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Transfer Functions

Multi-dimensional Transfer Functions and Direct Manipulation Widgets - Most direct volume renderings produced today employ one-dimensional transfer functions, which assign color and opacity to the volume based solely on the single scalar quantity which comprises the dataset. Though they have not received widespread attention, multi-dimensional transfer functions are a very effective way to extract materials and their boundaries for both scalar and multivariate data. However, identifying good transfer functions is difficult enough in one dimension, let alone two or three dimensions. SCI researchers have created a set of direct manipulation widgets that make specifying such transfer functions intuitive and convenient. We have also investigated how to use modern graphics hardware to both interactively render with multi-dimensional transfer functions and to provide interactive shadows for volumes. The transfer functions, widgets, and hardware combine to form a powerful system for interactive volume exploration.

Examples of multi-field volume classified using a GTF. The dataset is the Visible Male Color Cryosection, courtesy of the National Institutes of Health. Dataset size is 256 cubed.

Gaussian Transfer Functions - Volume rendering is a flexible technique for visualizing dense 3D volumetric datasets. A central element of volume rendering is the conversion between data values and observable quantities such as color and opacity. This process is usually realized through the use of transfer functions that are precomputed and stored in lookup tables. For multidimensional transfer functions applied to multivariate data, these lookup tables become prohibitively large. We propose the direct evaluation of a particular type of transfer functions based on a sum of Gaussians. Because of their simple form (in terms of number of parameters), these functions and their analytic integrals along line segments can be evaluated efficiently on current graphics hardware, obviating the need for precomputed lookup tables. We have adopted these transfer functions because they are well suited for classification based on a unique combination of multiple data values that localize features in the transfer function domain. We apply this technique to the visualization of several multivariate datasets (CT, cryosection) that are difficult to classify and render accurately at interactive rates using traditional approaches.

Curvature-based non-photorealistic volume rendering. The two small images depict the contribution of thickness-controlled contours (top) and ridge and valley emphasis (bottom). The large image shows the combination of the two effects.

Curvature Based - Direct volume rendering of scalar fields uses a transfer function to map locally measured data properties to opacities and colors. The domain of the transfer function is typically the one-dimensional space of scalar data values. We have advanced the use of curvature information in multi-dimensional transfer functions, with a methodology for computing high-quality curvature measurements. The proposed methodology combines an implicit formulation of curvature with convolution-based reconstruction of the field. Curvature-based transfer functions are shown to extend the expressivity and utility of volume rendering through contributions in three different application areas: non-photorealistic volume rendering, surface smoothing via anisotropic diffusion, and visualization of isosurface uncertainty.

• Publications -- Transfer Functions
• Image Gallery -- Transfer Functions