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Visualizing Gridded Datasets with Large Number of Missing Values

Visualizing Gridded Datasets with Large Number of Missing Values. Suzana Djurcilov and Alex Pang University of California, Santa Cruz. OVERVIEW. Motivation NEXRAD Background Visualization Options Conclusions and Suggestions Future Directions. Motivation.

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Visualizing Gridded Datasets with Large Number of Missing Values

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  1. Visualizing Gridded Datasets with Large Number of Missing Values Suzana Djurcilov and Alex Pang University of California, Santa Cruz

  2. OVERVIEW Motivation NEXRAD Background Visualization Options Conclusions and Suggestions Future Directions

  3. Motivation • Known visualization tools (e.g. VTK) often assume full grid • Filling grids with arbitrary values causes incorrect visualizations

  4. 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

  5. NEXRAD

  6. 3 1 3 1 5 5 99.99 -99.99 Incorrect contours when using arbitrary values Threshold = 2.0

  7. What can be done ?

  8. Point Cloud • Draw a point or sphere at point location • Advantage: quick and simple • Disadvantage: cluttering, poor depth perception

  9. Point Cloud

  10. 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

  11. Interpolation methods

  12. Interpolation - Distribution types Clustered Uniform

  13. Interpolation - artifacts Stack-of-pancakes artifact from Shepard’s

  14. Delaunay • Take a subset around a certain treshold • Connect the points using Delaunay triangulation • Advantage: widely available • Disadvantage: connected regions, convex shapes

  15. Delaunay

  16. 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

  17. Surface reconstruction

  18. Take an average of neighboring normals Use only available data Modified Normals

  19. Modified Normals before after

  20. Take an average of neighboring gradients Move surface vertices in direction of the gradient Takes out very sharp features Modified Isosurface

  21. Modified Isosurface before after

  22. Smoothed Isosurface • Taubin 1995 - Gaussian smoothing of vertex points • Alternative inward and outward steps • Advantage: takes out sharp edges • Disadvantage: possibility of excessive smoothing

  23. Smoothed Isosurface

  24. 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

  25. 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

  26. Future Work • Expand into other physical sciences • Experiment with vector algorithms • Apply a variety of gradient filters

  27. 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)

  28. UCSC http://www.cse.ucsc.edu/research/slvg/nexrad.html Point Cloud Delaunay Surface Reconstruction Smoothed Isosurface

  29. Volume Visualization Default transfer function Transfer function not including missing values

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