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Learning outcomes |
This course focuses on the review and understanding of current geometric computing (computational geometry) research techniques and problems, and its application in science, engineering, and business.
The general objectives of the course are:
- Provide doctoral students with an insight into the geometric computing;
- Provide doctoral students with strong skills in scientific research methodologies,
- Preparing students for a career in science and / or academic career.
With respect to the specific learning objectives, at the end of the course students should be able to describe and implement at least one algorithm for:
- Computing the convex hull of a set of points;
- Geometric search (e.g., K-d tree);
- Construction of a cubic Bézier surface;
- Reconstruction of a triangulated surface from a point cloud generated by a 3D scanner.
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Main Bibliography |
- A. Bronstein et al., “Numerical Geometry of Non-Rigid Shapes”, Springer-Verlag, 2008.
- Warren and H. Weimer, “Subdivision Methdos for Geometric Design”, Morgan Kaufman, 2002.
- N. Dodgson et al., “Advances in Multiresolution for Geometric Modelling”, Springer-Verlag, 2005.
- A. Gomes, I. Voiculescu, J. Jorge, B. Wyvill, and C. Galbraith, “Implicit Curves and Surfaces: Mathematics, Data Structures, and Algorithms”, Springer-Verlag, 2009.
- S. Jia and J. Li, “3D Shape Analysis: Construction, Classification and Matching”, VDM Verlag, 2008.
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