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Visualizing Gridded Datasets with Large Number of Missing

Values

Suzana Djurcilov and Alex Pang

University of California, Santa Cruz

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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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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