Scientific Volume and Flow Illustration

Abstract

Data visualization is the creation of visual representations of large data sets in order to facilitate exploration and understanding. Accurately and automatically conveying the structure of a volume model is a problem not fully solved by existing volume rendering approaches. Standard volume rendering approaches create images that may match the appearance of translucent materials in nature, but may not embody important structural details. We have introduced the volume illustration approach, combining the familiarity of a physics-based illumination model with the ability to enhance important features using illustration-inspired rendering techniques. Since features to be enhanced are defined on the basis of higher-order model characteristics rather than volume sample value, the application of volume illustration techniques requires less manual tuning than the design of a good transfer function. Volume illustration provides a flexible unified framework for enhancing structural perception of volume models through the amplification of features, the addition of illumination effects, and the application of procedural textures. These methods can be extended to capture the movements of features in time-varying volumes. I show examples from medicine and atmospheric physics.