2016 IEEE Pacific Visualization Symposium (PacificVis) 2016
DOI: 10.1109/pacificvis.2016.7465271
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Screen-space silhouettes for visualizing ensembles of 3D isosurfaces

Abstract: Figure 1: Visualization of a weather forecast ensemble from the ECMWF Prediction System [29]. This ensemble comprises wind velocity data and consists of 50 members. For one isovalue, all members are visualized as silhouettes of isosurfaces. Additionally, a mean ensemble member is rendered as a gray isosurface to enhance the visual perception of the spatial context. Color is used to cluster members by their similarity. ABSTRACTVisualizing sets of isosurfaces from 3D scalar ensemble fields is a difficult task du… Show more

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Cited by 20 publications
(5 citation statements)
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“…For example, Ferstl et al 12 use PCA to convert streamlines into a structure preserving Euclidean space. Furthermore, a lot of approaches are implemented by feature extraction algorithms like k-series in the work 5,36,6 , and the work based on DBSCAN 10,26 , AHC 12,13,37,38,39,40 and topic modeling 41,42,43,44,45,46,47,48,49,50,51,52,53 .…”
Section: Spatio-temporal Simulation Data Visualizationmentioning
confidence: 99%
“…For example, Ferstl et al 12 use PCA to convert streamlines into a structure preserving Euclidean space. Furthermore, a lot of approaches are implemented by feature extraction algorithms like k-series in the work 5,36,6 , and the work based on DBSCAN 10,26 , AHC 12,13,37,38,39,40 and topic modeling 41,42,43,44,45,46,47,48,49,50,51,52,53 .…”
Section: Spatio-temporal Simulation Data Visualizationmentioning
confidence: 99%
“…This method produces a bubble-like shading effect. This effect is described in Demir et al [30]. Programmable Transfer Functions can not only implement the silhouette shading effect; they also enable the user to manipulate the silhouette parameters in real time.…”
Section: Silhouette Shadingmentioning
confidence: 99%
“…Increasing in complexity, we see the use of pairwise sequential animation and screen door tinting to show the differences between ensemble members using value changes to field points [PPA*12], the comparison of 2D function ensembles on three levels of details (i.e. surface plot, domain‐oriented and member‐oriented) [PPBT12], the comparison of 3D scalar field ensembles using mean isosurface [DKW16] and grouping ensemble members using hierarchical clustering [HHBY16]. Although these tools offer different ways to visualize and compare different data types, they are limited in the number of ensemble members that they visualize.…”
Section: Related Workmentioning
confidence: 99%