Master's Thesis - Studierstube Augmented Reality Project - Graz ...
Master's Thesis - Studierstube Augmented Reality Project - Graz ...
Master's Thesis - Studierstube Augmented Reality Project - Graz ...
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6.3 Results<br />
6.3 Results<br />
To complete this chapter some images resulting from the so far described algorithms<br />
and datasets can be presented in this section. The following figures show several more<br />
or less simple flow structures since more complex ones would not be feasible for these<br />
static images due to missing interactivity. Referring to that, it can be underlined<br />
that the more complexity a velocity field contains the more important interactivity<br />
gets, even if it is only the ability to rotate, pan and zoom the scene. Due to these<br />
inconveniences not always the same but the most instructive perspective was chosen<br />
for similar datasets. The diagrams 6.8 to 6.9 in section 6.4 subsequently compares the<br />
main techniques due to their performance in frames per seconds on the two reference<br />
systems which were presented in section 6.1.<br />
• Cash-Flow and glyph-based visualizations are shown in figure 6.2.<br />
• Different applications for particles are shown in figure 6.3.<br />
• Stream lines and path lines are shown in figure 6.4.<br />
• Different types of cutting planes are shown in figure 6.5.<br />
• Stream tubes are shown in figure 6.6.<br />
• Examples for a combination of visualizations are shown in figure 6.7.<br />
6.4 Scalability<br />
Diagram 6.8 to 6.9 show that a glyph renderer is faster when less glyphs are rendered. In<br />
this case we varied the render positions between rendering every fifth velocity value to<br />
rendering every 20 th position. Since this algorithms is mainly performed on the CPU<br />
and every frame all positions are passed to the graphics cards, a frame rate higher<br />
than 60 could not be expected and also a not so high difference between the reference<br />
systems.<br />
The performance of GPU based visualizations highly depends on the amount of<br />
rendered elements and therefore on the used buffer sizes. The buffer size n × m determinates<br />
in the case of a particle based visualization the number of possible rendered<br />
particles by n∗m and in the case of lines n determinates the number of lines and m the<br />
supporting-point count of each line. We tested quadratic buffer sizes from 256 to 1024<br />
pixels in each direction. Rectangular buffer textures are possible as well, but perform<br />
with the same results as quadratic ones until one size exceeds the next possible power<br />
of 2 (for example 256, 512, 1024,...). Consequently, a quadratic buffer size leads to<br />
an optimal result. Since all buffer positions are definitely processed each render pass,<br />
techniques for an early line termination, for example when the line moves out of the<br />
velocity field, would not increase the performance.<br />
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