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Introduction to Massive Model Visualization. Patrick Cozzi Analytical Graphics, Inc. Contents. Minimal computer graphics background Culling Level of Detail (LOD) Memory Management Videos throughout. Computer Graphics Background. Goal: Convert 3D model to pixels
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Introduction to Massive Model Visualization Patrick Cozzi Analytical Graphics, Inc.
Contents • Minimal computer graphics background • Culling • Level of Detail (LOD) • Memory Management • Videos throughout
Computer Graphics Background • Goal: Convert 3D model to pixels • 3D models are composed of triangles
(x0, y0, z0) (x1, y1, z1) (x2, y2, z2) Computer Graphics Background • 1 triangle = 3 vertices • Gross over simplification: 3 floats per vertex
far plane near plane viewer Computer Graphics Background • Triangles go through graphics pipeline to become pixels • View parameters define the size and shape of the world
Color Depth Stencil Computer Graphics Background PCIe Bus CPU GPU Monitor Vertex Processing Geometry Processing Fragment Processing
Computer Graphics Background • 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
Trends • No upper bound on model complexity • Procedural generation • Laser scans • Aerial imagery Image from [Lakhia06]
Trends • High GPU throughput • At least 10-200 million triangles per second • Widen gap between processor and memory performance • CPU – GPU bottleneck
Goal • output-sensitivity: performance as a function of the number of pixels rendered, not the size of the model
culled culled culled View Frustum Culling • Can be slower than brute force. When? rendered rendered rendered
Bounding Volumes • Spheres • Axis aligned bounding boxes • Object oriented bounding boxes • Hybrids
3 4 5 1 0 2 0 1 3 4 2 5 View Frustum Culling
3 4 5 1 0 2 0 1 3 4 2 5 View Frustum Culling
View Frustum Culling • Demo
culled Occlusion Culling • Effective in scenes with high depth complexity
Occlusion Culling • From-region or from-point • Most are conservative • Occluder Fusion • Difficult for general scenes with arbitrary occluders. So make simplifying assumptions: • [Wonka00] – urban environments • [Ohlarik08] – planets and satellites
Hardware Occlusion Queries • From-point visibility that handles general scenes with arbitrary occluders and occluder fusion • How? • Use the GPU
Hardware Occlusion Queries • Render occluders • Render object’s BV using HOQ • Render full object based on result
CPU Drawo1 Drawo2 Drawo3 GPU Drawo1 Drawo2 Drawo3 CPU Queryo1 -- stall -- Drawo1 GPU Queryo1 -- starve -- Drawo1 Hardware Occlusion Queries • CPU stalls and GPU starvation
Hardware Occlusion Queries • Demo
Level of Detail • Generation: less triangles, simpler shader • Selection: distance, pixel size • Switching: avoid popping • Discrete, Continuous, Hierarchical
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 • 1 Node • 3,086 Triangles 4 Nodes 9,421 Triangles 16 Nodes 77,097 Triangles
Hierarchical LOD • 1 Node • 3,086 Triangles 4 Nodes 9,421 Triangles 16 Nodes 77,097 Triangles
Node Refinement Hierarchical LOD visit(node) { if (computeSSE(node) < pixel tolerance) { render(node); } else { foreach (child in node.children) visit(child); } }
Hierarchical LOD • Demo
Hierarchical LOD • Easy to • Add view frustum culling • Add occlusion culling via HOQs • Render front to back • Use VMSSE to drive refinement • Requires preprocessing • Is Culling + HLOD enough?
Memory Management • 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 • Multithreaded • Disk reads • Decompression, normal generation, etc • Cache management • Prioritize reads, e.g. distance from viewer • Replacement policy • Skeleton in memory? • BV, error metric, parent – child relationships
Out-of-Core Prefetching • Reduce geometry cache misses • Predict and load required nodes
Out-of-Core Prefetching • Predict camera position [Correa03] f’ v’ v f
Out-of-Core Prefetching • [Varadhan02] • Coherence of Front • Prefetch ascendants/descendants • Prefetch with enlarged view frustum • Prioritize 0 1 4 5 6 7 2 3
Compression • “Size is Speed” • Geometric • Vertices, Indices • I/O and Rendering Performance • Texture • Performance or Quality Disk De/re-compress Render
Reorder Vertices Reorder Triangles Cache Coherent Layouts • Reorder vertices and indies to maximize GPU cache hits GPU Main Memory Pre VS Cache Vertex Shader Post VS Cache Primitive Assembly
Cache Coherent Layouts • Minimize ACMR • Average Cache Miss Ratio • Cache Oblivious [Yoon05] • Linear Time [Sander07]
Not Covered Today • Dynamic Scenes • Clustered backface culling • IBR, Mapping • Sorting • Batching • Ray Tracing
Summary • Combine culling, LOD, and out-of-core techniques • Keep the CPU and GPU busy • Exploit Coherence: Spatial and Temporal
For More Information • [Gobbetti08] – Technical Strategies for Massive Model Visualization • [Luebke01] – A Developer’s Survey of Polygonal Simplification Algorithms • My email: pjcozzi@siggraph.org