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Advanced Material Appearence Modelling

 Title: Advanced Material Appearence Modelling

Tutorial presenter: Michal Haindl and Jiri Filip

Time: Monday 23/5: 8:30-12:30


Textures are in graphics commonly used as paradigm of material appearance. This introductory course aims to provide overview of possible texture representations, methods of their acquisition, analysis, synthesis, and modelling as well as techniques of their editing, visualization, and quality evaluation. Methods’ properties and key target applications will be discussed.

Multidimensional visual texture is the appropriate paradigm for physically correct material visual properties representation. The course will presents recent advances in texture modelling methodology applied in computer vision, pattern recognition, computer graphics, and virtual/augmented reality applications. Contrary to previous courses on material appearance we focus on materials whose nature allows exploiting of texture modelling approaches.

This topic is introduced in the wider and complete context of pattern recognition and image processing. It comprehends modelling of multi-spectral images and videos which can be accomplished either by a multi-dimensional mathematical models or sophisticated sampling methods from the original measurements. The key aspects of the topic, i.e., different multi-dimensional data models with their corresponding benefits and drawbacks, optimal model selection, parameter estimation and model synthesis techniques are discussed. These methods produce compact parametric sets that allow not only to faithfully reproduce material appearance, but are also vital for visual scene analysis, e.g., texture segmentation, classification, retrieval etc.

Special attention is devoted to recent most advanced trends towards Bidirectional Texture Function (BTF) modelling, used for materials that do not obey Lambertian law, whose reflectance has non-trivial illumination and viewing direction dependency. BTFs recently represent the best known effectively applicable textural representation of the most real-world materials’ visual properties. The techniques covered include efficient Markov random field-based algorithms, intelligent sampling algorithms, spatially-varying reflectance models and challenges with their possible implementation on GPU.

The course also deals with proper data measurement, visualization of texture models in virtual scenes, visual quality evaluation feedback, as well as description of key industrial and research applications. We will discuss options which type of material representation is appropriate for required application, what are its limits and possible modelling options, and what the biggest challenges in realistic modelling of materials are.

For a more detialed description, please look at this file.