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Visualizing Gridded Datasets with Large Number of Missing
Values
Suzana Djurcilov and Alex Pang
University of California, Santa Cruz
UCSC
OVERVIEW• Motivation
• NEXRAD
• Background
• Visualization Options
• Conclusions and Suggestions
• Future Directions
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Motivation
• Known visualization tools (e.g. VTK) often assume full grid
• Filling grids with arbitrary values causes incorrect visualizations
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Background
• NEXRAD (WSR-88D) is a 3D radar
• Output is a conical grid with usually no more than 4% filled
• Standard viz methods are 2D
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NEXRAD
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Incorrect contours when using arbitrary values
-99.99 99.995 5
31 13
Threshold = 2.0
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What can be done ?
P o in tC lo ud
D e lau n ayT ria n gu la tion
S u rfa ceR e con s truc tion
P o lygo n ize
S ca tte red In te rp o la teto fu ll g rid
V o lu m eR e n de ring
M o d if iedG ra d ie n t
M o d if iedS u rfa ce
S m o o th ed
Iso surfa ce C u tt ingP la n es
G ridd ed
V isu a liza tionO p tio ns
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Point Cloud
• Draw a point or sphere at point location
• Advantage: quick and simple
• Disadvantage: cluttering, poor depth perception
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Point Cloud
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Interpolation
• Very useful for evenly distributed data
• Many choices: Shepard’s, Multiquadrics, Krigging etc.
• Need to be careful to preserve desired properties in the data
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Interpolation methods
Method Troubles Good forShepard’s Many artifacts Simple tasks
Multi-quadrics Out-of-range values Small datasets
Thin-platesplines
Expensive Low-variabilitydatasets
Krigging User-specifiedvarigram
High-variabilitydatasets
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Interpolation - Distribution types
Clustered Uniform
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Interpolation - artifacts
Stack-of-pancakes artifact from Shepard’s
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Delaunay
• Take a subset around a certain treshold
• Connect the points using Delaunay triangulation
• Advantage: widely available
• Disadvantage: connected regions, convex shapes
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Delaunay
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Surface reconstruction
• Hoppe et al. 1992 - treat the subset as unorganized points
• Recreate the surface using tangent-planes incident to the mesh points
• Advantage: plausible surface from a subset
• Disadvantage: choppy edges
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Surface reconstruction
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Modified Normals
• Take an average of neighboring normals
• Use only available data
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VVVVVVV
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Modified Normals
before after
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Modified Isosurface
• Take an average of neighboring gradients• Move surface vertices in direction of the gradient• Takes out very sharp features
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Modified Isosurface
before after
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Smoothed Isosurface
• Taubin 1995 - Gaussian smoothing of vertex points
• Alternative inward and outward steps
• Advantage: takes out sharp edges
• Disadvantage: possibility of excessive smoothing
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Smoothed Isosurface
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Conclusions
• Sparse gridded datasets can be handled as gridded or scattered
• Standard methods need adjustments for missing values
• We present two options for improving isosurfaces
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Suggestions
• For very sparse data use scattered methods
• Interpolation best for uniform distribution
• Clustered data better treated raw
• With high-frequency data post-process isosurfaces with smoothing
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Future Work
• Expand into other physical sciences
• Experiment with vector algorithms
• Apply a variety of gradient filters
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Acknowledgements
• Wendell Nuss, NPS, Monterey
• ONR grant N00014-96-0949, NSF grant IRI-9423881, DARPA grant N66001-97-8900, NASA grant ncc2-5281
• Santa Cruz Laboratory for Visualization and Graphics (SLVG)
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http://www.cse.ucsc.edu/research/slvg/nexrad.html
Point Cloud Delaunay
Surface Reconstruction Smoothed Isosurface
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Volume Visualization
Default transfer function Transfer function notincluding missing values
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