Multi-fields are widely used in areas ranging from physical simulations to medical imaging. Illustrative visualization techniques can help to effectively communicate features of interest found in a given field. Current techniques for multi-field visualization are mostly focused on showing subsets of local attributes such as single values or vector directions, e.g., using colors, texture, streamlines or glyphs. Instead, we present an approach based on highlighting areas with similar characteristics, considering all attributes of the field.

Our approach is example-based and interactive. A user simply selects a point within the field, upon which the system automatically derives the characteristic combination of attributes for that point. Our system then automatically creates a visualization highlighting areas within the field which are similar to the example point with respect to these characteristics. The visualizations are presented using sparse, illustrative techniques, using contours and colors to clearly delineate and identify separate areas. Users can interact with the visualizations in real-time, by moving the example point or, optionally, by changing the characteristics or adjusting other parameters used to determine similarity.

Statistical shape modeling is a widely used technique for the representation and analysis of the shapes and shape variations present in a population. A statistical shape model models the distribution in a high dimensional shape space, where each shape is represented by a single point.

We present a design study on the intuitive exploration and visualization of shape spaces and shape models. Our approach focuses on the dual-space nature of these spaces. The high-dimensional shape space represents the population, whereas object space represents the shape of the 3D object associated with a point in shape space.

A 3D object view provides local details for a single shape. The high dimensional points in shape space are visualized using a 2D scatter plot projection, the axes of which can be manipulated interactively. This results in a dynamic scatter plot, with the further extension that each point is visualized as a small version of the object shape that it represents. We further enhance the population-object duality with a new type of view aimed at shape comparison. This new “shape evolution view” visualizes shape variability along a single trajectory in shape space, and serves as a link between the two spaces described above.

Our three-view exploration concept strongly emphasizes linked interaction between all spaces. Moving the cursor over the scatter plot or evolution views, shapes are dynamically interpolated and shown in the object view. Conversely, camera manipulation in the object view affects the object visualizations in the other views. We present a GPU-accelerated implementation, and show the effectiveness of the three-view approach using a number of real-world cases. In these, we demonstrate how this multi-view approach can be used to visually explore important aspects of a statistical shape model, including specificity, compactness and reconstruction error.

Deformation is a topic of interest in many disciplines. In particular in medical research, deformations of surfaces and even entire volumetric structures are of interest. Clear visualization of such deformations can lead to important insight into growth processes and progression of disease.

We present new techniques for direct focus+context visualization of deformation fields representing transformations between pairs of volumetric datasets. Typically, such fields are computed by performing a non-rigid registration between two data volumes. Our visualization is based on direct volume rendering and uses the GPU to compute and interactively visualize features of these deformation fields in real-time. We integrate visualization of the deformation field with visualization of the scalar volume affected by the deformations. Furthermore, we present a novel use of texturing in volume rendered visualizations to show additional properties of the vector field on surfaces in the volume.

Non-photorealistic techniques are usually applied to produce stylistic renderings. In visualization, these techniques are often able to simplify data, producing clearer images than traditional visualization methods. We investigate the use of particle systems for visualizing volume datasets using non-photorealistic techniques. In our VolumeFlies framework, user-selectable rules affect particles to produce a variety of illustrative styles in a unified way. The techniques presented do not require the generation of explicit intermediary surfaces.