The application of transport theory to visualization of 3-D scalar data fields

Abstract

This paper describes a general visualization model for 3-D scalar data fields based on linear transport theory that contains common volume rendering models as special cases and/or approximations. The concept of ‘‘virtual’’ particles for the extraction of information from data fields is introduced. The role of different types of interaction of the data field with those particles such as absorption, scattering, source, and color shift are discussed and demonstrated. Special attention is given to possible tools for the enhancement of interesting data features. Random distortions or noise of the data field are mapped onto the image plane in a well-defined way such that picture processing methods can be used to reconstruct the appearance of the undistorted data set. Random texturing can provide visual insights as to the magnitude and distribution of deviations of related data fields, e.g., originating from analytic models and measurements, or in the noise content of a given data field. Hidden symmetries of a data set can often be identified visually by allowing it to interact with a preselected beam of ‘‘physical’’ particles with the attendant appearance of characteristic structural effects such as channeling.