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Title: Thinking outside the


1
Thinking outside the Visualization Box
  • Ken Joy
  • Visualization and Graphics Research Group
  • Institute for Data Analysis and Visualization
  • Computer Science Department
  • University of California, Davis

2
People
  • Thanks to
  • Ben Gregorski, Chris Co, Serban Porumbescu, Karim
    Mahrous, Janine Bennett, Lok Hwa, IDAV, UC Davis
  • Mark Duchaineau, Peter Lindstrom, Valerio
    Pascucci, LLNL
  • Josh Senecal, LLNL, Davis
  • Hans Hagen, Kaiserslautern
  • Bernd Hamann, UC Davis
  • Note that the name of our organization has
    changed.

3
The Problem
This is a portion of an isosurface taken from the
final time step of a Richtmeyer-Meshkov
instability shock-tube simulation.
The simulation was generated on a 2000x2000x2000
grid. An isosurface of 460 million triangles was
generated by marching cubes.
4
The Problem
5
Simplification, Wavelets, Subdivision
  • We simplified the mesh.
  • We generated a fine mesh from a simplified base
    mesh using Catmull-Clark subdivision.
  • Invented new Catmull-Clark wavelets to preserve
    features.
  • Lots of publications,

6
Original
7
16 of the coefficients
8
5 of the coefficients
9
1.6 of the coefficients
10
Base Mesh
11
Patches
12
What did we learn?
  • We worked on this data set for almost two years,
    and still didnt have a interactive renderable
    version.
  • Occlusion problems were killing us!
  • Features were disappearing!
  • and this was only one isovalue!!!
  • Data size of this one isosurface exceeded the
    size of the original data set.
  • We had to think outside of Multiresolution
    Analysis to find a solution

13
What did we do?
  • Dynamic generation of the isosurface.
  • Dynamic extraction based upon a 3D
    longest-edge-bisection tetrahedral mesh which is
    refined and coarsened depending on viewpoint,
    error, and frame rate.
  • Algorithm depends on an innovative storage scheme
    for the multiresolution data, and an occlusion
    scheme, allowing real-time display of the
    isosurface.

14
Dynamic Generation of Isosurfaces
15
Results!
  • Dynamic Isosurface Generation Gregorski, et al.
    IEEE Visualization 2002
  • Time-Varying Isosurfaces Gregorski, et al.
    TVCG, 2004
  • Compression and Occlusion Gregorski, et al.
    IEEE Visualization 2004, submitted.

16
Lessons?
  • We learned that we must think out of the box to
    solve major problems.
  • Key words
  • Large, massive, terascale
  • Time varying
  • Interactive, real-time
  • Time critical
  • Mathematically sound

17
What happens if the texture is too big?
18
Dynamic sensor data!
  • Potentially billions of sensors
  • Sensors are scattered and may move in time.

19
Problems!
  • Data is scattered.
  • Data may move in time.
  • Applications are time-varying and time-critical.
  • We cannot afford to generate a mesh for each time
    step.
  • Can we develop visualization methods that do not
    depend on a fixed mesh?

20
Meshless Isosurfaces
Isosplatting, Co, et al., 2003
21
Meshless Isosurfaces
Isosplatting, Co, et al., 2003
22
Meshless Isosurfaces
  • Solves the multi-block isosurface problem
  • i.e., no cracks.

Co, et al., VisSym 2004
23
Lessons?
  • Many mesh problems can be solved by meshless
    techniques.
  • Instead of one algorithm for each mesh type
  • Thinking outside the mesh

24
Thinking Outside the Visualization Box
  • Perhaps innovation in visualization can best be
    achieved by Thinking outside the box.
  • In our research, we frequently think inside,
    working on the little problems that improve the
    existing algorithms.
  • What about new algorithms, new techniques,
    new approaches?

25
Think outside the Multiresolution box
  • Think outside subdivision, wavelets,
    splines, mesh simplification, etc.
  • "The key to terascale visualization is deciding
    what to visualize."
  • The key is to focus the data exploration, not to
    show absolutely everything.
  • Queries on Scientific Data Sets.
  • Find those regions of interest to the user.
  • Carr, Banff 2004 Volumetric Queries

26
Think outside the Mesh
  • Perhaps think Meshless
  • Solves many problems!

27
Think outside the Big Three
  • Are there other fundamental visualization
    algorithms besides Slicing, Isosurfaces, and
    Volume Rendering for scalar fields

28
Segmentation Techniques
Nielson and Franke, IEEE Visualization 1997
Bonnell, et al., IEEE Visualization 2000, TVCG
2003.
Mahrous, et al. TVCG 2004.
29
Think outside the Heuristic
  • Edelsbrunner Corollary (Banff, 2004)
  • Work in the sunshine!
  • Work in the world of theorems. Theorems require
    deeper thinking about the subject and can show
    ways to get out of the box.
  • Corollary Think outside linear

30
Think outside the Scalar Field
  • Vector Fields
  • Tensor Fields
  • DT-MRI Can we work with the original data?
  • Multi-valued Fields
  • Distributions at each data point
  • Air Quality Problems
  • Radiation Transport Simulations
  • Distributions at each data point
  • Vector distribution at each data point
  • Tensor distribution at each data point

31
Think outside 2D
  • Three-dimensional time-varying multivalued data
    exploration is HARD. Lets focus our activities
    there.
  • What are separatrices in 3-dimensional vector
    fields?
  • What are the topological properties of a
    3-dimensional tensor field?

32
Think outside Static
  • The problems of the world are not static
    problems!
  • Blood flow cannot be stopped during an MRI (on a
    human, at least)
  • Time-varying Data
  • Time Critical Data
  • User-controlled visualizations.

33
Think outside SciVis and InfoVis
  • Isnt SciVis just InfoVis with spatial location?
  • Focus Context in SciVis?

34
Think outside the Pretty Picture
  • AARGH!!!
  • It looks good this way
  • Pleasing to my eye
  • Color scheme implies nothing!
  • I believe that our field should be called Data
    Exploration
  • Does it convey the correct information?
  • Is it right? (Mike Kirby)

35
Think outside the GPU
  • Programming the GPU is Research in Programming

36
Think outside the Academic Problem
  • Hitching our research to someone elses driving
    application and solving those problems on the
    owners terms, leads us to richer visualization
    research. (Fred Brooks, 1996)
  • Visualization is 40 papers per year. (Banks,
    Banff 2004) Therefore, since 1990, the
    visualization field consists of approximately 550
    papers.
  • Where are our clients? (Lorensen, 2003)
  • Is visualization a relevant field?

37
Kens Eleven
  • Think outside the Multiresolution box
  • Think outside the Mesh
  • Think outside the Big Three
  • Think outside the Heuristic
  • Think outside the Scalar Field
  • Think outside 2D
  • Think outside Static
  • Think outside SciVis and InfoVis
  • Think outside the Pretty Picture
  • Think outside the GPU
  • Think outside the Academic Problems

38
HELP ME!
  • Where do we need to think outside the box?
  • What are the right questions to ask?

39
Thank You
joy_at_cs.ucdavis.edu http//graphics.cs.ucdavis.edu
/joy
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