In the past a lot of work has been invested in various aspects of an interactive visualization of CFD simulation data. This includes e.g. increasing the rendering speed and responsiveness of complex visualizations, using and enhancing multimodal user interfaces, and incorporating parallel approaches for an efficient extraction of flow properties and their respective visual representation. Still, only few projects combine the significant advances in these areas. In this paper, we describe our software framework ViSTA FlowLib, which facilitates merging current research results of various related areas. This is done by connecting dedicated sub-modules with clearly defined responsibilities through appropriate interfaces, whilst implementing sensible default behavior. ViSTA FlowLib combines efficient rendering techniques and a parallel computation of the visualization with intuitive multimodal user interfaces to allow for an interactive exploration of unsteady fluid flows in a virtual environment. Special care has been taken to achieve a high scalability in respect to computing power, projection technology, and input-output device availability.

An effective means for flow visualization is the depiction of particle trajectories. When rendering large amounts of these pathlines, standard visualization techniques suffer from several weaknesses, ranging from ambiguous depth perception to high geometrical complexity and decreased interactivity. This paper addresses these problems by choosing a novel approach to pathline visualization in 3D space, which we call Virtual Tubelets. It employs billboarding techniques in combination with suitable textures to create the illusion of threedimensional tubes, which efficiently depict the particles' trajectories, while still maintaining interactive frame rates. Certain issues concerning virtual environments and immersive displays with multiple projection screens are resolved by choosing an appropriate orientation for the billboards. The use of modern, programmable graphics hardware allows for an additional speed-up of the rendering process and a further improvement of the image quality. This results in a nearly perfect illusion of tubular geometry, including plausible intersections and consistent illumination with the rest of the scene. To prove the efficiency of our approach, rendering speed and visual quality of Virtual Tubelets and conventional, polygonal tube renderings are compared.

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The analysis of unsteady phenomena is an important topic for scientific visualization. Several time-dependent visualization techniques exist, as well as solutions for dealing with the enormous size of time-varying data in interactive visualization. Many current visualization toolkits support displaying time-varying data sets. However, for the interactive exploration of time-varying data in scientific visualization, no common time model that describes the temporal properties which occur in the visualization process has been established. In this work, we propose a general time model which classifies the time frames of simulation phenomena and the connections between different time scales in the analysis process. This model is designed for intuitive interaction with time in visualizationapplications for the domain expert as well as for the developer of visualization tools. We demonstrate the benefits of our model by applying it to two use cases with different temporal properties.

Figure 1: Camera setup optimization sequence for a five sided CAVE with four cameras. The camera positions were constrained to remain on the open top side of the CAVE. Notice the increased point sample density in head height to improve the head-tracking robustness. ABSTRACTWe propose a method to determine the optimal camera alignment for a tracking system with multiple cameras by specifying the volume to be tracked and an initial camera setup. We use optimization strategies based on methods usually employed for solving nonlinear systems of equations. All approaches are fully automatic and take advantage of modern graphics hardware since we also implement a GPU-based, accelerated visibility test. The algorithm automatically optimizes the whole setup by adjusting the given set of camera parameters. We can steer the optimization towards different goals depending on the desired application, e.g. the widest possible volume coverage or maximum camera visibility to overcome heavy occlusion problems during the tracking process. We also consider parameter constraints that the user may specify according to restrictions in the local environment where the cameras have to be mounted. This allows for a convenient definition of higher level constraints for the camera setup.