Name
Abstract | ||
|---|---|---|
Representing uncertainty when creating visualizations is becoming more indispensable to understand and analyze scientific data. Uncertainty may come from different sources, such as, ensembles of experiments or unavoidable information loss when performing data reduction. One natural model to represent uncertainty is to assume that each position in space instead of a single value may take on a distribution of values. In this paper we present a new volume rendering method using per voxel Gaussian mixture models (GMMs) as the input data representation. GMMs are an elegant and compact way to drastically reduce the amount of data stored while still enabling realtime data access and rendering on the GPU. Our renderer offers efficient sampling of the data distribution, generating renderings of the data that flicker at each frame to indicate high variance. We can accumulate samples as well to generate still frames of the data, which preserve additional details in the data as compared to either traditional scalar indicators (such as a mean or a single nearest neighbor down sample) or to fitting the data with only a single Gaussian per voxel. We demonstrate the effectiveness of our method using ensembles of climate simulations and MRI scans as well as the down sampling of large scalar fields as examples. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/LDAV.2012.6378978 | LDAV |
Keywords | Field | DocType |
real-time data access,data reduction,mri scans,gmm,large scalar fields,uncertainty representation,gpu,ensemble visualization,information retrieval,data structures,volume rendering method,uncertainty visualization,natural sciences computing,rendering (computer graphics),data fitting,climate simulations,scientific data analysis,input data representation,down sampling,data visualisation,volume visualization,gaussian processes,data distribution,gaussian mixture model,volume rendering,real-time systems,real time systems | Data structure,Computer vision,Volume rendering,Data visualization,Computer science,Algorithm,Gaussian,Artificial intelligence,Gaussian process,Rendering (computer graphics),Mixture model,Data reduction | Conference |
ISBN | Citations | PageRank |
978-1-4673-4732-7 | 10 | 0.51 |
References | Authors | |
8 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shusen Liu | 1 | 10 | 1.86 |
| Joshua A. Levine | 2 | 369 | 19.64 |
| Peer-Timo Bremer | 3 | 1446 | 82.47 |
| Valerio Pascucci | 4 | 3241 | 192.33 |