Ray-tracing has revolutionized computer graphics by simulating the physical behavior of light, but its computational demands would make it utterly impractical without acceleration structures. A single modern scene can contain hundreds of millions of triangles - consider that Unreal Engine 5's Nanite system routinely handles over a billion polygons. In a 4K resolution frame (8.3 million pixels), even basic one-ray-per-pixel rendering would require 8.3 trillion ray-triangle intersection tests if using brute-force methods. At an optimistic rate of one test per nanosecond, this would demand over two hours to render just one frame, making real-time performance completely impossible. This is where acceleration structures become essential, reducing the algorithmic complexity from O(N) to O(log N) through intelligent spatial organization of scene geometry. The presentation will explore how bounding volume hierarchies (BVHs) have emerged as the gold-standard acceleration structure, particularly through their implementation in modern GPU ray-tracing hardware. We'll examine the critical Surface Area Heuristic (SAH) that guides optimal BVH construction, balancing the costs of traversal versus intersection testing. The mathematical formulation of SAH shows how it minimizes expected computational cost by weighting probabilities based on relative surface areas. Real-world implementations like NVIDIA's RTX platform demonstrate this theory in action, where dedicated hardware can traverse BVH structures at astonishing speeds of tens of billions of ray-triangle tests per second. However, challenges remain - dynamic scenes force difficult tradeoffs between rebuild quality and speed, while memory consumption grows prohibitively large for complex scenes, with BVH structures typically requiring about 40 bytes of storage per triangle. Looking to the future, the presentation will analyze emerging neural acceleration techniques that promise to complement or potentially replace traditional structures. Recent research from SIGGRAPH 2023 demonstrates how machine learning can predict optimal traversal paths or even bypass explicit geometry representation entirely through neural signed distance functions. However, these approaches currently face limitations in training time and generalization capability. The presentation will conclude with an examination of next-generation hardware solutions, from photonic computing to processing-in-memory architectures, that may overcome current bottlenecks and unlock new possibilities in real-time ray-tracing.

1. Acceleration Structures for Ray-Tracing
From BVHs to Neural Acceleration
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2. Outline
1 Introduction
2 Core Techniques
3 Modern Implementations
4 Challenges
5 Future Directions
6 Conclusion
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3. Why Acceleration Structures?
The Ray-Tracing Challenge:
Modern scenes: 100M+ triangles (e.g., UE5 Nanite)
Brute-force: O(N) complexity is impractical
Goal: Reduce to O(log N) with spatial partitioning
Real-World Example
Pixar’s Renderman uses BVHs for film rendering (24 rays/pixel at 4K =
199M rays/frame!)
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4. Key Questions
How do BVHs compare to kD-Trees in practice?
Can Vulkan RT compete with NVIDIA OptiX?
Will neural acceleration replace traditional structures?
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5. Taxonomy of Acceleration Structures
Acceleration Structures
BVH kD-Tree Grids
SAH Construction Surface Splitting Voxel Traversal
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6. BVH Construction (SAH)
Surface Area Heuristic:
C(A → B) = Ct +
SA(A)
SA(P)
NA +
SA(B)
SA(P)
NB
Ct: Traversal cost (typically 1-3 cycles)
SA: Surface area of node
Optimal split minimizes total cost
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7. BVH vs kD-Tree
BVH Pros:
Faster rebuild (dynamic scenes)
GPU-friendly (HLBVH)
RTX hardware acceleration
kD-Tree Pros:
Fewer intersection tests
Better for static scenes
Used in Mental Ray
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8. Vulkan Ray Tracing
Vulkan RT Features:
Cross-platform alternative to OptiX/DXR
Acceleration structures:
VkAccelerationStructureKHR
Compaction for memory efficiency
Challenges:
No SAH construction in hardware
Less mature than RTX
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9. NVIDIA RTX Pipeline
AS Builder in driver (OptiX 7.0+)
Hardware-accelerated traversal
AI denoising (DLSS)
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10. Real-World Performance
Structure Build Time (ms) Ray Test (ns)
BVH (SAH) 12.5 3.2
SBVH 18.1 2.8
kD-Tree 142.0 1.9
Neural SDF 210.0 5.4
Table: Performance on RTX 4090 (1M triangles)
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11. Dynamic Scene Problems
Rebuild Costs:
Full BVH rebuild: 10-100ms for complex scenes
Solutions:
Refitting (update bounds only)
Temporal coherence (RTXDI)
Parallel construction (HLBVH)
Case Study
UE5 Nanite uses software rasterization for dynamic objects to avoid BVH
rebuilds
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12. Memory Limitations
Memory Footprint:
BVH Size ≈ 40 bytes/triangle
100M triangles = 4GB VRAM
Compression techniques:
Quantization (8-bit indices)
Implicit hierarchies
Neural compression (Google, 2022)
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13. Neural Acceleration
Emerging Approaches:
Neural SDFs (NeRF-like)
Learned BVH cut points
Hybrid raster/ray-tracing
Limitations
Training time (hours/days)
Generalization problems
Hardware support lacking
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14. Hardware Trends
Next-Gen Acceleration:
Photonic computing (light-based traversal)
In-memory processing (Samsung HBM-PIM)
Dedicated neural RT cores (NVIDIA post-Hopper)
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15. Key Takeaways
BVHs dominate real-time (RTX/Vulkan)
SAH remains gold standard for construction
Neural methods promising but not production-ready
Memory and rebuild costs still critical
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16. Q&A
Questions?
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17. Advanced BVH Optimizations
Recent Papers:
ReBLUR (NVIDIA, 2023): Denoising + BVH refinement
Neural Radiance Caching (SIGGRAPH 2022)
Compressed BVH (Google, 2021)
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18. Ray Tracing APIs Comparison
API HW AS Build Denoising Cross-Platform
Vulkan RT y n y
OptiX y y n
DXR y y n
Metal RT y n n
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19. Further Reading/Resources
https://xbdev.net/vulkan/
https://vulkanlab.xbdev.net
Ray-Tracing with Vulkan - Owners’ Workshop Manual (Paperback)
Vulkan Graphics API: in 20 Minutes (Coffee Break Series)
Real-Time Ray-Tracing with Vulkan for the Impatient (Kenwright)
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