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![Page 1: 1 On-the-Fly Transformation and Rendering of Compressed Irregular Volume Data Chuan-kai Yang Department of Computer Science State University of New York.](https://reader036.fdocuments.net/reader036/viewer/2022081519/56649ed35503460f94be423e/html5/thumbnails/1.jpg)
1
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
![Page 13: 1 On-the-Fly Transformation and Rendering of Compressed Irregular Volume Data Chuan-kai Yang Department of Computer Science State University of New York.](https://reader036.fdocuments.net/reader036/viewer/2022081519/56649ed35503460f94be423e/html5/thumbnails/13.jpg)
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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
![Page 28: 1 On-the-Fly Transformation and Rendering of Compressed Irregular Volume Data Chuan-kai Yang Department of Computer Science State University of New York.](https://reader036.fdocuments.net/reader036/viewer/2022081519/56649ed35503460f94be423e/html5/thumbnails/28.jpg)
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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
![Page 35: 1 On-the-Fly Transformation and Rendering of Compressed Irregular Volume Data Chuan-kai Yang Department of Computer Science State University of New York.](https://reader036.fdocuments.net/reader036/viewer/2022081519/56649ed35503460f94be423e/html5/thumbnails/35.jpg)
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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
![Page 38: 1 On-the-Fly Transformation and Rendering of Compressed Irregular Volume Data Chuan-kai Yang Department of Computer Science State University of New York.](https://reader036.fdocuments.net/reader036/viewer/2022081519/56649ed35503460f94be423e/html5/thumbnails/38.jpg)
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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
![Page 40: 1 On-the-Fly Transformation and Rendering of Compressed Irregular Volume Data Chuan-kai Yang Department of Computer Science State University of New York.](https://reader036.fdocuments.net/reader036/viewer/2022081519/56649ed35503460f94be423e/html5/thumbnails/40.jpg)
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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