1. Hybrid Ray Tracing of Massive Models
Christian Lauterbach Dinesh Manocha UNC Chapel Hill
10/12/2009

2. Motivation: Ray Tracing
As a visualization method
General and robust solution for light transport
Transparency, refraction, reflection
Lighting: shadows, indirect lighting, …
Performance logarithmic with model complexity

3. This talk Memory issues for ray tracing of massive models
Hybrid rendering approaches

4. Problem: Memory Memory overhead Access pattern
Any object can be accessed by ray at any time
Need to store acceleration structure
Access pattern
Low locality
High number of cache misses
3 orders of magnitude ~ disk vs. memory speed

5. Massive models: ReduceM
Goals:
Compact representation for hierarchy and geometry
Low rendering performance overhead

6. Compact combined representation for hierarchy and
ReduceM idea
Triangle strips for ray tracing
Two-level hierarchy: …
High-level hierarchy (BVH, kd-tree, …)
Compact combined representation for hierarchy and
geometry

7. ReduceM Main features: Compact ReduceM representation
Fast traversal and intersection of strips
Construction of triangle strips optimized for ray tracing

8. Representation Based on triangle strips
Encode hierarchy on top as efficiently as possible 7 6 4 2 5 1 3

9. Representation Key idea: Can represent hierarchy via order of vertices7 6 4 2 5 1 3

10. Representation Overall: Overhead for hierarchy
Store vertices in order that defines hierarchy
Store local indices to define strip
 Sufficient both for triangle intersection and hierarchy traversal
Overhead for hierarchy
Local vertex indices
Some vertices stored twice (about 1.5-3%)

11. Traversal and intersection
Ported ray packet techniques to ReduceM
New possibilities:
Larger packet size for high-level hierarchy
Share edge results for triangle intersection
Intersection of single ray with multiple edges
Up to 90% higher ray tracing performance [Lauterbach et al. 07]

12. Construction
Many algorithms for triangle strip generation for GPU rasterization
Different criteria for ray tracing
Length  compression ratio
Spatial coherence  ray tracing performance

13. Construction overview
Graph
Adjacency
Graph + Hierarchy
Partitioning
Sequences
Ordering
[1,3,2,4,6] [6,4,7,9,8]
Triangle strips
Strip output

14. Construction algorithm
Our approach:
Strip generation using surface area heuristic information
Partitioning:
generate ideal hierarchy
Ordering:
Use hierarchy as reference to evaluate possible triangle sequences
Iteratively try to combine sequences
Partitioning
Ordering
Graph
Graph + Hierarchy

15. Results Tested on set of massive models
All benchmarks are fully in-core [Lauterbach et al. 07, Lauterbach et al. 08]
St. Matthew (372M) Powerplant (12.7M) Double Eagle (82M) Boeing 777 (360M)
Build time: 1h 36m Build time: 5m Build time: 33m Build time: 1h 50m

16. Key results Memory footprint Rendering performance
Reduced by up to 80% compared to standard kd-tree or BVH
Rendering performance
Optimized strips: up to 58% higher compared to rasterization strips
Overall performance comparable to kd-tree
Higher for some large models
Single ray performance up to 90% higher

17. Results
Logarithmic performance maintained

18. Comparison
Most similar to compressed BVH approaches [Mahovsky 05, Cline et al. 06]
Higher compression of hierarchy
But:
Does not change geometry footprint
Rendering times 40-60% with best compression
Worst for single rays

19. State-of-the-art Basic visualization: fast enough
E.g. visibility, shading, hard shadows
One or several rays / pixel
Decent lighting: barely interactive (~1-5 fps)
E.g. soft shadows, simple ambient occlusion
<= 16 rays / pixel
High-quality: non-interactive (<< 1fps)
E.g. indirect lighting, “good” lighting, anti-aliasing
Tens to hundreds of rays / pixel

20. GPU Rendering Fast visibility High quality
Levels-of-detail, mesh layouts, compression, out-of-core rendering, …
High quality
Cheap antialiasing, shading, …

21. Hybrid rendering One solution until hardware nirvana
Use GPU rendering where it makes sense
Use ray tracing otherwise
Try to reduce ray workload

22. Future architectures Graphics pipelines are getting more flexible GPUs
DirectX 11 compute shaders
More configurable stages
Intel Larrabee
Software pipeline

23. Hybrid ray tracing Already widely used in GPU ray tracing
Rasterize visibility, add reflection, refraction and shadows with ray tracing [Reiter-Horn et al. 07]
Counter-argument
When ray tracing 100+ rays/pixel, why care about one more for visibility?

24. Selective ray tracing Motivation:
GPU ray tracing feasible, but still orders of magnitude slower than rasterization
Want to use ray tracing for ‘interesting’ effects
Accurate hard/soft shadows
Ambient occlusion
Indirect lighting, …
But: Hardware not yet fast enough to trace enough rays

25. GPU algorithms Problems: Hard-to-control errors Not robust
Hard shadows Soft shadows Ambient occlusion
(from [Lloyd 08])
(from [Laine et al. 05])
(from [Bavoil et al. 08])
Problems:
Hard-to-control errors
Not robust
Not scalable

26. Selective ray tracing Idea: Example:
Use ray tracing only to correct localized errors in GPU rendering algorithms
Example:
Shadow mapping
Artifacts marked
Final result

27. Ray generation and compaction
Overview
Hierarchy Geometry
Frame buffer(s) unshaded
FB with pixels marked
Open ray buffer
Traced ray results
FB shaded with ray results
Accuracy detection
Ray generation and compaction
Ray tracing
Shading
Main applications:
Hard shadows
Soft shadows
Ambient occlusion

28. Massive model rendering
To use ray tracing, need to store geometry and hierarchy on GPU
Problem: even less memory than CPUs
ReduceM for GPU ray tracing
With minor modifications can also use directly use strip representation in GPU rendering

29. Shadow mapping Shadow mapping algorithm Result
Renders scene from light into depth map
During rendering, reproject each pixel to light’s view and test whether occluded using map
Main source of error is mismatched sampling rate of shadow map
Result
Jagged shadow boundaries
Missed shadows

30. Edge detection + conservative rendering
Shadow mapping
Normal shadow mapping
Edge detection
Edge detection + conservative rendering

31. Soft shadows Also identify and ray trace penumbra regions
Our solution:
Project area light onto each shadow map pixel
Mark all pixels in that projected region

32. Ambient occlusion Ambient occlusion Screen-space ambient occlusion
Reconstruct local geometry from depth buffer neighborhood: R x R

33. Ambient occlusion Problems:
Information in depth buffer insufficient
Result: missing shadows or view-dependent changes in occlusion a) b) x x c)
Actual geometry d) x

34. Ambient occlusion Error detection: Can partially ray trace
Find discontinuities in neighborhood
If found, revert to ray tracing
Can partially ray trace
E.g. discontinuity in one quadrant? Still use screen-space solution for others

35. Shadow results
Rendering of complex models with accurate shadows on current GPU
E.g. Powerplant
Performance:
~3-5 times faster than full ray tracing
~2-3 times slower than original algorithm
Accuracy
Virtually identical to ray traced solution

36. Shadow results
Hard shadows, real-time capture
Soft shadows, ~2 fps

37. Challenges Integrate with GPU rendering Ray organization
Levels-of-detail
Shared representations for rendering
Ray organization

38. Future work GPU rendering as dense visibility sampling
Can use for more general purposes?
Hybrid rendering representations
How to modify future rendering pipelines?

39. Next up: 30 min. break Then: Sung-Eui Yoon: Data Management