Exploration and visualization of large scale time varying and unstructured volume data
Download
1 / 35

Exploration and Visualization of Large-scale, Time-varying and Unstructured Volume Data - PowerPoint PPT Presentation

Exploration and Visualization of Large-scale, Time-varying and Unstructured Volume Data Lars Linsen, Ralph Bruckschen, Jaya Sreevalsan-Nair, Christof Nuber, Bernd Hamann, Kenneth I. Joy Center for Image Processing and Integrated Computing (CIPIC) University of California, Davis

Related searches for Exploration and Visualization of Large-scale, Time-varying and Unstructured Volume Data

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha

Download Presentation

Exploration and Visualization of Large-scale, Time-varying and Unstructured Volume Data

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


Exploration and Visualization of Large-scale, Time-varying and Unstructured Volume Data

Lars Linsen, Ralph Bruckschen, Jaya Sreevalsan-Nair, Christof Nuber, Bernd Hamann, Kenneth I. Joy

Center for Image Processing and Integrated Computing (CIPIC)

University of California, Davis

Presentation at All-Hands Meeting ‘03

San Diego, CA

March 18 - 21, 2003


Volume data representation

regular / structured irregular / unstructured

imaging data

(stack of images)

simulated data

(numerically computed)

measured data

(distributed sensors)

+ implicit grid connectivity + high adaptivity

+ implicit vertex positions

applications

advantages

All-Hands Meeting '03


Volume data representation

regular / structured irregular / unstructured

imaging data

(stack of images)

simulated data

(numerically computed)

measured data

(distributed sensors)

+ implicit grid connectivity + high adaptivity

+ implicit vertex positions

changing over time

applications

advantages

All-Hands Meeting '03


Overview

  • Wavelet-based multiresolution with subdivision

  • - regular

  • - provides high adaptivity

  • - time-varying

  • 2. Multiresolution over unstructured hexahedral grids

  • - irregular

  • Point-based high-resolution visualization

  • - regular / irregular

  • - simple rendering primitives

All-Hands Meeting '03


Overview

  • Wavelet-based multiresolution with subdivision

  • - regular

  • - provides high adaptivity

  • - time-varying

  • 2. Multiresolution over unstructured hexahedral grids

  • - irregular

  • Point-based high-resolution visualization

  • - regular / irregular

  • - simple rendering primitives

All-Hands Meeting '03


subdivision

polyhedral shapes:

All-Hands Meeting '03


Downsampling filter

w/o wavelets

w/ wavelets

Linear B-spline wavelet

downsampling filter:

Brain: 1050 x 970 x 753

original

All-Hands Meeting '03


Isosurface extraction

w/ wavelets

w/o wavelets

Richtmyer-Meshkov instability: 1024 x 1024 x 1024

All-Hands Meeting '03


Isosurface extraction

w/ wavelets

w/o wavelets

Richtmyer-Meshkov instability: 1024 x 1024 x 1024

All-Hands Meeting '03


Isosurface extraction

w/ wavelets

w/o wavelets

Richtmyer-Meshkov instability: 1024 x 1024 x 1024

All-Hands Meeting '03


Isosurface extraction

w/ wavelets

w/o wavelets

Richtmyer-Meshkov instability: 1024 x 1024 x 1024

All-Hands Meeting '03


Isosurface extraction

w/ wavelets

w/o wavelets

Richtmyer-Meshkov instability: 1024 x 1024 x 1024

All-Hands Meeting '03


Isosurface extraction

w/ wavelets

w/o wavelets

Richtmyer-Meshkov instability: 1024 x 1024 x 1024

All-Hands Meeting '03


Isosurface extraction

w/ wavelets

w/o wavelets

Richtmyer-Meshkov instability: 1024 x 1024 x 1024

All-Hands Meeting '03


Isosurface extraction

w/ wavelets

w/o wavelets

Richtmyer-Meshkov instability: 1024 x 1024 x 1024

All-Hands Meeting '03


Isosurface extraction

w/ wavelets

w/o wavelets

Richtmyer-Meshkov instability: 1024 x 1024 x 1024

All-Hands Meeting '03


Isosurface extraction

w/ wavelets

w/o wavelets

Richtmyer-Meshkov instability: 1024 x 1024 x 1024

All-Hands Meeting '03


Isosurface extraction

w/ wavelets

w/o wavelets

Richtmyer-Meshkov instability: 1024 x 1024 x 1024

All-Hands Meeting '03


Isosurface extraction

w/ wavelets

w/o wavelets

Richtmyer-Meshkov instability: 1024 x 1024 x 1024

All-Hands Meeting '03


Isosurface extraction

w/ wavelets

w/o wavelets

Richtmyer-Meshkov instability: 1024 x 1024 x 1024

All-Hands Meeting '03


Isosurface extraction

w/ wavelets

w/o wavelets

Richtmyer-Meshkov instability: 1024 x 1024 x 1024

All-Hands Meeting '03


subdivision

Hypercube:

1

All-Hands Meeting '03


subdivision

All-Hands Meeting '03


Time-varying volume data

Argon bubble: 640 x 256 x 256 x 450

w/o wavelets

w/ wavelets

All-Hands Meeting '03


Overview

  • Wavelet-based multiresolution with subdivision

  • - regular

  • - provides high adaptivity

  • - time-varying

  • 2. Multiresolution over unstructured hexahedral grids

  • - irregular

  • Point-based high-resolution visualization

  • - regular / irregular

  • - simple rendering primitives

All-Hands Meeting '03


Unstructured hexahedral grids

Irregular multiresolution hierarchy:

2624 cells 824 cells 524 cells

All-Hands Meeting '03


Unstructured hexahedral grids

Visualization via cutting planes (oil pressure):

2624 cells 824 cells 524 cells

All-Hands Meeting '03


Time-varying unstructured hexahedral grids

Visualization via

isosurfaces

(oil concentration):

All-Hands Meeting '03


Overview

  • Wavelet-based multiresolution with subdivision

  • - regular

  • - provides high adaptivity

  • - time-varying

  • 2. Multiresolution over unstructured hexahedral grids

  • - irregular

  • Point-based high-resolution visualization

  • - regular / irregular

  • - simple rendering primitives

All-Hands Meeting '03


Point-based high-resolution visualization

High-resolution visualization at interactive frame rates

Bottleneck: Loading data from hard disk

  • Sort points by color value (color implicit, location stored)

  • Sort colors

  • Data encoding

    Rendering:

  • Point-based

  • Splatting

All-Hands Meeting '03


Point-based high-resolution visualization

Human brain: 1050 x 970 x 753

All-Hands Meeting '03


Point-based high-resolution visualization

Visible Female Human

(2048 x 1216 x 5186):

All-Hands Meeting '03


Conclusion

  • Exploration and visualization of

  • large-scale,

  • unstructured and/or

  • time-varying volume data

  • based on

  • multiresolution or

  • special storage scheme / data retrieval.

All-Hands Meeting '03


Acknowledgments

  • NPACI – SDSC, The Scripps Research Institute

  • CASC, Lawrence Livermore National Laboratory

  • Edward G. Jones, Center for Neuroscience, UC Davis

  • Mary Wheeler, Malgorzata Peszynska, TICAM, UT Austin

  • Arthur W. Toga, UCLA

  • Edward G. Jones, Center for Neuroscience, UC Davis

  • CASC, Lawrence Livermore National Laboratory

  • CCSE, Lawrence Berkeley National Laboratory

  • Victor M. Spitzer, National Library of Medicine

All-Hands Meeting '03


Contact

Lars Linsen

Center for Image Processing and Integrated Computing (CIPIC)

Department of Computer Science

University of California, Davis

llinsen@ucdavis.edu

http://graphics.cs.ucdavis.edu

All-Hands Meeting '03


ad
  • Login