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On-the-Fly Transformation and Rendering of

Compressed Irregular Volume Data

Chuan-kai YangDepartment of Computer Science

State University of New York at Stony Brook

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Volume Rendering

Raycasting: direct volume rendering:

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Raycasting Opacity, Color

f

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IrregularRegular

Regular/Irregular Grids

Cartesian grids rectilinear grids curvilinear grids

unstructured/tetrahedral gridshybrid grids

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Huge Data Sets

Technological advances in data acquisition devices

Data irregularity Huge data sets data sets are

stored in compressed format How to render a compressed data

set?

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Strategies

Decompression before rendering Latency, data loading time, memory

requirement On-the-fly decompression during

rendering On-the-fly rendering during

decompression Rendering in the compression

domain

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What’s Next? Visualization is too slow!Volume Simplification!

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Surface Simplification

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Volume Simplification

original

80% simplified

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Volume Simplification – Contd.

original 80% simplified 95% simplified

original 80% simp., metric 180% simp., metric 2

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Outline

Related work One-the-fly rendering of compressed

irregular grids – Gatun1 On-the-fly simplification and rendering

of compressed irregular grids – Gatun2 Time-critical rendering Conclusion Future/past work

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Outline

On-the-fly rendering of compressed irregular grids

On-the-fly simplification and rendering of compressed irregular grids

Time-critical rendering Conclusion Future/past work

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On-the-Fly Rendering of Compressed Irregular

Grids

Tetrahedron compression Garrity-Hong-Bunyk’s rendering Gatun’s rendering Performance results

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Tetrahedron Compression – 1

Represent the “fourth vertex” implicitly:

Edge-adjacent face

cur

fourth vertex

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Tetrahedron Compression – 2

Vertex-adjacent face

cur

fourth vertex

New Vertex Index

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Garrity-Hong-Bunyk’s Algo.

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G-H-B’s Algorithm – Contd.

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Gatun’s Inward Compression

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Gatun’s Rendering

G-H-B’s algorithm is already very fast, so we try to reduce the memory footprint…

Principle 1: once a decompressed tetrahedron is rendered, it can be thrown away (Garbage collection!)

Principle 2: a decompressed tetrahedron should be rendered as soon as possible

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Decompression Order

Tetrahedron decompression order may not be favored by the renderer

ab

A B

E

Decompressed order:

A, B, C, D, E

C

D

Check face projections

Vertex projection

Classification

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Classification

Using at most four cross-products, the projection of a given tetrahedron can be classified

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Readiness Check

A tetrahedron becomes “ready” only if the projection of its processed faces can cover its projection

Once a tetrahedron is rendered, all of its “unprocessed” faces become “processed”

Boundary faces first become “processed”

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Decomp. Order, Revisited

What if D, E are decompressed first?

AB

EC

D

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Bi-directional Rendering

Data set segment

sub-segsub-seg

high watermark

low watermark

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Performance Results

1

10

100

1000

10000

128x128 256x256 512x512 1024x1024

Generic 320M Gatun 320M Generic 160M

Gatun 160M Generic 80M Gatun 80M

1

10

100

1000

10000

128x128 256x256 512x512 1024x1024

Generic 320M Gatun 320M Generic 160M

Gatun 160M Generic 80M Gatun 80M

Liquid Oxygen Post Delta Wing

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Performance Results – Contd.

Six data sets: 180K to 1M tetrahedra

Out-of-core: one or two order of magnitudes better

In-core: 30% improvement Peak memory Saving: 50% to 70%

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Outline

On-the-fly rendering of compressed irregular grids

On-the-fly simplification and rendering of compressed irregular grids

Time-critical rendering Conclusion Future/past work

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On-the-Fly Simp. and Rend. of Compressed

Irregular Grids

Static volume simplification Run-time volume simplification On-the-fly simplification and

rendering of compressed irregular grids

Performance results

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Static Volume Simplification

Gelder ’99

116

8

74

98

7 4

6

98

74

6

9

8

74

6

98

74

6

98

74

6

9

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Static Volume Simp. – Contd.

Priority queue is used to build the simplification hierarchy

Each “vertex merge” is associated with a “rank”

Build the merge-trees structure

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Run-time Volume Simplification

8

6 2 13

3104111

5 7

1412

9 15

1

7 9 5

4810

136

212

14

3 11

Merge Tree

15 vertices14 vertex merges

Example: 7 vertex merges

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Decomp. & Simp. & Rendering

A

B

C

D

E

F

A

B

C

D

E

F

1

Rendering

G2

Vertex C: 2

3

3

Discard

2

Discard

4 Rendering

Rendering

Rendering

GC

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Performance ResultsGenericGatun2

blunt-fin

0

1

2

3

4

5

6

7

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

simplification ratio

rend

erin

g tim

e (s

ec)

fighter

0

0.5

1

1.5

2

2.5

3

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

simplification ratio

rend

erin

g tim

e (s

ec)

Simplification overhead: less then 5% of total execution time

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Outline

On-the-fly rendering of compressed irregular grids

On-the-fly simplification and rendering of compressed irregular grids

Time-critical rendering Conclusion Future/past work

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Time-critical Rendering

What’s missing? Determine the simplification ratio

Fixed frame rate Decompression overhead

More than 50% of total execution time at simplification ratio 0.9

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Multi-resolution Pre-simp.

Pre-simplified configurations Simplification ratio= 1 – 2-i, i= 0, 1, 2,

… Compression overhead is fixed

verticesof #

verticessimplified of # ratiotion simplifica

0 10.5 0.75 0.875 …

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Outline

On-the-fly rendering of compressed irregular grids

On-the-fly simplification and rendering of compressed irregular grids

Time-critical rendering Conclusion Future/past work

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Conclusion

A powerful scheme of combining lossless and lossy volume compressions in one framework <2.5 bits/tetra RMSE<0.12 (range: 0 – 255) at simp. ratio

0.9 Capable of time-critical rendering

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Outline

On-the-fly rendering of compressed irregular grids

On-the-fly simplification and rendering of compressed irregular grids

Time-critical rendering Conclusion Future/past work

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Future Work

Integration of compression, (view-independent of view-dependent) simplification and rendering for surface or volumetric meshes

Layered representation for out-of-core iso-surface extraction

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Layered Representation

Value-based decomposition v.s. Space-based decomposition

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Algorithm

Sub-range (layer) calculation Histogram: each sub-range should

capture roughly the same number of tetrahedra

Distribution: each tetrahedron is sent to the sub-range that intersects it

Binary search to locate a sub-range for a given iso-value query

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Discussion

Always a roughly fixed proportion is touched

More friendly to triangle strips generation

Each sub-mesh may be compressed On-the-fly iso-surface extraction of

compressed irregular grids

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Compression Domain Rendering of Regular

Grids

Whole volume FPST Pros and cons

Block-based FPST Pros and cons

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Whole Volume FPST

slice

2D FourierTransformImage plane

Spatial domain

3D FourierTransform

Frequency domain

projection

(X-ray like images)

Dunne et al. 90 and Malzbender et al. 93

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Pros and Cons

Asymptotically faster Aliasing and ghost effects along

boundary Xray-like, lack of self-occlusion

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Block-Based FPST

2D 3D

Chiueh et al. 97

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Pros and Cons

Approximated self-occlusion Aliasing and ghost effects along block

boundary Approximation with average Overlapped partition or not?

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Out-of-Core, I/O Conscious

Volume Rendering

Masking I/O by Computation! How to load blocks in a correct order?

Image plane

1

2

3

4

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

y

x

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Automatic Application-Specific File Prefetching

System

Source-to-Source I/O related code extraction

Modify Kernel to schedule the prefetch thread far ahead

Automatic I/O prefetching!

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Zodiac:A Video Authoring System

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Further Optimizations

Wasted effort on small tetrahedrons Pre-filtering Non-filtered and no-contribution

rate: < 10% Early-ray termination

Pseudo early-ray termination Single-segment rate:

> 90%

A B

CD