Specialization compulsory (Computer Graphics and Virtual Reality)
This subject has not requirements
This course covers the techniques, algorithms and data structures used to acquire, represent and query geometric models of solids and surfaces. The course will cover various modeling techniques, including boundary representations, implicit representations, instantiation and Boolean combinations of shapes, as well as procedural modeling. We will also discuss effective data structures for representing various types of objects, as well as the process of acquiring models from real objects.
Person in charge
Carlos Andujar Gran (
Alvaro Vinacua Pla (
Nuria Pelechano Gomez (
Technical Competences of each Specialization
Computer graphics and virtual reality
CEE1.1 - Capability to understand and know how to apply current and future technologies for the design and evaluation of interactive graphic applications in three dimensions, either when priorizing image quality or when priorizing interactivity and speed, and to understand the associated commitments and the reasons that cause them.
Generic Technical Competences
CG1 - Capability to apply the scientific method to study and analyse of phenomena and systems in any area of Computer Science, and in the conception, design and implementation of innovative and original solutions.
Appropiate attitude towards work
CTR5 - Capability to be motivated by professional achievement and to face new challenges, to have a broad vision of the possibilities of a career in the field of informatics engineering. Capability to be motivated by quality and continuous improvement, and to act strictly on professional development. Capability to adapt to technological or organizational changes. Capacity for working in absence of information and/or with time and/or resources constraints.
CTR6 - Capacity for critical, logical and mathematical reasoning. Capability to solve problems in their area of study. Capacity for abstraction: the capability to create and use models that reflect real situations. Capability to design and implement simple experiments, and analyze and interpret their results. Capacity for analysis, synthesis and evaluation.
Foundations of 3D modeling
Elements of a geometric modeling system. Solid models. Closed, bounded and regular sets of points. Two-manifold surfaces. Abstraction levels in geometric modeling.
Boundary representation (BRep)
Polyhedra. Cells, shells, faces, loops, edges and vertices. Genus of a surface. Euler equation for polyhedra. Incidence relationships. Creation of BRep models. Sweep. Boolean operations.
Space decomposition models
Voxelizations. Octrees. Classic, Face and Extended octrees. Octree representation. Basic operations on octrees.
Scalar fields. Surface reconstruction from scalar fields. Blobby molecules, metaballs and soft objects.
Data structures for triangle meshes
Euler equation for triangle meshes. Face-based, Vertex-based and edge-based representations. The half-edge data structure. APIs for geometry processing.
Geometric tests and queries
Estimating normal and tangent planes at vertices of polygonal meshes. Discrete curvature at mesh vertices. Mesh quality. Non-selfintersection test.
Fractals. Lindenmayer systems (L-systems). Stochastic and parametric grammars. Shape grammars. Generative modeling.
Pipeline for the acquisition of 3D models. Technologies. Registration and merge.
Material will be presented in lectures along the term. You are expected to conduct complementary readings and exercises
will also be assigned on occasion, to be presented at a later date or turned in.
Implementation of selected algorithms
A selection of relevant algorithms will be assigned to implement in Lab sessions and on your own. You may be required to
present your solution to the class. You must turn in fully functional source code that runs in the indicated platform. Usual languages are C++ and Python.
At the end of the term you will have a final exam, which may be a take-home.
At the middle of the term you will have a partial exam, which may be a take-home.
The teaching methodology will be based based on weekly theory classes and lab classes. Course concepts will be introduced in the theory classes. Exercises will be used to consolidate these concepts, which will be further developed in the lab sessions.
The lab sessions basically involve the teacher presenting the guidelines for the practical work (split by sessions) and the concepts bearing on the software to be used. Students will complete the design and programming of the various applications bearing on the course contents. The exercises will be carried out individually.
Partial: mark based on the student's performance in the partial exam
Exam: mark based on the student's performance in the final exam
Lab: grade stem from the student's implementations of selected algorithms (including occasionally their presentation of their
solution in a laboratory class)
The final grade for the course will be computed as: