Marc Schirski

Virtual Tubelets-efficiently visualizing large amounts of particle trajectories

The depiction of particle trajectories is an effective means for the visualization of fluid flows. However, standard visualization techniques suffer from a variety of weaknesses, ranging from ambiguous depth perception for simple line drawings to a high geometrical complexity and decreased interactivity for polygonal tubes. This paper addresses these problems by introducing a novel approach to pathline visualization, which we call Virtual Tubelets. It employs billboarding techniques in combination with suitable textures in order to create the illusion of solid tubes, thus efficiently and unambiguously depicting large amounts of particle trajectories at interactive frame rates. By choosing an appropriate orientation for the billboards, certain issues concerning immersive displays with multiple projection screens are resolved, which allows for an unrestricted use in virtual environments as well. Using modern graphics hardware with programmable vertex and pixel pipelines results in an additional speed-up of the rendering process and a further improvement of image quality. This creates a nearly perfect illusion of tubular geometry, including plausible intersections and consistent illumination with the rest of the scene. The efficiency of our approach is proven by comparing rendering speed and visual quality of Virtual Tubelets to that of conventional, polygonal tube renderings.

A Time Model for Time‐Varying Visualization

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 visualization applications 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.

Interactive exploration of large data in hybrid visualization environments

With rising data sizes and growing complexity, the results of modern numerical simulations are increasingly dif- ficult to understand. Thus, using Virtual Reality methodology for an interactive analysis of such data gains more and more importance. However, interaction within virtual environments comes at the cost of real-time constraints, which are difficult to meet. Using a hybrid visualization environment consisting of a high-performance computing (HPC) system connected to a graphics workstation (or multiple rendering nodes) we propose a workload distribution which significantly increases interactivity during the data analysis process. Based on a novel model of the exploration process, we introduce an additional step into the conventional visualization pipeline before mapping the whole process onto system components. This incorporates the respective benefits of high-performance computing and GPU-based computation into a single visualization framework. Basically, by coupling an HPC-based extraction of a region-of-interest to GPU-based flow visualization, an interactive analysis of large datasets is made possible. Taking interactive particle tracing and volume rendering as examples, we show the applicability of our approach to an interactive exploration of datasets exceeding the memory limits of a single workstation.

Automatic Multi-Camera Setup Optimization for Optical Tracking

We 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.

Time step prioritising in parallel feature extraction on unsteady simulation data

Explorative analysis of unsteady computational fluid dynamics (CFD) simulations requires a fast extraction of flow features. For time-varying data, the extraction algorithm has to be executed for each time step in the period under observation. Even when parallelised on a remote high performance computer, the users waiting time still exceeds interactivity criteria for large data sets. Moreover, computations are generally performed in a fixed order, not taking into account the importance of partial results for the users investigation. In this paper we propose a general method to guide parallel feature extraction on unsteady data sets in order to assist the user during the explorative analysis even though interactive response times might not be available. By re-ordering of single time step computations, the order in which features are provided is arranged according to the users exploration process. We describe three different concepts based on typical user behaviours. Using this approach, parallel extraction of unsteady features is enhanced for arbitrary extraction methods.

Parallel Calculation of Accurate Path Lines in Virtual Environments through Exploitation of Multi-Block CFD Data Set Topology

The use of Virtual Reality (VR) techniques for the investigation of complex flow phenomena offers distinct advantages in comparison to conventional visualization techniques. Especially for unsteady flows, VR methodology provides an intuitive approach for the exploration of simulated fluid flows. However, the visualization of Computational Fluid Dynamics (CFD) data is often too time-consuming to be carried out in real-time, and thus violates essential constraints concerning real-time interaction and visualization. To overcome this obstacle, we make use of the fact that typically a multi-block approach is employed for domain decomposition, and we use the corresponding data structures for the computation of path lines and for parallelization. In this paper, we present the synthesis of fragmented multi-block data sets and our implementation of an accurate path line integration scheme in order to speed up path line computations. We report on the results of our efforts and describe a combination of this algorithm with a highly efficient visualization approach of large amounts of particle traces, thus considerably improving interactivity when exploring large scale CFD data sets.

Camera setup optimization for optical tracking in virtual environments

In this paper we present a method for finding the optimal camera alignment for a tracking system with multiple cameras, by specifying the volume that should be tracked and an initial camera setup. The approach we use is twofold: on the one hand, we use a rather simple gradient based steepest descent method and on the other hand, we also implement a simulated annealing algorithm that features guaranteed optimality assertions. Both approaches are fully automatic and take advantage of modern graphics hardware since we implemented a GPU-based accelerated visibility test. The proposed algorithms can automatically optimize the whole camera setup by adjusting the given set of parameters. The optimization may have different goals depending on the desired application, e.g. one may wish to optimize towards the widest possible coverage of the specified volume, while others would prefer to maximize the number of cameras seeing a certain area to overcome heavy occlusion problems during the tracking process. Our approach also considers parameter constraints that the user may specify according to the local environment where the cameras have to be set up. This makes it possible to simply formulate higher level constraints e.g. all cameras have a vertical up vector. It individually adapts the optimization to the given situation and also asserts the feasibility of the algorithms output.

Exploring Flow Fields with GPU-Based Stream Tracers in Virtual Environments

Abstract In this paper we present an immersive visualization approach for the intuitive exploration of ow elds, whichoperates entirely within the graphics subsystem. We augment particle data with a brief history of their recent posi-tions, thus effectively computing and displaying animated tracers facilitating the understanding of the underlyingow eld. Image quality is enhanced by employing a billboard-based rendering method for the particle trajec-tories simulating lit tubular geometry. This leads to signicantly reduced depth perception problems and depthorder ambiguities. Interactivity is maintained even for large amounts of tracers by shifting the computational loadto the GPU. We alleviate 3D seed point specication problems by offering interaction mechanisms with full 6degrees-of-freedom within an immersive virtual environment. Categories and Subject Descriptors (according to ACM CCS) : I.3.7 [Computer Graphics]: Virtual Reality, I.3.6[Computer Graphics]: Interaction Techniques, I.3.3 [Computer Graphics]: Display Algorithms

Particles with a history: visualizing flow fields with GPU-based streamlines

Our research focuses on the visualization of flow fields in virtual environments. Thus, interactivity is of prime importance for all techniques applied. Here, we propose the augmentation of the computation of particle trajectories on a graphics processing unit (GPU) with a method for storing and rendering a particle’s recent history. This technique extends our previous work in [Schirski et al. 2005], which introduces a high-quality but geometrically lightweight visualization method for particle trajectories based on billboardingtechniques.