Module Information

Module Identifier
Module Title
Academic Year
Semester 1
Only available to students registered for a Masters' degree in the Faculty of Science.
Other Staff

Course Delivery

Delivery Type Delivery length / details
Lecture 30 x 1 hour lecture


Assessment Type Assessment length / details Proportion
Semester Exam 2 Hours   Written exam  70%
Semester Assessment Presentation  of a selected topic covered. Partial peer assessment.  30%
Supplementary Assessment Resubmission of failed components (or equivalent).  100%

Learning Outcomes

On successful completion of this module students should be able to:

Demonstrate and understanding of the fundamental techniques for 3D data acquisition and analysis.

Show the knowledge of 3D data capture and extraction of information from 3D data.

Demonstrate awareness of relevant literature on 3D imaging and analysis.

Demonstrate capability to communicate concepts and ideas within 3D data processing.

Brief description

This module will equip students with the fundamental concepts and technniques for 3D data acquisition and analysis mainly through lecture delivery, programme demonstration and laboratory practicals. It will then help the students gain clear understanding of the interactions between these two components and inspiration to develop novel hardware and software for 3D data acquisition and processing in the real world.


The lectures will introduce the ideas, concepts and techniques. Students will be expected to read further from the bibliography. This will be assessed by written exam and by delivery of a presentation on a topic covered in class.

1. Introduction
Necessity and possibility to capture 3D data, module assessment, reading list.

2. Fundamental mathematics.
Vector, matrix, eigenvalue and eigenvector, singular value decomposition, least squares, non-linear squares, projective geometry.

3. 3D programming concepts.
Introduction, scene graph, geometry creation (eg. using Java3D, OpenGL, etc).

4. Camera calibration and correction.
Camera modeling, distortion modeling, rig based method, projective geometry based method.

5. Triangulation based data acquisition and stereo vision.
Configurations, principles, issues.

6. Time of flight based data acquisition.
Configurations, principles, issues.

7. Motion capture.
Configurations, principles, issues.

8. 3D data visualisation.
Points, mesh, volumetric, saliency, simplification.

9. Segmentation and clustering.
Region growing, minimum spanning tree, K-means, hierarchical, consensus clustering techniques.

10. 3D data matching.
Point combination based, feature based, population based, local and global registration.

11. 3D data integration.
Point based method, mesh based method volumetric method.

12. Object classification and recognition.
Representation, local and global feature based matching, content-based image retrieval.

Module Skills

Skills Type Skills details
Application of Number Inherent to subject.
Communication Class discussion, presentation.
Improving own Learning and Performance Inherent to subject.
Information Technology Inherent to subject.
Personal Development and Career planning Encourages students to see roles in subject for career and personal development.
Problem solving Inherent to subject
Research skills Presentation, after class reading.
Subject Specific Skills Digital data acquisition and processing about the real world.
Team work

Reading List

Essential Reading
Cyganek, B.[Boguslaw], Siebert, J.P.[J. Paul], (March 2009) An Introduction to 3D Computer Vision Techniques and Algorithms Wiley Primo search Nick Pears, Yonghuai Liu, Pete Bunting (editors) (2012) 3D Imaging, Analysis and Applications (text book) Springer Primo search
Supplementary Text
O. Faugeras (1993) Three-Dimensional Computer Vision MIT Press Primo search


This module is at CQFW Level 7