Monday

Tutorial 3
State of the Art in Data Representation for Visualization (Full day) [proposal]

Arie Kaufman, State University of New York at Stony Brook
Boaquan Chen, University of Minnesota at Twin Cities
Klaus Mueller, State University of New York at Stony Brook
Amitabh Varshney, University of Maryland

This course provides a unified framework, based on principles from signal processing, to define, compare, and contrast different graphics representations available to date, including polygons, points, volumes, images, and free-form representations. We will provide a comprehensive overview of these representations and their hybrids using a unified conceptual framework. We will further demonstrate several practical examples of major applications, available tools, and techniques. The course provides a fresh look on the subject and examines current challenges and future research directions in data representation for visualization.

The topics covered in the cource include sampling theories and antialiasing techniques, volumetric splatting, point-based representation and rendering, polygon-based level-of-details techniques, differential points, hybrid methods combining image-, point- and polygon- based techniques. The course material is moderately advanced. Basic knowledge of computer graphics rendering (including basic volume rendering), sampling theories and mathematics is recommended.