Visibility Culling III: Image-Space Occlusion

1. Visibility Culling III:Image-Space Occlusion David Luebke Computer Science Department University of Virginia <luebke@cs.virginia.edu>

2. Review: General Occlusion Culling • When cells and portals don’t work… • Trees in a forest • A crowded train station • Need general occlusion culling algorithms: • Aggregate occlusion • Dynamic scenes • Non-polygonal scenes

3. Review: Hierarchical Z-Buffer • A Z-pyramid represents depth buffer in hierarchical fashion • Depth at each pixel at a given level represents the max of the depth of the four corresponding pixels at next level • Rasterize triangles into buffer hierarchically with early termination

4. Review:Hierarchical Z-Buffer • Organize scene into an octree (a kind of spatial hierarchy) • Before rendering the polygons associated with a node in the octree, test if the node’s bbox is visible • Do an occlusion query to “test render” the faces of the bounding box against the depth-buffer • If query fails, bbox would not be visible and we can skip the node’s geometry

5. Review: Hierarchical Z-Buffer • HZB algorithm is most effective when Z-pyramid is “mostly complete” • Start by rendering nodes visible last frame directly into full-res Z-buffer • Build Z-pyramid from full-res Z-buffer • Traverse octree front-to-back, using Z-query to test nodes and render if needed • Note: also test nodes already rendered to see if they have become invisible

6. Review: Hierarchical Z-Buffer • HZB algorithm exploits: • Image-space coherence: • Polygons occluded in one pixel are probably occluded in nearby pixels • Object-space coherence: • Polygons near an occluded polygon are probably also occluded • Temporal coherence: • Polygons visible recently are probably still visible

7. Hierarchical Occlusion Maps • A more hardware-friendly general occlusion culling algorithm • Two major differences from HZB: • Separates occluders from occludees • Decouples occlusion test into an depth test and a overlap test

8. HierarchicalOcclusion Maps • Occluders versus occludees: Blue parts: occluders Red parts: occludees

9. View X Point Y Z Hierarchical Occlusion Maps • Depth versus overlap: Depth + Overlap = Occlusion

10. Hierarchical Occlusion Maps • Representation of projection for overlap test: occlusion map • Corresponds to a screen subdivision • Records average opacity per partition • Generate by rendering occluders • Record pixel opacities (i.e., coverage)

11. Occlusion Maps Rendered Image Occlusion Map

12. Occlusion Map Pyramid • Analyzing cumulative projection: • A hierarchical occlusion map (HOM) • Generate by recursive averaging (once per frame) • Records average opacities for blocks of multiple pixels, representing occlusion at multiple resolutions • Construction can be accelerated by texture hardware

13. Occlusion Map Pyramid 64 x 64 32 x 32 16 x 16

14. Occlusion Map Pyramid

15. Overlap Tests • Query: is projection of occludee inside cumulative projection of occluders? • Cumulative projection: occlusion pyramid • Occludee projection: expensive in general • Overestimate occludee with 3-D bounding box • Overestimate projection of 3-D bounding box with 2-D bounding rectangle in screen-space

16. Overlap Tests • Hierarchical structure enables some optimizations: • Predictive rejection • Terminate test when it must fail later • Conservative rejection • The transparency threshold • Aggressive Approximate Culling • Ignore objects barely visible through holes • The opacity threshold

17. 1 0 2 3 4 Aggressive Approximate Culling

18. Hierarchical Occlusion Maps • Not discussed here: • Depth test • Depth estimation buffer • Modified Z-buffer • Selecting occluders • For more details, see Hansong Zhang’s dissertation at UNC

19. HOM: Discussion • Provides a robust, general, hardware-friendly occlusion culling algorithm • Supports dynamic scenes • Supports non-polygonal geometry • Few hardware assumptions

20. HOM: Discussion • Not clear how relevant the actual algorithm is on modern hardware • Most interesting/important points: • Separation of occluders and occludees • Factoring occlusion into overlap + depth • Aggressive approximate culling • To think about: • How could we simplify the occluders to accelerate occlusion testing?

21. Current Hardware Support • Recent hardware supports Z-query operation • Allows systems to exploit: • Object-space coherence (bounding boxes) • Temporal coherence (last-rendered list) • Examples in OpenGL: • HP_OCCLUSION_QUERY • NV_OCCLUSION_QUERY • Go to NVIDIA occlusion presentation… • An aside: applies to cell-portal culling!

22. Visibility Culling: Discussion • When is visibility culling worthwhile? • When scene has high depth complexity • Examples: architectural walkthroughs, complex CAD assemblies, dense forest • Non-examples: terrain (usually), single highly-tessellated object (e.g., bunny, a radiositized room)

23. Visibility Culling:Discussion • How does visibility culling compare to: • Level-of-detail: • Reduces geometry processing • Helps transform-bound apps • Visibility culling: • Reduces geometry and pixel processing • Helps transform- and fill rate-bound apps • Texture / Image representations: • Reduces geometry and pixel processing • Incurs texture/image processing costs

24. Visibility Culling: Discussion • How does visibility culling interact with level of detail? • Fairly seamless integration; generally a win • One issue: visibility of simplified model may differ from original model; requires some care • LODs can speed up occluder selection and rendering

25. Visibility Culling: Discussion • How does visibility culling interact with texture and image-based representations? • Texture/image reps generally replace far-field geometry • Involves an implicit occlusion culling step • Reduces scene depth complexity, decreasing the utility of visibility culling • If near-field geometry still includes complex heavily-occlusive assemblies, still a win

26. Visibility Culling:Discussion • How much culling effort is appropriate? • Cells and portals: relatively cheap, with large potential speedups • Hierarchical occlusion maps: relatively costly, carefully weigh potential gains • Multiple processors allow much more aggressive culling calculation • Pipelining culling calculations, Performer-style, allows cull time = render time • Tradeoff: one frame increased latency

27. Summary • The basic, very powerful idea: • Rapidly compute a potentially visible set • Let hardware handle the rest • For many scenes, visibility culling is a simple way to get huge speedups • View-frustum culling always a must • For scenes with high depth complexity, occlusion culling can be a big win

28. Summary • Architectural models: visibility is practically a solved problem • Cells and portals work well • Cull-box portal culling: simple, fast • Line-stabbing: elegant, powerful

29. Summary • Occlusion culling of general models: still a largely open problem • Important issues: • Dynamic scenes • Aggregate occlusion effects • Image-based approaches seem most promising at this time

30. Summary • General occlusion culling algorithms: • Hierarchical Z-buffer: • A simple, truly elegant algorithm • But doesn’t seem amenable to hardware • Hierarchical occlusion maps: • Separates occluders from occludees, overlap from depth • Lends itself well to aggressive culling • Fairly high overhead, only worthwhile with high depth complexity • Recent hardware supports (asynchronous) occlusion query