, 2nd Edition, By David Bourg, Bryan Bywalec.
4. Physically Based Modeling: Principles and Practice (Online Siggraph '97 Course notes) |
Additional Information
| Graduate Attributes and Skills |
Knowledge integration: This course will integrate and apply knowledge from more basic courses (e.g., math) and teach basic techniques that will be useful in latter or parallel courses (robotics, vision, bioinformatics).
Problem-solving skills: The students will develop their problem-solving skills by experimenting empirically with complex algorithms and their possible issues. The 'debugging geometry' subject is particularly useful in this aspect.
Critical and analytical thinking: The students will have to deeply understand and analyse the presented methodology so they could bring it into implementation in the practicals. |
| Keywords | Computer Graphics,Geometry Processing,Game Physics,Simulation |
Contacts
| Course organiser | Dr Amir Vaxman
Tel: (0131 6)50 8286
Email: avaxman@inf.ed.ac.uk |
Course secretary | Mrs Helen Tweedale
Tel: (0131 6)50 2692
Email: Helen.Tweedale@ed.ac.uk |
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