Inhaltszusammenfassung:
The size of datasets in scientific computing is rapidly
increasing. This increase is caused by a boost of processing power in
the past years, which in turn was invested in an increase of the
accuracy and the size of the models. A similar trend enabled a
significant improvement of medical scanners; more than 1000 slices of
a resolution of 512x512 can be generated by modern scanners in daily
practice. Even in computer-aided engineering typical models eas-ily
contain several million polygons. Unfortunately, the data complexity
is growing faster than the rendering performance of modern computer
systems. This is not only due to the slower growing graphics
performance of the graphics subsystems, but in particular because of
the significantly slower growing memory bandwidth for the transfer of
the geometry and image data from the main memory to the graphics
accelerator.
Large model visualization addresses this growing divide between data
complexity and rendering performance. Most methods focus on the
reduction of the geometric or pixel complexity, and hence also the
memory bandwidth requirements are reduced.
In this dissertation, we discuss new approaches from three different
research areas. All approaches target at the reduction of the
processing complexity to achieve an interactive visualization of large
datasets. In the second part, we introduce applications of the
presented ap-proaches. Specifically, we introduce the new VIVENDI
system for the interactive virtual endoscopy and other applications
from mechanical engineering, scientific computing, and architecture.
Abstract:
The size of datasets in scientific computing is rapidly
increasing. This increase is caused by a boost of processing power in
the past years, which in turn was invested in an increase of the
accuracy and the size of the models. A similar trend enabled a
significant improvement of medical scanners; more than 1000 slices of
a resolution of 512x512 can be generated by modern scanners in daily
practice. Even in computer-aided engineering typical models eas-ily
contain several million polygons. Unfortunately, the data complexity
is growing faster than the rendering performance of modern computer
systems. This is not only due to the slower growing graphics
performance of the graphics subsystems, but in particular because of
the significantly slower growing memory bandwidth for the transfer of
the geometry and image data from the main memory to the graphics
accelerator.
Large model visualization addresses this growing divide between data
complexity and rendering performance. Most methods focus on the
reduction of the geometric or pixel complexity, and hence also the
memory bandwidth requirements are reduced.
In this dissertation, we discuss new approaches from three different
research areas. All approaches target at the reduction of the
processing complexity to achieve an interactive visualization of large
datasets. In the second part, we introduce applications of the
presented ap-proaches. Specifically, we introduce the new VIVENDI
system for the interactive virtual endoscopy and other applications
from mechanical engineering, scientific computing, and architecture.
Large Model Visualization , Occlusion Culling , Virtual Endoscopy
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