1. Ray Classification for Accelerated BVH Traversal
Jakub Hendrich, Adam Pospíšil,
Daniel Meister, Jiří Bittner

2. BVH Traversal
San Miguel
(7.8M tris) 2 2

3. Previous Work 5D ray-space [Arvo87, Müller17, …]
Shaft culling [Haines94, Bala99, Reshetov05, …]
BVHs & SAH [Goldsmith87, Wald07, Ganestam15, …]
Ray classification & Shaft culling: old ideas, but not used frequently because of not knowing how to combine them with modern BVHs 3 3

4. Main Idea 4 4

5. Accelerated Traversal
shaft = classify(ray)
if (shaft.candidateList):
stack.push(shaft.candidateList)
else:
stack.push(bvh.root)
// original traversal kernel
while (!stack.empty()):
node = stack.pop() … 5 5

6. Construction Outline input: scene, BVH for each voxel in scene
for each interval in directional cube map
construct frustum shaft geometry
create candidate list 6 6

7. Shaft Geometry 7 7

8. Candidate List Construction
Starting from BVH root, replace nodes with children until a termination criterion is met:
no intersection with the shaft
leaf node
low hit probability 8 8

9. Estimating Hit Probability
When to stop opening and replacing nodes with their children?
crucial decision:
too early: staying close to root, not saving many steps
too late: long candidate lists, possibly adding trav. steps
Considering:
node hit ratio (20 sample rays) vs. threshold
occlusion: don’t open nodes behind geometry 9 9

10. Memory Optimizations Raw memory complexity: 𝑂( 𝑠 3 𝑑 2 𝑘)
𝑠 and 𝑑: spatial & directional resolution, 𝑘 candidate list size
Candidate list sharing among adjacent shafts
Occupied Voxels – only voxels occupied by geometry
View Dependent – ray sampling to determine shafts with many rays 10 10

11. Evaluation Setup std. C++11, threads, no SIMD PBRT v3 [Pharr16]
binary & quaternary BVHs
10 scenes, 3 cameras each (1920x1080 px, 8 spp) 11 11

12. Evaluated Variants Complete Occupied Voxels View Dependent Measured:
build size
build time
traversals per ray
FirstHit / AnyHit / total render time 12 12

13. Rendering speedup
Conference (330k tris) 13 13

14. Rendering speedup
San Miguel (7.8M tris) 14 14

15. Rendering speedup
Happy Buddha (1.1M tris) 15 15

16. Speedup wrt. Memory Consumption (Conference)16 16

17. Conclusion Ray classification for accelerated BVH traversal
ray space subdivision + precomputed compact candidate lists
algorithm for optimized candidate list computation
Easy to plug into & use within existing framework
Up to 1.25x speedup in PBRT 17 17

18. Future Work Wide BVHs & optimized traversal kernels
Tailor for any-hit usage
Combine with path guiding [Müller17]
Ray reordering [Bikker12] 18 18

19. Thank You! → Ray Classification for Accelerated BVH Traversal 19 19

20. Memory Layout 20 20

21. CL Length Distribution
Conference (330k tris)
San Miguel (7.8M tris) 21 21

22. Candidate List Construction
stack.push(bvh.root)
while (!stack.empty()):
node = stack.pop()
if (shaft.overlaps(node)):
if (node.isInner()):
p = shaft.getHitProbability(node, sampleRays)
if (p >= minHitProbability): stack.push(all node.children)
else:
node = shaft.cullNodeSubtree(node)
if (node.valid()): shaft.candidateList.append(node)
else: // leaf
shaft.candidateList.append(node)
core part - great importance!!! Key for algorithm efficiency!!! 22 22