Examination arrangement

Course content

Solid modeling: boundary representation (B-rep), constructive solid geometry, octree etc.
Polygon mesh models and their processing.
Geometric algorithms and computational geometry.
Homogeneous transformations and projective geometry.
Geometric estimation.
Feature extraction from geometric data
Algorithmic automation.

Learning outcome

Knowledge

- The candidate is in the forefront of knowledge within the fields of the selected geometric models and algorithms.
- The candidate can evaluate the expediency and application of geometric models and algorithms in manufacturing-oriented research and development projects.

Skills

- The candidate can apply the selected geometric models and algorithms to tackle his/her own research problems.
- The candidate can implement computational routines for geometric data preprocessing.

General competence

- The candidate has the ability to communicate and lead discussions on recent research about geometric models and algorithms, as well as their application to manufacturing-related problems.
- The candidate has the ability to evaluate and critique geometric models and algorithms.

Learning methods and activities

The course is based on regular on-campus seminars centered around the individual topics and backed by the corresponding reading materials. Every seminar will contain an in-depth group discussion.

Coursework requirements:
- On a given topic, prepare and give one seminar consisting of an introductory lecture followed by a group discussion.
- Attend at least 75% of the seminars.

Further on evaluation

Candidates must provide one paper where the applicability of the topics of the course to her/his own thesis work is thoroughly discussed. The paper should be in the form of a scientific paper, include examples of the application of geometric computing, and preferably constitute a basis for a future publishable scientific paper.

Grading scale: Pass/Fail

Course materials

Selected research papers and book chapters.