1. Multiresolution View-Dependent Splat Based Volume Rendering of Large Irregular Data
Jeremy Meredith, Lawrence Livermore National Laboratory
Kwan-Liu Ma, University of California, Davis

2. Multi-res View-Dependent Splatting
Highlights:
Visualize very large irregular data interactively
Pro:
Multi-res: hierarchical oct-tree
View-dependent
Splatting
Con:
Oct-tree add much overhead to store volume data

3. Multi-resolution
Hierarchical oct-tree

4. Splatting
Integral of emitted light at each point on image plane

5. Splatting Low-Albedo Optical Model
The amount of emitted light received at location x at the image plane
Use Taylor approximation
Here, If If(x) is the amount of light at location x in the frame buffer, and αnew(x), Inew(x) are the new incoming opacities and light intensities, respectively
OpenGL blending function
glBlendFunc(GL SRC ALPHA,GL ONE MINUS SRC ALPHA)

6. EVL Internal Volume Visualization Workshop
Charles Zhang
EVL, UIC

7. Our Goals Very large data
Difficulties: WAN, beyond local RAM, interactivity
Is multi-res necessary?
Pro: fast access, exploration convenient, scalability, pre-fetch compatible
Con: a pre-processing must
Which mutli-res: wavelet?
Is view-dependence necessary?
Pro: no redundant data fetch, easy pre-fetching, good for stereo-display(?)
Con: overhead?
Parallel Volume Rendering
Image-ordering or object ordering?
IO: accurate
OO: fast, suitable for view-dependence
Sort-first or sort-last
Sort-first uses less bandwidth

8. Our Goals (cont’d) Interactivity Fast overview?
Multi-res, view-dependent?
Fast view point change?
Tolerance is high when view moving fast.
Distributed / Parallel Computing
Distributed data on WAN?
Is the data scalable, what’s the limit for our viz tools
Load-balancing on rendering cluster
Multi-tile display
The display is scalable?