Real-Time Rendering of Massive Models

Credits
6
Types
Elective
Requirements
This subject has not requirements , but it has got previous capacities
Department
CS
This course will introduce the various techniques that enable the interactive visualization and handling of very complex objects and scenes. While there has been a leap in the power of graphics hardware, more complex datasets are generated through advances in 3D modeling, simulation and data capture. Thus, the need to deal with these massive models arises in fields such as scientific visualization, CAD, cultural heritage, videogame engines and others. The students will be exposed to hierarchical representation of scenes, model's simplification and visibility culling. As a result, they will gain a global view of the problem and a wide knowledge of the current solutions.

Weekly hours

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

Objectives

  1. Using Hierarchical Geometric Models for the display of very large models.
    Related competences: CTR5, CTR6, CEE1.1, CG3,
  2. Simplification algorithms for triangle meshes.
    Related competences: CTR5, CTR6, CEE1.1, CG3,
  3. Visibility computation algorithms
    Related competences: CTR5, CTR6, CEE1.1, CG3,
  4. Interactive navigation in complex environments
    Related competences: CB8, CB9, CTR5, CTR6, CEE1.1, CG3,

Contents

  1. Hierarchical geometric models
    Algorithms for space subdivision (regular grids, octrees, BSP trees, Kd-trees), scene subdivision (BVHs) and external memory-based data structures.
  2. Mesh representation data structures
    Triangle and polyognal mesh representation: Independent face set, Indexed face set, Adjacency lists, Winged edge, Half edge, Corner table.
  3. Simplification of triangle meshes
    Introduction to the basic concepts, operators and error metrics used in geometry and topology-based simplification. Its application to appearance-preserving simplification and out-of-core gigantic model simplification.
  4. Level of detail
    Introduction to object level of detail (LOD) and its application to complex scenes (time critical rendering). Strategies for LOD: Discrete, Continuous, or View-Dependent. Popping effect prevention.
  5. Visibility computation
    Introduction to the basic concepts and algorithms for visibility computation, including visibility preprocessing, point and region visibility, and visibility computation using the GPU. PVS compression.
  6. Interactive navigation in complex environments
    How to estructure gigantic data for out-of-core visualization of huge scenes. Use of view dependent visualization. Algorithms for collision detection in gigantic models.

Activities

Activity Evaluation act


Hierarchical Geometric Models


Objectives: 1
Contents:
Theory
12h
Problems
0h
Laboratory
6h
Guided learning
0h
Autonomous learning
27h

Simplification algorithms for triangle meshes


Objectives: 2
Theory
8h
Problems
0h
Laboratory
4h
Guided learning
0h
Autonomous learning
18h

Visibility computation algorithms


Objectives: 3
Contents:
Theory
8h
Problems
0h
Laboratory
4h
Guided learning
0h
Autonomous learning
18h

Interactive navigation in complex environments


Objectives: 4
Contents:
Theory
8h
Problems
0h
Laboratory
4h
Guided learning
0h
Autonomous learning
18h

Paper presentation

Each student has to prepare the corresponding presentation and a supporting document, which have to sent to the course coordinator before the session.
Objectives: 1 2 3 4
Week: 15
Theory
0h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
0h

Exercises

Set of exercises raised during the course to assess knowledge acquisition by students during the course.

Theory
0h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
9h

Teaching methodology

This course is structured in three session types:

* T sessions (theory): presentation by the corresponding professor. The professor will ask the students to do some short exercises on the subjects covered in these sessions.

* D sessions (discussion): sessions conducted by the professor, in which some students will solve exercises or present previously distributed papers. Each student has to prepare the corresponding presentation and a supporting document, which have to sent to the course coordinator before his D session.

* L sessions (lab): in these sessions students will have to solve practical problems programmings some of the algorithms presented in the theory sessions. L sessions will start with a short lecture section.

Evaluation methodology

The final qualification is computed as:

FinalQualification = 0.25 * ShortExercises + 0.25 * DPresentation + 0.5 * LabQualification

where:

* ShortExercises represents the short problems the instructor will ask during T sessions.

* DPresentation is the presentation the students will do on a paper selected from a list.

* LabQualification will be the qualification obtained by the students in the L sessions.

Bibliography

Basic

Complementary

  • Massive model visualization techniques: course notes. - David Kasik, Andreas Dietrich, Enrico Gobbetti, Fabio Marton, Dinesh Manocha, Philipp Slusallek, Abe Stephens, and Sung-Eui Yoon, ACM SIGGRAPH 2008 classes (SIGGRAPH '08), 2008.