Real-time visualization of particle traces in virtual environments can aid in the exploration and analysis of complex three dimensional vector fields. This paper introduces a scalable method suitable for the interactive visualization of large time-varying vector fields on commodity hardware. A real-time data streaming and visualization approach and its out-of-core scheme for the pre-processing and rendering of data are described. The presented approach yields low-latency application start-up times and small memory footprints. A proof of concept systems was implemented on a low-cost Linux workstation equipped with spatial tracking hardware, data gloves and shutter glasses. The system was used to implement a virtual wind tunnel in which a volumetric particle injector can introduce up to 60000 particles into the flow field while an interactive rendering performance of 60 frames per second is maintained.

We present an out-of-core, point-based approach for interactive rendering of very large volumetric datasets. Our approach is based on the assumption that the density of voxels with the same function-value in large discretized volumetric scalar fields is high enough to be used to render contour and volume approximations using points to represent the voxels. This approach allows us to visualize isovalue-structures in high-resolution datasets at full resolution and interactive frame rates. In a pre-processing step, we sort the voxels by function-value and store them in a file together with a look-up table for later interactive retrieval. The displayed voxelsets can then be changed in real time by determining their locations in the file and loading them into memory. As we store position, and not function-value, the volumetric dimension of a dataset to be handled by our approach is limited by three factors: the number of points that can be rendered to achieve a sufficient frame rate, the number of bits used to store the position data, and the maximum file-size supported by the operating system. Depending on the spatial distribution of the voxels among the function-values selected, the result is either one or multiple contours or ``isoclouds''.

Visualization of particle traces provides intuitive and efficient means for the exploration and analysis of complex vector fields. This paper presents a method suitable for the real-time visualization of arbitrarily large time-varying vector fields in virtual environments. We describe an out-of-core scheme in which two distinct pre-processing and rendering components enable real-time data streaming and visualization. The presented approach yields low-latency application start-up times and small memory footprints. The described system was used to implement a ''volumetric fog lance,'' which can emit up to 60000 particles into a flow field while maintaining an interactive frame rate of 60 frames per second. All algorithms were specifically designed to support commodity hardware. The proof-of-concept system is running on a low-cost Linux workstation equipped with a 120GB-E-IDE RAID (Redundant Array of Inexpensive Disk) system.

We present a method which allows us to visualize high-resolution medical volumetric datasets exceeding 512^3 data points at interactive frame-rates. Our approach is an out-of-core point-based rendering approach. In a pre-processing step we store the data points of the dataset sorted by their value on disk. At runtime we retrieve them when needed from disk to immediately stream them to the rendering hardware. The high resolution of the dataset and the density of the data points allows us to use a pure point-based rendering approach to achieve good image quality. The density of points with equal or similar values within the dataset can be considered as being high enough to display regions and contours using points only. Interactivity is not restricted to navigation through the dataset itself, changing the values of interest to be displayed can be done with real-time response. We are able to interactively display high-resolution datasets without any loss of detail. For datasets exceeding 512^3 values we are still able to display those datasets on standard off-the-shelf hardware. Using points only we reduce the amount of data to be transferred to the graphics hardware when compared to hardware-based volume-rendering or mesh-based approaches.