1. Afrigraph 2004 Massive model visualization 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 “Hierarchical Z-Buffer visibility” (Greene Sig93)“Hierarchical Z-Buffer visibility” (Greene Sig93) Visibility Culling Hierarchical Z-Buffer

10. COMPUTER GRAPHIK – UNIVERSITÄT DES SAARLANDES Afrigraph 2004State of the Art in Massive Model Visualization10 Organize scene into an octree (a kind of spatial hierarchy)Organize scene into an octree (a kind of spatial hierarchy) Visibility Culling Hierarchical Z-Buffer

11. COMPUTER GRAPHIK – UNIVERSITÄT DES SAARLANDES Afrigraph 2004State of the Art in Massive Model Visualization11 Visibility Culling Hierarchical Z-Buffer HZB algorithm :HZB algorithm : Make use of frame-to-frame coherence: Make use of frame-to-frame coherence: – at start of each frame render the nodes that were visible in previous frame – at start of each frame render the nodes that were visible in previous frame – read the z-buffer and construct the z-pyramid – read the z-buffer and construct the z-pyramid – traverse the octree using the z- pyramid for node occlusion test – traverse the octree using the z- pyramid for node occlusion test

12. COMPUTER GRAPHIK – UNIVERSITÄT DES SAARLANDES Afrigraph 2004State of the Art in Massive Model Visualization12 Visibility Culling Hierarchical Z-Buffer cost of scan-converting the faces of the octree cubes ——Z-pyramidcost of scan-converting the faces of the octree cubes ——Z-pyramid If the nearest Z value of the polygon is farther away than this sample in the Z pyramid, we know immediately that the polygon is hidden.

13. COMPUTER GRAPHIK – UNIVERSITÄT DES SAARLANDES Afrigraph 2004State of the Art in Massive Model Visualization13 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 –Exploits Temporal coherence: Frame to frame 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

14. COMPUTER GRAPHIK – UNIVERSITÄT DES SAARLANDES Afrigraph 2004State of the Art in Massive Model Visualization14 Hierarchical Occlusion Map (HOM) [Zhang 97]Hierarchical Occlusion Map (HOM) [Zhang 97] Visibility Culling Hierarchical Occlusion Maps View Point Z X Y Depth + Overlap = Occlusion

15. COMPUTER GRAPHIK – UNIVERSITÄT DES SAARLANDES Afrigraph 2004State of the Art in Massive Model Visualization15 Blue parts: occluders Red parts: occludees Visibility Culling Hierarchical Occlusion Maps

16. COMPUTER GRAPHIK – UNIVERSITÄT DES SAARLANDES Afrigraph 2004State of the Art in Massive Model Visualization16 Visibility Culling Hierarchical Occlusion Maps Hierarchical Occlusion Map (HOM) [Zhang 97]Hierarchical Occlusion Map (HOM) [Zhang 97] –Pixels record opacity of screen space regions –Algorithm: 1.Select occluders: E.g. visible objects from previous frame 2.Render occluders and estimate depth Pure white pixels on black background Pure white pixels on black background 3. Building HOM: 4. HOM culling: Traverse the BVHs, do view frustem cull- Traverse the BVHs, do view frustem cull- -ing, then -ing, then (1)using depth estimation buffer to do (1)using depth estimation buffer to do depth comparison, depth comparison, (2)overlap test with HOM (2)overlap test with HOM

17. COMPUTER GRAPHIK – UNIVERSITÄT DES SAARLANDES Afrigraph 2004State of the Art in Massive Model Visualization17 Render occluders and estimate depth:Render occluders and estimate depth: single z-plane: Depth estimation buffer: software buffer conservatively Construction: traverse visible occluders in last frame Visibility Culling Hierarchical Occlusion Maps Constructed at each frame Image space bounding rectangle and farthest depth of bounding volume Update depth of pixel farther than the former depth

18. COMPUTER GRAPHIK – UNIVERSITÄT DES SAARLANDES Afrigraph 2004State of the Art in Massive Model Visualization18 Building HOMBuilding HOM Visibility Culling Hierarchical Occlusion Maps At the finest level it’s just a bit map with – 1 where it is transparent and – 0 where it is opaque (occluded) Higher levels are half the size in each dimension and store gray-scale values Records average opacities for blocks of pixels Represents occlusion at multiple resolutions

19. COMPUTER GRAPHIK – UNIVERSITÄT DES SAARLANDES Afrigraph 2004State of the Art in Massive Model Visualization19 Visibility Culling Hierarchical Occlusion Maps HOM culling: for each occluderHOM culling: for each occluder 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

20. COMPUTER GRAPHIK – UNIVERSITÄT DES SAARLANDES Afrigraph 2004State of the Art in Massive Model Visualization20 Visibility Culling Hierarchical Occlusion Maps City model with 312,524 polygons. Average speed-up obtained by our visibility culling algorithm is about five times.

21. COMPUTER GRAPHIK – UNIVERSITÄT DES SAARLANDES Afrigraph 2004State of the Art in Massive Model Visualization21 Visibility Culling Hierarchical Occlusion Maps Compared to HZB:Compared to HZB: HZB: HZB’culling is less conservative. HZB is easier to use temporal coherence for occluder selection because nearest Z values for objects are available in the Z- buffer. Updating the active occluder list is more difficult in HOM since it only have estimated farthest Z values. HOM: There is no need for a Z-buffer The construction of HOM has readily-available hardware support HOM supports conservative early termination in the hierarchical test by using a transparency threshold