Many applications require simultaneous display of multiple datasets, representing multiple samples, or multiple conditions, or multiple simulation times, in the same visualization. Such multiple dataset visualization ...
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ISBN:
(纸本)9728865392
Many applications require simultaneous display of multiple datasets, representing multiple samples, or multiple conditions, or multiple simulation times, in the same visualization. Such multiple dataset visualization (MDV) has to handle and render massive amounts of data concurrently. We analyze the performance of two widely used techniques, namely, isosurface extraction and texture-based rendering for visualization of multiple sets of the scalar volume data. Preliminary tests performed using up to 25 sets of moderate-size (256(3)) data show that the calculated times for the generation and rendering of polygons representing isosurface, and for the mapping of a series of textured slices increase non-uniformly with increasing the number of individual datasets. Both techniques are found to no longer be interactive with the frame-rates dropping below one for six or more datasets. To improve the MDV frame-rate, we propose a scheme based on the combination of hardware-assisted texture mapping and general clipping. In essence, it exploits the 3D surface texture mapping by rendering only the externally visible surfaces of all volume datasets at a given instant, with dynamic clipping enabled to explore the interior of the data. The calculated frame-rates remain above one and are substantially higher than those with the other two techniques.
In this state-of-the-art report we discuss relevant research works related to the visualization of complex, multi-variate data. We discuss how different techniques take effect at specific stages of the visualization p...
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In this state-of-the-art report we discuss relevant research works related to the visualization of complex, multi-variate 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.
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