ADVANCED 3D MODELING

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Credits
6
Types
Specialization compulsory (Computer Graphics and Virtual Reality)
Requirements
This subject has not requirements

Department
CS
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.

Teachers

Person in charge

  • Carlos Andujar Gran ( )

Others

  • Alvaro Vinacua Pla ( )

Weekly hours

Theory
2
Problems
0
Laboratory
1
Guided learning
0
Autonomous learning
5

Competences

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

Generic

  • 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.

Transversal Competences

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.

Reasoning

  • 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.

Contents

  1. 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.
  2. 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.
  3. Subdivision surfaces
    Subdivision surfaces. Interpolation and approximation. Update rule. Classification. Catmull-Clark subdivision.
  4. CSG models
    Constructive Solid Geometry. CSG trees. Basic operations. Point-inside-CSG test.
  5. Space decomposition models
    Voxelizations. Octrees. Classic, Face and Extended octrees. Octree representation. Basic operations on octrees.
  6. Implicit modeling
    Scalar fields. Surface reconstruction from scalar fields. Blobby molecules, metaballs and soft objects.
  7. 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.
  8. Geometric tests and queries
    Estimating normal and tangent planes at vertices of polygonal meshes. Discrete curvature at mesh vertices. Mesh quality. Non-selfintersection test.
  9. Procedural modeling
    Fractals. Lindenmayer systems (L-systems). Stochastic and parametric grammars. Shape grammars. Generative modeling.
  10. Geometry acquisition
    Pipeline for the acquisition of 3D models. Technologies. Registration and merge.

Activities

Lectures

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.
Theory
39
Problems
0
Laboratory
0
Guided learning
4
Autonomous learning
52

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.
Theory
0
Problems
0
Laboratory
13
Guided learning
5
Autonomous learning
33

Teaching methodology

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.

Evaluation methodology

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:

Final Grade = 0.4 Exam + 0.3 Partial + 0.3 Lab

Bibliografy

Basic:

Complementary: