Visibility-driven transfer functions

Direct volume rendering is an important tool for visualizing complex data sets. However, in the process of generating 2D images from 3D data, information is lost in the form of attenuation and occlusion. The lack of a feedback mechanism to quantify the loss of information in the rendering process makes the design of good transfer functions a difficult and time consuming task. In this paper, we present the notion of visibility-driven transfer functions, which are transfer functions that provide a good visibility of features of interest from a given viewpoint. To achieve this, we introduce visibility histograms. These histograms provide graphical cues that intuitively inform the user about the contribution of particular scalar values to the final image. By carefully manipulating the parameters of the opacity transfer function, users can now maximize the visibility of the intervals of interest in a volume data set. Based on this observation, we also propose a semi-automated method for generating transfer functions, which progressively improves a transfer function defined by the user, according to a certain importance metric. Now the user does not have to deal with the tedious task of making small changes to the transfer function parameters, but now he/she can rely on the system to perform these searches automatically. Our methodology can be easily deployed in most visualization systems and can be used together with traditional 1D opacity transfer functions based on scalar values, as well as with multidimensional transfer functions and other more sophisticated rendering algorithms.