Figure 1: Volume renderings of a 64 3 synthetic volume with four different curvature measures. Left to right: first principal curvature κ

Illustrative volume visualization frequently employs non-photorealistic rendering techniques to enhance important features or to suppress unwanted details. However, it is difficult to integrate multiple non-photorealistic rendering approaches into a single framework due to great differences in the individual methods and their parameters. In this paper, we present the concept of style transfer functions. Our approach enables flexible data-driven illumination which goes beyond using the transfer function to just assign colors and opacities. An image-based lighting model uses sphere maps to represent non-photorealistic rendering styles. Style transfer functions allow us to combine a multitude of different shading styles in a single rendering. We extend this concept with a technique for curvaturecontrolled style contours and an illustrative transparency model. Our implementation of the presented methods allows interactive generation of high-quality volumetric illustrations. Categories and Subject Descriptors (according to ACM CCS): I.3.3 [Computer Graphics]: Picture/Image Generation I.3.7 [Computer Graphics]: Three-Dimensional Graphics and Realism

We introduce a flexible combination of volume, surface, and line rendering. We employ object-based edge detection because this allows a flexible parametrization of the generated lines. Our techniques were developed mainly for medical applications using segmented patient-individual volume datasets. In addition, we present an evaluation of the generated visualizations with 8 medical professionals and 25 laypersons. Integration of lines in conventional rendering turned out to be appropriate. Categories and Subject Descriptors (according to ACM CCS): I.3.3 [Computer Graphics]: Picture/Image Generation—Display algorithms; I.3.3 [Computer Graphics]: Picture/Image Generation—Line and curve generation

Volume illustration is a developing trend in volume visualization, focused on conveying volume information effectively by enhancing interesting features of the volume and omitting insignificant data. However, the calculations involved have limited the illustration process to noninteractive rendering. We have developed a new interactive volume illustration system (IVIS) that harnesses the power of programmable graphics processors, and includes a novel approach for feature halo enhancement. This interactive illustration system is a powerful tool for exploration and analysis of volumetric datasets.

Abstract—Volumetric data commonly has high depth complexity which makes it difficult to judge spatial relationships accurately. There are many different ways to enhance depth perception, such as shading, contours, and shadows. Artists and illustrators frequently employ halos for this purpose. In this technique, regions surrounding the edges of certain structures are darkened or brightened which makes it easier to judge occlusion. Based on this concept, we present a flexible method for enhancing and highlighting structures of interest using GPU-based direct volume rendering. Our approach uses an interactively defined halo transfer function to classify structures of interest based on data value, direction, and position. A feature-preserving spreading algorithm is applied to distribute seed values to neighboring locations, generating a controllably smooth field of halo intensities. These halo intensities are then mapped to colors and opacities using a halo profile function. Our method can be used to annotate features at interactive frame rates. Index Terms—Volume rendering, illustrative visualization, halos. 1

In this state-of-the-art report we discuss relevant research works related to the visualization of complex, multivariate data. We discuss how different techniques take effect at specific stages of the visualization pipeline and how they apply to multi-variate data sets being composed of scalars, vectors and tensors. We also provide a categorization of these techniques with the aim for a better overview of related approaches. Based on this classification we highlight combinable and hybrid approaches and focus on techniques that potentially lead towards new directions in visualization research. In the second part of this paper we take a look at recent techniques that are useful for the visualization of complex data sets either because they are general purpose or because they can be adapted to specific problems.

Figure 1: Vector field contours of the electrostatic field around a benzene molecule from different view directions. Using our new technique allows to select those stream lines which have tangent direction and osculating plane perpendicular to the current view direction in the seeding point. We describe an approach to define contours of 3D vector fields and employ them as an interactive flow visualization tool. Although contours are well-defined and commonly used for surfaces and 3D scalar fields, they have no straightforward extension in vector fields. Our approach is to extract and visualize specific stream lines which show the most similar behavior to contours on surfaces. This way, the vector field contours are a particular set of isolated stream line segments that depend on the view direction and few additional parameters. We present an analysis of the usefulness of vector field contours by demonstrating their application to linear vector fields. In order to achieve interactive visualization, we develop an efficient GPU-based implementation for real-time extraction and rendering of vector field contours. We show the potential of our approach by applying it to a number of example data sets. Index Terms: I.3.3 [Computer Graphics]:,—Contours, Flow Visualization 1

Abstract. During real-time medical data exploration using volume rendering, it is often difficult to enhance a particular region of interest without losing context information. In this paper, we present a new illustrative technique for focusing on a user-driven region of interest while preserving context information. Our focal probes define a region of interest using a distance function which controls the opacity of the voxels within the probe, exploit silhouette enhancement and use non-photorealistic shading techniques to improve shape depiction. 1

Abstract—Conveying shape using feature lines is an important visualization tool in visual computing. The existing feature lines (e.g., ridges, valleys, silhouettes, suggestive contours, etc.) are solely determined by local geometry properties (e.g., normals and curvatures) as well as the view position. This paper is strongly inspired by the observation in human vision and perception that a sudden change in the luminance plays a critical role to faithfully represent and recover the 3D information. In particular, we adopt the edge detection techniques in image processing for 3D shape visualization and present Photic Extremum Lines (PELs) which emphasize significant variations of illumination over 3D surfaces. Comparing with the existing feature lines, PELs are more flexible and offer users more freedom to achieve desirable visualization effects. In addition, the user can easily control the shape visualization by changing the light position, the number of light sources, and choosing various light models. We compare PELs with the existing approaches and demonstrate that PEL is a flexible and effective tool to illustrate 3D surface and volume for visual computing. Index Terms—Surface and volume illustration, illumination, photic extremum lines (PELs), silhouettes, suggestive contours, ridges and valleys, digital geometry processing. 1

ge (or slice), where each of the data values represents a pixel (Figure 1.1). This alternative view is motivated by the slice oriented, traditional way physicians look at a volumetric dataset. It is denoted by a matrix V = # XYZ with X rows, Y columns and Z slices, which represents a discrete grid of volume elements (or voxels) v # {1, ..., X} {1, ..., Y } {1, ..., Z}. For each 1 voxel we denote by I(v) : N # its gray value, which, for example, reflects the x-ray intensity in CT volume data. Of course, the voxel value can be a vector to represent the object properties in some specific fields (e.g. computational fluid dynamics). Each voxel is characterized by its position in the 3D grid. Medical volume data obtained from MRI (Magnetic Resonance Imaging) and CT-scanners (Computed-Tomography) typically are anisotropic with an equal sampling density in x and y direction but a coarser density along the z direction. The size is typically about more than 100 slices or more with 5