visualizing gridded datasets with large number of missing values
DESCRIPTION
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. - PowerPoint PPT PresentationTRANSCRIPT
![Page 1: Visualizing Gridded Datasets with Large Number of Missing Values](https://reader035.vdocument.in/reader035/viewer/2022070402/56813832550346895d9fe1d4/html5/thumbnails/1.jpg)
UCSC
Visualizing Gridded Datasets with Large Number of Missing
Values
Suzana Djurcilov and Alex Pang
University of California, Santa Cruz
![Page 2: Visualizing Gridded Datasets with Large Number of Missing Values](https://reader035.vdocument.in/reader035/viewer/2022070402/56813832550346895d9fe1d4/html5/thumbnails/2.jpg)
UCSC
OVERVIEW• Motivation
• NEXRAD
• Background
• Visualization Options
• Conclusions and Suggestions
• Future Directions
![Page 3: Visualizing Gridded Datasets with Large Number of Missing Values](https://reader035.vdocument.in/reader035/viewer/2022070402/56813832550346895d9fe1d4/html5/thumbnails/3.jpg)
3UCSC
Motivation
• Known visualization tools (e.g. VTK) often assume full grid
• Filling grids with arbitrary values causes incorrect visualizations
![Page 4: Visualizing Gridded Datasets with Large Number of Missing Values](https://reader035.vdocument.in/reader035/viewer/2022070402/56813832550346895d9fe1d4/html5/thumbnails/4.jpg)
4UCSC
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
![Page 5: Visualizing Gridded Datasets with Large Number of Missing Values](https://reader035.vdocument.in/reader035/viewer/2022070402/56813832550346895d9fe1d4/html5/thumbnails/5.jpg)
5UCSC
NEXRAD
![Page 6: Visualizing Gridded Datasets with Large Number of Missing Values](https://reader035.vdocument.in/reader035/viewer/2022070402/56813832550346895d9fe1d4/html5/thumbnails/6.jpg)
6UCSC
Incorrect contours when using arbitrary values
-99.99 99.995 5
31 13
Threshold = 2.0
![Page 7: Visualizing Gridded Datasets with Large Number of Missing Values](https://reader035.vdocument.in/reader035/viewer/2022070402/56813832550346895d9fe1d4/html5/thumbnails/7.jpg)
7UCSC
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
![Page 8: Visualizing Gridded Datasets with Large Number of Missing Values](https://reader035.vdocument.in/reader035/viewer/2022070402/56813832550346895d9fe1d4/html5/thumbnails/8.jpg)
8UCSC
Point Cloud
• Draw a point or sphere at point location
• Advantage: quick and simple
• Disadvantage: cluttering, poor depth perception
![Page 9: Visualizing Gridded Datasets with Large Number of Missing Values](https://reader035.vdocument.in/reader035/viewer/2022070402/56813832550346895d9fe1d4/html5/thumbnails/9.jpg)
9UCSC
Point Cloud
![Page 10: Visualizing Gridded Datasets with Large Number of Missing Values](https://reader035.vdocument.in/reader035/viewer/2022070402/56813832550346895d9fe1d4/html5/thumbnails/10.jpg)
10UCSC
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
![Page 11: Visualizing Gridded Datasets with Large Number of Missing Values](https://reader035.vdocument.in/reader035/viewer/2022070402/56813832550346895d9fe1d4/html5/thumbnails/11.jpg)
11UCSC
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
![Page 12: Visualizing Gridded Datasets with Large Number of Missing Values](https://reader035.vdocument.in/reader035/viewer/2022070402/56813832550346895d9fe1d4/html5/thumbnails/12.jpg)
12UCSC
Interpolation - Distribution types
Clustered Uniform
![Page 13: Visualizing Gridded Datasets with Large Number of Missing Values](https://reader035.vdocument.in/reader035/viewer/2022070402/56813832550346895d9fe1d4/html5/thumbnails/13.jpg)
13UCSC
Interpolation - artifacts
Stack-of-pancakes artifact from Shepard’s
![Page 14: Visualizing Gridded Datasets with Large Number of Missing Values](https://reader035.vdocument.in/reader035/viewer/2022070402/56813832550346895d9fe1d4/html5/thumbnails/14.jpg)
14UCSC
Delaunay
• Take a subset around a certain treshold
• Connect the points using Delaunay triangulation
• Advantage: widely available
• Disadvantage: connected regions, convex shapes
![Page 15: Visualizing Gridded Datasets with Large Number of Missing Values](https://reader035.vdocument.in/reader035/viewer/2022070402/56813832550346895d9fe1d4/html5/thumbnails/15.jpg)
15UCSC
Delaunay
![Page 16: Visualizing Gridded Datasets with Large Number of Missing Values](https://reader035.vdocument.in/reader035/viewer/2022070402/56813832550346895d9fe1d4/html5/thumbnails/16.jpg)
16UCSC
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
![Page 17: Visualizing Gridded Datasets with Large Number of Missing Values](https://reader035.vdocument.in/reader035/viewer/2022070402/56813832550346895d9fe1d4/html5/thumbnails/17.jpg)
17UCSC
Surface reconstruction
![Page 18: Visualizing Gridded Datasets with Large Number of Missing Values](https://reader035.vdocument.in/reader035/viewer/2022070402/56813832550346895d9fe1d4/html5/thumbnails/18.jpg)
18UCSC
Modified Normals
• Take an average of neighboring normals
• Use only available data
10111111
ijkkijjkiijkkijjki
ijk
VVVVVVV
![Page 19: Visualizing Gridded Datasets with Large Number of Missing Values](https://reader035.vdocument.in/reader035/viewer/2022070402/56813832550346895d9fe1d4/html5/thumbnails/19.jpg)
19UCSC
Modified Normals
before after
![Page 20: Visualizing Gridded Datasets with Large Number of Missing Values](https://reader035.vdocument.in/reader035/viewer/2022070402/56813832550346895d9fe1d4/html5/thumbnails/20.jpg)
20UCSC
Modified Isosurface
• Take an average of neighboring gradients• Move surface vertices in direction of the gradient• Takes out very sharp features
![Page 21: Visualizing Gridded Datasets with Large Number of Missing Values](https://reader035.vdocument.in/reader035/viewer/2022070402/56813832550346895d9fe1d4/html5/thumbnails/21.jpg)
21UCSC
Modified Isosurface
before after
![Page 22: Visualizing Gridded Datasets with Large Number of Missing Values](https://reader035.vdocument.in/reader035/viewer/2022070402/56813832550346895d9fe1d4/html5/thumbnails/22.jpg)
22UCSC
Smoothed Isosurface
• Taubin 1995 - Gaussian smoothing of vertex points
• Alternative inward and outward steps
• Advantage: takes out sharp edges
• Disadvantage: possibility of excessive smoothing
![Page 23: Visualizing Gridded Datasets with Large Number of Missing Values](https://reader035.vdocument.in/reader035/viewer/2022070402/56813832550346895d9fe1d4/html5/thumbnails/23.jpg)
23UCSC
Smoothed Isosurface
![Page 24: Visualizing Gridded Datasets with Large Number of Missing Values](https://reader035.vdocument.in/reader035/viewer/2022070402/56813832550346895d9fe1d4/html5/thumbnails/24.jpg)
24UCSC
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
![Page 25: Visualizing Gridded Datasets with Large Number of Missing Values](https://reader035.vdocument.in/reader035/viewer/2022070402/56813832550346895d9fe1d4/html5/thumbnails/25.jpg)
25UCSC
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
![Page 26: Visualizing Gridded Datasets with Large Number of Missing Values](https://reader035.vdocument.in/reader035/viewer/2022070402/56813832550346895d9fe1d4/html5/thumbnails/26.jpg)
26UCSC
Future Work
• Expand into other physical sciences
• Experiment with vector algorithms
• Apply a variety of gradient filters
![Page 27: Visualizing Gridded Datasets with Large Number of Missing Values](https://reader035.vdocument.in/reader035/viewer/2022070402/56813832550346895d9fe1d4/html5/thumbnails/27.jpg)
27UCSC
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)
![Page 28: Visualizing Gridded Datasets with Large Number of Missing Values](https://reader035.vdocument.in/reader035/viewer/2022070402/56813832550346895d9fe1d4/html5/thumbnails/28.jpg)
UCSC
http://www.cse.ucsc.edu/research/slvg/nexrad.html
Point Cloud Delaunay
Surface Reconstruction Smoothed Isosurface
![Page 29: Visualizing Gridded Datasets with Large Number of Missing Values](https://reader035.vdocument.in/reader035/viewer/2022070402/56813832550346895d9fe1d4/html5/thumbnails/29.jpg)
29UCSC
Volume Visualization
Default transfer function Transfer function notincluding missing values