1. Afrigraph 2004 Tutorial A: Part I Rasterization Based Approaches Andreas Dietrich Computer Graphics Group, Saarland University Saarbrücken, Germany

2. COMPUTER GRAPHIK – UNIVERSITÄT DES SAARLANDES Afrigraph 2004State of the Art in Massive Model Visualization2 Overview Part I – Rasterization Based ApproachesPart I – Rasterization Based Approaches –Visibility Culling -Hierarchical Z-Buffer -Hierarchical Occlusion Maps -Prioritized-Layered Projection –Simplification Techniques -LODs / HLODs, -Textured Depth Meshes –Existing Architectures -MMR -Gigawalk -iWalk

3. COMPUTER GRAPHIK – UNIVERSITÄT DES SAARLANDES Afrigraph 2004State of the Art in Massive Model Visualization3 Overview Part I – Rasterization Based ApproachesPart I – Rasterization Based Approaches  Visibility Culling -Hierarchical Z-Buffer -Hierarchical Occlusion Maps -Prioritized-Layered Projection –Simplification Techniques -LODs / HLODs, -Textured Depth Meshes –Existing Architectures -MMR -Gigawalk -iWalk

4. COMPUTER GRAPHIK – UNIVERSITÄT DES SAARLANDES Afrigraph 2004State of the Art in Massive Model Visualization4 Visibility Culling Large scenes often densely occludedLarge scenes often densely occluded –Only a fraction of the total dataset visible  Visibility culling –Try to find the visible set i.e. objects that contribute to the image –Goal: -Rejecting large parts of the scene before actual HSR -Reduce rendering cost to complexity of visible portion -Ideally output sensitive : Running time proportional to visible set size

5. COMPUTER GRAPHIK – UNIVERSITÄT DES SAARLANDES Afrigraph 2004State of the Art in Massive Model Visualization5 Visibility Culling Culling techniquesCulling techniques –View-frustum culling -Reject geometry outside the viewing volume –Back-face culling -Reject geometry facing away from the observer –Occlusion culling -Reject objects occluded by others

6. COMPUTER GRAPHIK – UNIVERSITÄT DES SAARLANDES Afrigraph 2004State of the Art in Massive Model Visualization6 Visibility Culling Occlusion Culling Occlusion cullingOcclusion culling –Not as trivial as view-frustum or back-face culling –Often requires preprocessing –Usually involving some scene hierarchy -Occlusion tests performed top-down –Difference to Hidden surface removal (HSR) -Does not identify exact potion of visible polygons -Tries to identify objects not visible -Often exact HSR follows after culling step –However, distinction not that clear -Some HSR algorithms feature built-in occlusion culling e.g. Ray casting (see Part II)

7. COMPUTER GRAPHIK – UNIVERSITÄT DES SAARLANDES Afrigraph 2004State of the Art in Massive Model Visualization7 Visibility Culling Occlusion Culling Main classification [Cohen-Or 03]Main classification [Cohen-Or 03] –From-point methods -Computation with respect to current viewpoint –Image precision variants: Operate on fragments –Object precision variants: Operate on raw objects –From-region methods -Bulk computations valid for a specific region –Cell-and-portal variants: Exploit scene characteristics –Generic scene variants: Work with arbitrary scenes

8. COMPUTER GRAPHIK – UNIVERSITÄT DES SAARLANDES Afrigraph 2004State of the Art in Massive Model Visualization8 Visibility Culling Occlusion Culling Additional classification criteria [Cohen-Or 03]Additional classification criteria [Cohen-Or 03] –Conservative vs. approximate techniques –Tightness of approximation –All objects vs. subset of occluders –Convex vs. generic occluders –Individual vs. fused occluders –2D vs. 3D –Special hardware requirements –Need of precomputation –Dynamic scenes

9. COMPUTER GRAPHIK – UNIVERSITÄT DES SAARLANDES Afrigraph 2004State of the Art in Massive Model Visualization9 Visibility Culling Hierarchical Z-Buffer Hierarchical Z-Buffer (HZB) [Greene 93]Hierarchical Z-Buffer (HZB) [Greene 93] –Exploits object-space coherence: Octree subdivision –Exploits Image-space coherence: Z-pyramid Octree used forOctree used for –View-frustum culling –Hierarchic top-down rendering / occlusion –Front-back rendering Z-PyramidZ-Pyramid –Use original Z-buffer as finest level –Combine 2x2 samples by choosing farthest Z value

10. COMPUTER GRAPHIK – UNIVERSITÄT DES SAARLANDES Afrigraph 2004State of the Art in Massive Model Visualization10 Visibility Culling Hierarchical Z-Buffer Z-pyramid visibility queryZ-pyramid visibility query –Polygon visibility test: 1.Find finest-level pyramid sample covering screen-space bounding box of polygon 2.If nearest polygon Z value farther away than sample Z value  Polygon hidden Otherwise subdivide polygon and recurse –Allows for fast octree node occlusion query: 1.Test projected octree node faces 2.If node is hidden  All polygons inside hidden Otherwise subdivide node and recurse

11. COMPUTER GRAPHIK – UNIVERSITÄT DES SAARLANDES Afrigraph 2004State of the Art in Massive Model Visualization11 Visibility Culling Hierarchical Occlusion Maps Hierarchical Occlusion Map (HOM) [Zhang 97]Hierarchical Occlusion Map (HOM) [Zhang 97] –Pixels record opacity of screen space regions –Construction: 1.Select occluders: E.g. visible objects from previous frame 2.Render occluders: Pure white pixels on black background 3.Form next coarser level: Average 2x2 pixel regions

12. COMPUTER GRAPHIK – UNIVERSITÄT DES SAARLANDES Afrigraph 2004State of the Art in Massive Model Visualization12 Visibility Culling Hierarchical Occlusion Maps Object occlusion testObject occlusion test 1.Find hierarchy level with pixels approximately the same size as screen-space object bounding box 2.Examine each pixel in map overlapping bounding rectangle: If all pixel completely opaque  Objects projection inside occluders  Z-test: -Single Z-plane behind all occluders -Depth estimation buffer (Z-planes for separate screen regions) Otherwise check next level for not completely opaque pixels  Use transparency threshold to terminate recursion  Render object using Z-buffer

13. COMPUTER GRAPHIK – UNIVERSITÄT DES SAARLANDES Afrigraph 2004State of the Art in Massive Model Visualization13 Visibility Culling PLP Prioritized-Layered Projection [Klosowski 00]Prioritized-Layered Projection [Klosowski 00] –Approximate occlusion culling -Estimates visible primitives -Renders the primitives most likely visible up to a given budget –Scene partitioned into cells –Basic Idea: Cells containing much geometry are likely to occlude other cells  Render cells front-to-back in layers  Use probabilistic values to prioritize cell rendering order (depending on viewpoint settings)

14. COMPUTER GRAPHIK – UNIVERSITÄT DES SAARLANDES Afrigraph 2004State of the Art in Massive Model Visualization14 Visibility Culling PLP Priority-Based Cell TraversalPriority-Based Cell Traversal –Maintain priority queue (front ) -Contains cells to be rendered next -Front „advances“ from the viewpoint into the view frustum: 1.Remove cell (depending on priority) from the front 2.Render it (using z-Buffer) 3.Add adjacent cells to the front with updated priority 4.Continue until triangle budget reached

15. COMPUTER GRAPHIK – UNIVERSITÄT DES SAARLANDES Afrigraph 2004State of the Art in Massive Model Visualization15 Visibility Culling PLP Priority-Based Cell TraversalPriority-Based Cell Traversal –As priority use solidity -Heuristic to determine how difficult it is to see a particular cell -No inherent property of a cell -Accumulated during rendering, depending on viewpoint –Solidity is transferred to neighboring cells

16. COMPUTER GRAPHIK – UNIVERSITÄT DES SAARLANDES Afrigraph 2004State of the Art in Massive Model Visualization16 Overview Part I – Rasterization Based ApproachesPart I – Rasterization Based Approaches –Visibility Culling -Hierarchical Z-Buffer -Hierarchical Occlusion Maps -Prioritized-Layered Projection  Simplification Techniques -LODs / HLODs, -Textured Depth Meshes –Existing Architectures -MMR -Gigawalk -iWalk

17. COMPUTER GRAPHIK – UNIVERSITÄT DES SAARLANDES Afrigraph 2004State of the Art in Massive Model Visualization17 Simplification Polygonal Simplification Idea:Idea: –Simplify small or distant model parts without significant loss in the scene‘s visual appearance –Switch at runtime between different complexity levels  Reduce I/O bandwidth  Improve runtime performance

18. COMPUTER GRAPHIK – UNIVERSITÄT DES SAARLANDES Afrigraph 2004State of the Art in Massive Model Visualization18 Simplification Polygonal Simplification ClassificationClassification –Static simplification -Offline computation of discrete versions of each object: Levels-of-Detail (LODs) –Dynamic simplification -Data structures encoding a continuous detail spectrum –View-dependent simplification -Single objects can span multiple simplification levels

19. COMPUTER GRAPHIK – UNIVERSITÄT DES SAARLANDES Afrigraph 2004State of the Art in Massive Model Visualization19 Simplification Polygonal Simplification TechniquesTechniques –Sampling -Sample initial model e.g. with points on surface –Adaptive subdivision -Find base mesh approximating initial model and subdivide –Decimation -Remove vertices and retriangulate resulting holes –Vertex-merging -Collapse two or more vertices, remove degenerated triangles -Special case: Edge collapse (merge 2 vertices per step) -Use error metric to decide which vertices to collapse, e.g. Quadratic Error Metrics (QEM) [Garland 97]

20. COMPUTER GRAPHIK – UNIVERSITÄT DES SAARLANDES Afrigraph 2004State of the Art in Massive Model Visualization20 Simplification Polygonal Simplification Hierarchical Level of Detail (HLOD) [Erikson 01]Hierarchical Level of Detail (HLOD) [Erikson 01] –Use LODs for each scene graph node –HLODs: Simplification of entire scene graph branches  Higher fidelity than separate approximations  Simplify scene graph traversal

21. COMPUTER GRAPHIK – UNIVERSITÄT DES SAARLANDES Afrigraph 2004State of the Art in Massive Model Visualization21 Simplification Image-Based Methods Polygonal simplification sometimes difficultPolygonal simplification sometimes difficult – E.g. because of unsuitable scene structure  Image-Based Rendering (IBR) -Image-based entities (impostors) as alternative representation for scene parts -Simple variant: Billboards

22. COMPUTER GRAPHIK – UNIVERSITÄT DES SAARLANDES Afrigraph 2004State of the Art in Massive Model Visualization22 Simplification Image-Based Methods Textured Depth Meshes (TDM) [Sillion 97]Textured Depth Meshes (TDM) [Sillion 97] –Simple polygon mesh of rough scene structure –Textured with detailed model images  Better parallax movement  Longer valid when viewpoint changes

23. COMPUTER GRAPHIK – UNIVERSITÄT DES SAARLANDES Afrigraph 2004State of the Art in Massive Model Visualization23 Overview Part I – Rasterization Based ApproachesPart I – Rasterization Based Approaches –Visibility Culling -Hierarchical Z-Buffer -Hierarchical Occlusion Maps -Prioritized-Layered Projection –Simplification Techniques -LODs / HLODs, -Textured Depth Meshes  Existing Architectures -MMR -Gigawalk -iWalk

24. COMPUTER GRAPHIK – UNIVERSITÄT DES SAARLANDES Afrigraph 2004State of the Art in Massive Model Visualization24 Existing Architectures MMR MMR [Aliaga 99]MMR [Aliaga 99] –Massive Model Rendering framework –One of first systems capable of interactive display of a 12.5 million power plant scene (out-of-core rendering) –Modular System: Incorporates a variety of techniques -Visibility Culling -Mesh simplification -Static LODs -Hierarchical occlusion maps -Textured depth meshes

25. COMPUTER GRAPHIK – UNIVERSITÄT DES SAARLANDES Afrigraph 2004State of the Art in Massive Model Visualization25 Existing Architectures MMR Basic Idea:Basic Idea: –Partition scene into viewpoint cells (not automatic) –Associate cull box with each viewpoint-cell –For every observer position inside a viewpoint cell -Clip geometry against cull box -Replace clipped geometry with TDMs

26. COMPUTER GRAPHIK – UNIVERSITÄT DES SAARLANDES Afrigraph 2004State of the Art in Massive Model Visualization26 Existing Architectures MMR Textured depth meshesTextured depth meshes –Pre-generated images of geometry outside cull box viewed from cell center –Images projected onto simplified depth meshes

27. COMPUTER GRAPHIK – UNIVERSITÄT DES SAARLANDES Afrigraph 2004State of the Art in Massive Model Visualization27 Existing Architectures MMR Run-time pipelineRun-time pipeline

28. COMPUTER GRAPHIK – UNIVERSITÄT DES SAARLANDES Afrigraph 2004State of the Art in Massive Model Visualization28 Existing Architectures MMR Multiprocessor pipelined implementationMultiprocessor pipelined implementation –Interframe phase –Cull phase –Render phase –Prefetch phase Only a single rendering pipeline used

29. COMPUTER GRAPHIK – UNIVERSITÄT DES SAARLANDES Afrigraph 2004State of the Art in Massive Model Visualization29 Existing Architectures MMR ResultsResults –SGI Onyx with Infinite Reality graphics –Frame rates: 5-15 fps –Only 0.9% of original polygons need to be rendered –However: Popping and distortion when switching cells –Preprocessing time: -17 hours for cells on selected sample camera paths -525 hours for complete model (estimated)

30. COMPUTER GRAPHIK – UNIVERSITÄT DES SAARLANDES Afrigraph 2004State of the Art in Massive Model Visualization30 Existing Architectures Gigawalk Gigawalk [Baxter 02]Gigawalk [Baxter 02] –Fully automatic scene organization (BVH) –Hierarchical Z-Buffer –Static LODs / HLODs –Uses two rendering pipelines -Parallel rendering of occluders and visible geometry –Can exploit temporal coherence -Use of visible geometry from a previous frame as occluders

31. COMPUTER GRAPHIK – UNIVERSITÄT DES SAARLANDES Afrigraph 2004State of the Art in Massive Model Visualization31 Existing Architectures Gigawalk System architectureSystem architecture –3 Processes running in parallel -Occluder Rendering (OC) -Scene Traversal, Culling, LOD selection (STC) -Rendering Visible Scene Geometry (RVG)

32. COMPUTER GRAPHIK – UNIVERSITÄT DES SAARLANDES Afrigraph 2004State of the Art in Massive Model Visualization32 Existing Architectures Gigawalk Timing relationshipTiming relationship –Frame i uses occluders from frame i-2

33. COMPUTER GRAPHIK – UNIVERSITÄT DES SAARLANDES Afrigraph 2004State of the Art in Massive Model Visualization33 Existing Architectures Gigawalk ResultsResults –SGI Onyx with Infinite Reality graphics, dual graphics rasterization pipelines –Frame rates: 11-50 fps (tanker model: 82 million triangles) –Preprocessing time: 35 hours (Pentium IV, 2GHz) –However: No out-of-core rendering

34. COMPUTER GRAPHIK – UNIVERSITÄT DES SAARLANDES Afrigraph 2004State of the Art in Massive Model Visualization34 Existing Architectures iWalk iWalk [Correa 03]iWalk [Correa 03] –Interactive frame rates on a single commodity PC –Prioritized-Layered Projection -Approximate visibility -Budget-based rendering -Optionally conservative variant (cPLP) –Efficient prefetching –Out-of-core rendering

35. COMPUTER GRAPHIK – UNIVERSITÄT DES SAARLANDES Afrigraph 2004State of the Art in Massive Model Visualization35 Existing Architectures iWalk Multi-threaded rendering pipeline (simplified)Multi-threaded rendering pipeline (simplified) –PLP used for visibility culling and prefetching

36. COMPUTER GRAPHIK – UNIVERSITÄT DES SAARLANDES Afrigraph 2004State of the Art in Massive Model Visualization36 Existing Architectures iWalk ResultsResults –Commodity PC (Pentium IV, 2.8 GHz), 512 MByte RAM –Rendering budget: 280.000 triangles per frame –Average frame rate: 9.3 fps (power plant) –Median accuracy: 99.2% pixels correct –Preprocessing time: 3 minutes

37. End of Part I Questions ?