Visualization Enhancements of Dense Particle Data Sets James L. Bigler School of Computing...

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Visualization Enhancements of Dense Particle Data Sets James L. Bigler School of Computing University of Utah November 8, 2004
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Page 1: Visualization Enhancements of Dense Particle Data Sets James L. Bigler School of Computing University of Utah November 8, 2004.

Visualization Enhancements of Dense Particle Data Sets

James L. BiglerSchool of Computing

University of UtahNovember 8, 2004

Page 2: Visualization Enhancements of Dense Particle Data Sets James L. Bigler School of Computing University of Utah November 8, 2004.

Outline

• Motivation and Introduction

• Ambient Occlusion Shading

• Silhouette Edges

Page 3: Visualization Enhancements of Dense Particle Data Sets James L. Bigler School of Computing University of Utah November 8, 2004.

Phong Shaded

Data courtesy: Gerald T. Seidler University of Washington

Page 4: Visualization Enhancements of Dense Particle Data Sets James L. Bigler School of Computing University of Utah November 8, 2004.

With Silhouettes

Page 5: Visualization Enhancements of Dense Particle Data Sets James L. Bigler School of Computing University of Utah November 8, 2004.

With Ambient Occlusion

Page 6: Visualization Enhancements of Dense Particle Data Sets James L. Bigler School of Computing University of Utah November 8, 2004.

With Ambient Occlusion and SilhouettesSilhouettes

Page 7: Visualization Enhancements of Dense Particle Data Sets James L. Bigler School of Computing University of Utah November 8, 2004.

Why Particle Visualization?

Macro Micro Crop by value

Page 8: Visualization Enhancements of Dense Particle Data Sets James L. Bigler School of Computing University of Utah November 8, 2004.

How are Particles Visualized?

Page 9: Visualization Enhancements of Dense Particle Data Sets James L. Bigler School of Computing University of Utah November 8, 2004.

Local Lighting Models

Good for local (micro) structure, bad for global (macro) structure.

Page 10: Visualization Enhancements of Dense Particle Data Sets James L. Bigler School of Computing University of Utah November 8, 2004.

Shadows

Page 11: Visualization Enhancements of Dense Particle Data Sets James L. Bigler School of Computing University of Utah November 8, 2004.

Global Illumination

• Variation in ambient regions

• Soft shadows

• Interreflection of light between surfaces

Page 12: Visualization Enhancements of Dense Particle Data Sets James L. Bigler School of Computing University of Utah November 8, 2004.

Wyman Global Illumination for Interactive Isosurfaces

• Wyman et al. cached global illumination values on a grid. Goal was to maintain interactivity during rendering.

Page 13: Visualization Enhancements of Dense Particle Data Sets James L. Bigler School of Computing University of Utah November 8, 2004.

Ambient Occlusion or Obscurances

• Zhukov et al. Iones et al.

• Precomputed

• Stored as textures

• Geometric property

Page 14: Visualization Enhancements of Dense Particle Data Sets James L. Bigler School of Computing University of Utah November 8, 2004.

Vicinity Shading

• James Stewart• Similar to Wyman, precomputes and stores

in a texture volume for later use in interactive applications.

Page 15: Visualization Enhancements of Dense Particle Data Sets James L. Bigler School of Computing University of Utah November 8, 2004.

Silhouette Edges from Depth Buffer

• Usually black, emphasizes view dependent hull of objects

• Saito and Takahashi (“Comprehensible Rendering of 3-D Shapes”)– Cache various aspects of the

rendered image – Use depth and convolution

to find silhouette edges

Page 16: Visualization Enhancements of Dense Particle Data Sets James L. Bigler School of Computing University of Utah November 8, 2004.

Particle Ray Tracing

• Parker et al. show in “Interactive ray tracing” that large numbers of particles can interactively be rendered using a parallel ray tracer.

Page 17: Visualization Enhancements of Dense Particle Data Sets James L. Bigler School of Computing University of Utah November 8, 2004.

Ambient Occlusion

Page 18: Visualization Enhancements of Dense Particle Data Sets James L. Bigler School of Computing University of Utah November 8, 2004.

Texture Resolution

• 16x16 provides a nice compromise– Fidelity– Memory– Computation time

Page 19: Visualization Enhancements of Dense Particle Data Sets James L. Bigler School of Computing University of Utah November 8, 2004.

Precomputation Time and Memory

• Using 20 R14K processors on an SGI Origin 3800 (muse.sci.utah.edu). Textures were 16x16 with 49 samples per texel.

955,000

66 min.

233 MB

952,755

261 min.

232 MB

543,088

33 min.

132 MB

7,157,720

12 hours

1,747 MB

Fireball Bullet Foam

Page 20: Visualization Enhancements of Dense Particle Data Sets James L. Bigler School of Computing University of Utah November 8, 2004.

Impact on Performance• 10% slower than direct lighting alone.

• However, using only the ambient occlusion values can yield as good as or better performance than direct lighting alone.

Direct lighting

DL with Textures

Textures w/o DL

Fireball 6 16.43 f/s 14.97 f/s 16.75 f/s

Fireball 11 10.55 f/s 9.59 f/s 10.16 f/s

Cylinder 6 13.32 f/s 12.15 f/s 13.37 f/s

Cylinder 22

11.71 f/s 10.94 f/s 11.75 f/s

Bullet 2 28.17 f/s 25.59 f/s 28.79 f/s

Bullet 12 28.76 f/s 25.71 f/s 28.41 f/s

Page 21: Visualization Enhancements of Dense Particle Data Sets James L. Bigler School of Computing University of Utah November 8, 2004.

ImagesDirect Lighting

onlyDirect lighting with ambient occlusion

textures

Ambient occlusion textures only

Cylinder 22 Bullet 6 Fireball 11

Page 22: Visualization Enhancements of Dense Particle Data Sets James L. Bigler School of Computing University of Utah November 8, 2004.

Impact on Performance• 10% slower than direct lighting alone.

• However, using only the ambient occlusion values can yield as good as or better performance than direct lighting alone.

Direct lighting

DL with Textures

Textures w/o DL

Fireball 6 16.43 f/s 14.97 f/s 16.75 f/s

Fireball 11 10.55 f/s 9.59 f/s 10.16 f/s

Cylinder 6 13.32 f/s 12.15 f/s 13.37 f/s

Cylinder 22

11.71 f/s 10.94 f/s 11.75 f/s

Bullet 2 28.17 f/s 25.59 f/s 28.79 f/s

Bullet 12 28.76 f/s 25.71 f/s 28.41 f/s

Page 23: Visualization Enhancements of Dense Particle Data Sets James L. Bigler School of Computing University of Utah November 8, 2004.

Silhouette Edges

• Two options– Precomputation

(object based)– Run time

• Object based

• Image based

Page 24: Visualization Enhancements of Dense Particle Data Sets James L. Bigler School of Computing University of Utah November 8, 2004.

Ingredients for Edges

• Image buffer • Depth buffer• Edge detection kernel• Threshold for zero

crossings

-1 -1 -1

-1 8 -1

-1 -1 -1Laplacian kernel

Page 25: Visualization Enhancements of Dense Particle Data Sets James L. Bigler School of Computing University of Utah November 8, 2004.

Threshold Edge Response

Page 26: Visualization Enhancements of Dense Particle Data Sets James L. Bigler School of Computing University of Utah November 8, 2004.

• Anatomy of a ray

• If a and |b| are the same for each pixel we can use the collection of t as a depth buffer.

Depth Buffer

p(t) = a + tb

t

Page 27: Visualization Enhancements of Dense Particle Data Sets James L. Bigler School of Computing University of Utah November 8, 2004.

Performance

Without With

A 17.064 f/s 16.056 f/s

B 2.220 f/s 2.179 f/s

C 2.220 f/s 2.197 f/s

D 1.155 f/s 1.162 f/s

E 2.683 f/s 2.632 f/s

A

B C

D E

Page 28: Visualization Enhancements of Dense Particle Data Sets James L. Bigler School of Computing University of Utah November 8, 2004.

Movie

Page 29: Visualization Enhancements of Dense Particle Data Sets James L. Bigler School of Computing University of Utah November 8, 2004.

Movie

Page 30: Visualization Enhancements of Dense Particle Data Sets James L. Bigler School of Computing University of Utah November 8, 2004.

Questions?