Visualizing Gridded Datasets with Large Number of Missing Values
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Transcript of Visualizing Gridded Datasets with Large Number of Missing Values
UCSC
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)
UCSC
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