Transcript Introduction to Massive Model Visualization Patrick Cozzi Analytical Graphics, Inc. Contents      Minimal computer graphics background Culling Level of Detail (LOD) Memory Management Videos throughout.

Introduction to Massive Model
Visualization
Patrick Cozzi
Analytical Graphics, Inc.
Contents
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Minimal computer graphics background
Culling
Level of Detail (LOD)
Memory Management
Videos throughout
Computer Graphics Background
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Goal: Convert 3D model to pixels
3D models are composed of triangles
Computer Graphics Background
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1 triangle = 3 vertices
Gross over simplification: 3 floats per vertex
(x0, y0, z0)
(x1, y1, z1)
(x2, y2, z2)
Computer Graphics Background
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Triangles go through graphics pipeline to
become pixels
View parameters define the size and shape
of the world
far plane
near plane
viewer
Computer Graphics Background
PCIe Bus
CPU
GPU
Monitor
Color
Vertex
Processing
Geometry
Processing
Fragment
Processing
Depth
Stencil
Computer Graphics Background
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Visible Surfaces
Example Massive Models
Procedurally generated model of Pompeii: ~1.4 billion polygons.
Image from [Mueller06]
Example Massive Models
Boeing 777 model: ~350 million polygons.
Image from http://graphics.cs.uni-sb.de/MassiveRT/boeing777.html
Example Massive Models
Trends
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No upper bound on model complexity
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Procedural generation
Laser scans
Aerial imagery
Image from [Lakhia06]
Trends
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High GPU throughput
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At least 10-200 million triangles per second
Widen gap between processor and memory
performance
CPU – GPU bottleneck
Goal
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output-sensitivity: performance as a
function of the number of pixels
rendered, not the size of the model
View Frustum Culling
rendered
culled
culled
culled
rendered
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rendered
Can be slower than brute force. When?
Bounding Volumes
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Spheres
Axis aligned bounding boxes
Object oriented bounding boxes
Hybrids
View Frustum Culling
3
5
4
1
5
0
2
2
0
1
3
4
View Frustum Culling
3
5
4
1
5
0
2
2
0
1
3
4
View Frustum Culling
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Demo
Occlusion Culling
culled
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Effective in scenes with high depth
complexity
Occlusion Culling
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From-region or from-point
Most are conservative
Occluder Fusion
Difficult for general scenes with arbitrary
occluders. So make simplifying
assumptions:
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[Wonka00] – urban environments
[Ohlarik08] – planets and satellites
Hardware Occlusion Queries
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From-point visibility that handles general
scenes with arbitrary occluders and
occluder fusion
How?
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Use the GPU
Hardware Occlusion Queries
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Render occluders
Render object’s BV using HOQ
Render full object based on result
Hardware Occlusion Queries
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CPU stalls and GPU starvation
CPU
Draw o1 Draw o2 Draw o3
GPU
CPU
GPU
Draw o1 Draw o2 Draw o3
Query o1
-- stall --
Draw o1
Query o1 -- starve -- Draw o1
Hardware Occlusion Queries
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Demo
Is Culling Enough?
Is Culling Enough?
Level of Detail
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Generation: less triangles, simpler shader
Selection: distance, pixel size
Switching: avoid popping
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Discrete, Continuous, Hierarchical
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Simplification Operations
edge collapse
Also
• Vertex Merge
• Vertex Removal
• Cell Collapse
• See [Luebke01]
Discrete LOD
3,086 Triangles
52,375 Triangles
69,541 Triangles
Discrete LOD
Not enough detail
up close
Too much detail
in the distance
Continuous LOD
edge collapse
Image from [Luebke01]
vertex split
Hierarchical LOD
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1 Node
3,086 Triangles
4 Nodes
9,421 Triangles
16 Nodes
77,097 Triangles
Hierarchical LOD
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1 Node
3,086 Triangles
4 Nodes
9,421 Triangles
16 Nodes
77,097 Triangles
Hierarchical LOD
Node
Refinement
visit(node)
{
if (computeSSE(node) < pixel tolerance)
{
render(node);
}
else
{
foreach (child in node.children)
visit(child);
}
}
Hierarchical LOD
Hierarchical LOD
Hierarchical LOD
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Demo
Hierarchical LOD
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Easy to
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Add view frustum culling
Add occlusion culling via HOQs
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Render front to back
Use VMSSE to drive refinement
Requires preprocessing
Is Culling + HLOD enough?
Memory Management
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Out-of-Core
Compression
Cache Coherent Layouts
Out-of-Core HLOD
visit(node)
{
if ((computeSSE(node) < pixel tolerance) ||
(not all children resident))
{
render(node);
foreach (child in node.children)
requestResidency(child);
}
else
{
foreach (child in node.children)
visit(child);
}
}
Out-of-Core HLOD
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Multithreaded
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Cache management
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Disk reads
Decompression, normal generation, etc
Prioritize reads, e.g. distance from viewer
Replacement policy
Skeleton in memory?
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BV, error metric, parent – child relationships
Out-of-Core Prefetching
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Reduce geometry cache misses
Predict and load required nodes
Out-of-Core Prefetching
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Predict camera position [Correa03]
f’
v’
v
f
Out-of-Core Prefetching
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[Varadhan02]
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Coherence of Front
Prefetch ascendants/descendants
Prefetch with enlarged view frustum
Prioritize
0
1
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7
Compression
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“Size is Speed”
Geometric
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Vertices, Indices
I/O and Rendering Performance
Texture
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Performance or Quality
Disk
De/re-compress
Render
Cache Coherent Layouts
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Reorder vertices and indies to maximize
GPU cache hits
GPU Main
Memory
Pre VS
Cache
Reorder
Vertices
Vertex
Shader
Post VS
Cache
Reorder
Triangles
Primitive
Assembly
Cache Coherent Layouts
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Minimize ACMR
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Average Cache Miss Ratio
Cache Oblivious [Yoon05]
Linear Time [Sander07]
Not Covered Today
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Dynamic Scenes
Clustered backface culling
IBR, Mapping
Sorting
Batching
Ray Tracing
Summary
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Combine culling, LOD, and out-of-core
techniques
Keep the CPU and GPU busy
Exploit Coherence: Spatial and Temporal
For More Information
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[Gobbetti08] – Technical Strategies for
Massive Model Visualization
[Luebke01] – A Developer’s Survey of
Polygonal Simplification Algorithms
My email: [email protected]
About Analytical Graphics, Inc
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COTS software for national security and
space professionals
Rated best place to work: 2004-2006. 3rd in
2007.
Questions