Two Methods for Fast Ray-Cast Ambient Occlusion

1. Two Methods for Fast Ray-Cast Ambient Occlusion NVIDIA Research Samuli Laine and Tero Karras

2. Ambient Occlusion in Theory • Occlusion of incoming ambient light Light from these directions does not reach the surface Light from thesedirections reachesthe surface

3. What It Looks Like

4. Ambient Occlusion in Practice • Use limited range for occlusion • Otherwise everything would be occluded in indoor scenes • Also faster to calculate because of finite radius • Use falloff function to smooth the transition • Do not solve analytically • Theoretically doable, but would be ridiculously expensive • Solution: Monte Carlo sampling • 256 – 1024 samples is usually enough

5. Ray Casts for Ambient Occlusion • Cast a number of rays from the point to be shaded • Determine occlusion distance, apply falloff, sum • In reality use a fancy low-discrepancy sampling pattern

6. Using Shadow Rays • Shadow rays are usually faster than ordinary rays • Enough to detect any intersection, not necessarily closest one • Can do if we bake falloff function into the rays

7. Our Contributions • Two methods for calculating ambient occlusion faster • One for rasterization-based renderers • One for ray tracing -based renderers • No approximations as in screen-space methods • Up to ~10–15x speedup over state-of-the-art ray caster

8. Common Part: Occlusion Masks • Both methods keep occlusion status in bit masks • Occlusion of all rays from one point = one bit mask • Utilize precalculated look-up tables for fast update of these masks • Update occlusion of all rays at once! • Resembles LUTs of hemispherical rasterization paper by Kautz et al. 2004, but more versatile

9. Occlusion Look-up Table • Store binary plane vs. ray occlusion • Plane normal & distance from origin  3D table • Clever parameterization to avoid singularity at the center

10. Occlusion Look-up Table • Store binary plane vs. ray occlusion • Plane normal & distance from origin  3D table • Clever parameterization to avoid singularity at the center

11. Occlusion Look-up Table • Store binary plane vs. ray occlusion • Plane normal & distance from origin  3D table • Clever parameterization to avoid singularity at the center

12. Occlusion Look-up Table • Store binary plane vs. ray occlusion • Plane normal & distance from origin  3D table • Clever parameterization to avoid singularity at the center

13. Occlusion Look-up Table • Store binary plane vs. ray occlusion • Plane normal & distance from origin  3D table • Clever parameterization to avoid singularity at the center

14. Occlusion Look-up Table • Store binary plane vs. ray occlusion • Plane normal & distance from origin  3D table • Clever parameterization to avoid singularity at the center

15. Occlusion Look-up Table • Store binary plane vs. ray occlusion • Plane normal & distance from origin  3D table • Clever parameterization to avoid singularity at the center

16. Occlusion Look-up Table • Store binary plane vs. ray occlusion • Plane normal & distance from origin  3D table • Clever parameterization to avoid singularity at the center

17. Occlusion Look-up Table • Store binary plane vs. ray occlusion • Plane normal & distance from origin  3D table • Clever parameterization to avoid singularity at the center

18. Occlusion Look-up Table • Store binary plane vs. ray occlusion • Plane normal & distance from origin  3D table • Clever parameterization to avoid singularity at the center

19. Occlusion Look-up Table • Store binary plane vs. ray occlusion • Plane normal & distance from origin  3D table • Clever parameterization to avoid singularity at the center

20. Occlusion Look-up Table • Store binary plane vs. ray occlusion • Plane normal & distance from origin  3D table • Clever parameterization to avoid singularity at the center

21. Occlusion Look-up Table • Store binary plane vs. ray occlusion • Plane normal & distance from origin  3D table • Clever parameterization to avoid singularity at the center

22. Occlusion Look-up Table • Store binary plane vs. ray occlusion • Plane normal & distance from origin  3D table • Clever parameterization to avoid singularity at the center

23. Occlusion Look-up Table • Store binary plane vs. ray occlusion • Plane normal & distance from origin  3D table • Clever parameterization to avoid singularity at the center

24. Occlusion Look-up Table • Store binary plane vs. ray occlusion • Plane normal & distance from origin  3D table • Clever parameterization to avoid singularity at the center

25. Occlusion Look-up Table • Store binary plane vs. ray occlusion • Plane normal & distance from origin  3D table • Clever parameterization to avoid singularity at the center

26. Occlusion of a Triangle • Combination of four occlusion masks

27. Using Pre-Calculated Masks • 1283 look-up table is sufficiently accurate • To calculate occlusion of one triangle vs. all occlusion rays from a single receiver point • Transform triangle into coordinate space of receiver point • Calculate triangle plane and edge planes (trivial) • Fetch occlusion masks for all planes • Combine fetched masks together (binary AND) • Combine with current occlusion status (binary OR)

28. Method 1: Rasterization • First render depth and normal buffers • For each occluding triangle, rasterize bounds for region of influence • For each fragment inside the region of influence, calculate triangle vs. point occlusion and accumulate in frame buffer • Use logic op blending into occlusion bits in frame buffer • Bounding the region of influence similar to ambient occlusion volumes by McGuire 2010

29. Rasterization

30. Bounding the Region of Influence • Two choices for bounding volume • If triangle is large, construct hexagonal prism • If triangle is small, construct hemispherical billboard Large triangle Small triangle

31. Level of Detail Optimization • Far away occlusion doesn’t care about small details in the occluding surface • Therefore, we could use simplified geometry for larger occlusion distances • Nearby occlusion always has to be done using original geometry! • Split occlusion distance to various ranges, apply progressively more and more aggressive simplification as the distance grows • Can yield 30–140% speedup with tolerable error, depending on the scene

32. Level of Detail Example

33. Method 2: BVH Traversal • Assume that BVH for scene triangles is available • Find occluding triangles by traversing the BVH around the neighborhood of the receiver point • Similarities to packet traversal of Wald et al. 2007.

34. BVH Traversal Algorithm • Maintain cumulative occlusion mask for all AO rays during traversal • When making traversal decision, estimate the occlusion potential of current node’s children • Occlusion potential = Number of currently non-blocked rays that are blocked by the bounds of the node • If occlusion potential is zero, don’t go there • If both children are eligible, first process the one with greater potential • Try to build up occlusion quickly to be able to cull remaining nodes

35. Calculating Node Occlusion Mask • Tried out two kinds of node bounds • Boxes • Find silhouette edges, use same mask LUT as for triangles • Tight bounds, but expensive to compute (6 lookups) • Spheres • Determine direction and apex angle • Needs a separate 3D LUT for finding the occlusion mask • Conservative bounds, but cheap to compute (1–2 lookups) • Found out that spheres gave best overall performance

36. Results • Compared against our previous GPU ray caster[Aila and Laine 2009] • Added optimizations for shadow rays • Performance measured in Mrays/second • Used various ambient occlusion radii

37. Results

38. Results: Scalability

39. Results: Correctness

40. Future Work • Soft shadows from area light sources

41. Thank You • Questions