Efficient Index Support for View-Dependent Queries on CFD Data

Recent years have revealed a growing importance of Virtual Reality (VR) visualization techniques which offer comfortable means to enable users to interactively explore 3D data sets. Particularly in the field of computational fluid dynamics (CFD), the rapidly increasing size of data sets with complex geometric and supplementary scalar information requires new out-of-core solutions for fast isosurface extraction and other CFD post-processing tasks. Whereas spatial access methods overcome the limitations of main memory size and support fast data selection, their VR support needs to be improved. Firstly, interactive users strongly depend on quick first views of the regions in their view direction and, secondly, they require quick relevant views even when they change their view point or view direction.

We develop novel view-dependent extensions for access methods which support static and dynamic scenarios. Our new human vision-oriented distance function defines an adjusted order of appearance for data objects in the visualization space and, thus, supports quick first views. By a novel incremental concept of view-dependent result streaming which interactively follows dynamic changes of users’ viewpoints and view directions, we provide a high degree of interactivity and mobility in VR environments. Our integration into the new index based graphics data server “IndeGS” proves the efficiency of our techniques in the context of post-processing CFD data with dynamically interacting users.
Authors: Brochhaus C., Seidl T.
Published in: Papadias D., Zhang D., Kollios G. (eds.): Advances in Spatial and Temporal Databases. Proc. 10th International Symposium on Spatial and Temporal Databases (SSTD 2007), Boston, MA, USA. Springer LNCS 4605
Publisher: Springer - Heidelberg, Germany
Language: EN
Year: 2007
Pages: 57-74
ISBN: 978-3-540-735
Conference: SSTD
DOI:10.1007/978-3-540-73540-3_4
Url:LNCS 4605
Type: Conference papers (peer reviewed)
Research topic: Fast Access to Complex Data