MSBVH: An Efficient Acceleration Data Structure for Ray Traced Motion Blur

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1 MSBVH: An Efficient Acceleration Data Structure for Ray Traced Motion Blur Leonhard Grünschloß Martin Stich Sehera Nawaz Alexander Keller August 6, 2011

2 Principles of Accelerated Ray Tracing Hierarchical culling object list partitioning BVH

3 Principles of Accelerated Ray Tracing Hierarchical culling object list partitioning BVH

4 Principles of Accelerated Ray Tracing Hierarchical culling object list partitioning BVH

5 Principles of Accelerated Ray Tracing Hierarchical culling object list partitioning BVH

6 Principles of Accelerated Ray Tracing Hierarchical culling object list partitioning BVH

7 Principles of Accelerated Ray Tracing Hierarchical culling object list partitioning BVH

8 Principles of Accelerated Ray Tracing Hierarchical culling object list partitioning BVH

9 Principles of Accelerated Ray Tracing Hierarchical culling object list partitioning BVH

10 Principles of Accelerated Ray Tracing Hierarchical culling object list partitioning BVH

11 Principles of Accelerated Ray Tracing Hierarchical culling object list partitioning BVH bounded memory, but overlapping bounding volumes

12 Principles of Accelerated Ray Tracing Hierarchical culling object list partitioning BVH bounded memory, but overlapping bounding volumes spatial partitioning kd-tree

13 Principles of Accelerated Ray Tracing Hierarchical culling object list partitioning BVH bounded memory, but overlapping bounding volumes spatial partitioning kd-tree

14 Principles of Accelerated Ray Tracing Hierarchical culling object list partitioning BVH bounded memory, but overlapping bounding volumes spatial partitioning kd-tree

15 Principles of Accelerated Ray Tracing Hierarchical culling object list partitioning BVH bounded memory, but overlapping bounding volumes spatial partitioning kd-tree

16 Principles of Accelerated Ray Tracing Hierarchical culling object list partitioning BVH bounded memory, but overlapping bounding volumes spatial partitioning kd-tree

17 Principles of Accelerated Ray Tracing Hierarchical culling object list partitioning BVH bounded memory, but overlapping bounding volumes spatial partitioning kd-tree

18 Principles of Accelerated Ray Tracing Hierarchical culling object list partitioning BVH bounded memory, but overlapping bounding volumes spatial partitioning kd-tree

19 Principles of Accelerated Ray Tracing Hierarchical culling object list partitioning BVH bounded memory, but overlapping bounding volumes spatial partitioning kd-tree

20 Principles of Accelerated Ray Tracing Hierarchical culling object list partitioning BVH bounded memory, but overlapping bounding volumes spatial partitioning kd-tree

21 Principles of Accelerated Ray Tracing Hierarchical culling object list partitioning BVH bounded memory, but overlapping bounding volumes spatial partitioning kd-tree

22 Principles of Accelerated Ray Tracing Hierarchical culling object list partitioning BVH bounded memory, but overlapping bounding volumes spatial partitioning kd-tree

23 Principles of Accelerated Ray Tracing Hierarchical culling object list partitioning BVH bounded memory, but overlapping bounding volumes spatial partitioning kd-tree

24 Principles of Accelerated Ray Tracing Hierarchical culling object list partitioning BVH bounded memory, but overlapping bounding volumes spatial partitioning kd-tree nodes do not overlap, but reference duplication

25 SBVH Best of both worlds object list partitioning whenever overlap is small spatial partitioning otherwise

26 SBVH Best of both worlds object list partitioning whenever overlap is small spatial partitioning otherwise use spatial splits to build BVH with reference duplication

27 SBVH Best of both worlds object list partitioning whenever overlap is small spatial partitioning otherwise use spatial splits to build BVH with reference duplication

28 SBVH Best of both worlds object list partitioning whenever overlap is small spatial partitioning otherwise use spatial splits to build BVH with reference duplication

29 SBVH Best of both worlds object list partitioning whenever overlap is small spatial partitioning otherwise use spatial splits to build BVH with reference duplication

30 SBVH Best of both worlds object list partitioning whenever overlap is small spatial partitioning otherwise use spatial splits to build BVH with reference duplication

31 SBVH Best of both worlds object list partitioning whenever overlap is small spatial partitioning otherwise use spatial splits to build BVH with reference duplication

32 SBVH Best of both worlds object list partitioning whenever overlap is small spatial partitioning otherwise use spatial splits to build BVH with reference duplication

33 SBVH Best of both worlds object list partitioning whenever overlap is small spatial partitioning otherwise use spatial splits to build BVH with reference duplication

34 SBVH Best of both worlds object list partitioning whenever overlap is small spatial partitioning otherwise use spatial splits to build BVH with reference duplication How to support motion blur?

35 Multiple BVHs Sharing Identical Topology Convex combination of bounding boxes yields conservative BVH

36 Multiple BVHs Sharing Identical Topology Convex combination of bounding boxes yields conservative BVH

37 Multiple BVHs Sharing Identical Topology Example: linear interpolation at leaf level

38 Multiple BVHs Sharing Identical Topology Example: linear interpolation at leaf level

39 Multiple BVHs Sharing Identical Topology Example: linear interpolation at leaf level t=0 t=1

40 Multiple BVHs Sharing Identical Topology Example: linear interpolation at leaf level t=0 t=1

41 Multiple BVHs Sharing Identical Topology Example: linear interpolation at leaf level t=0 t=1

42 Multiple BVHs Sharing Identical Topology t=0 Example: linear interpolation at leaf level t=1 t=0.5

43 Multiple BVHs Sharing Identical Topology t=0 Example: linear interpolation at leaf level t=1 t=0.5 acceptable memory overhead

44 Multiple BVHs Sharing Identical Topology t=0 Example: linear interpolation at leaf level t=1 t=0.5 acceptable memory overhead allows for very tight bounding boxes for every ray time t

45 Interpolation and Spatial Splits Can a kd-tree be interpolated?

46 Interpolation and Spatial Splits Can a kd-tree be interpolated? objects can move across split planes thus node references change!

47 Interpolation and Spatial Splits Can a kd-tree be interpolated? objects can move across split planes thus node references change! hierarchy over convex hulls is inefficient

48 Interpolation and Spatial Splits Can a kd-tree be interpolated? objects can move across split planes thus node references change! hierarchy over convex hulls is inefficient splitting along time-axis requires lots of memory

49 Our Contribution Extend the SBVH to handle motion blur (MSBVH) by computing multiple bounding volumes per node using classic bounding volume interpolation traversal

50 Our Contribution Extend the SBVH to handle motion blur (MSBVH) by computing multiple bounding volumes per node using classic bounding volume interpolation traversal which includes spatial splits

51 Our Contribution Extend the SBVH to handle motion blur (MSBVH) by computing multiple bounding volumes per node using classic bounding volume interpolation traversal which includes spatial splits memory-efficient (MSBVH) reduced bounding volume overlap (MSBVH) Note: we assume the hierarchy is rebuilt per frame

52 Algorithm t=0 t=1

53 Algorithm t=0 t=0.5 t=1 1. Build the SBVH for t = 0.5 to determine topology

54 Algorithm t=0 t=0.5 t=1 1. Build the SBVH for t = 0.5 to determine topology 2. Compute partial primitives in leaf nodes

55 Algorithm t=0 t=0.5 t=1 1. Build the SBVH for t = 0.5 to determine topology 2. Compute partial primitives in leaf nodes

56 Algorithm t=0 t=0.5 t=1 1. Build the SBVH for t = 0.5 to determine topology 2. Compute partial primitives in leaf nodes 3. Compute corresponding bounds for t = 0 and t = 1

57 Algorithm t=0 t=0.5 t=1 1. Build the SBVH for t = 0.5 to determine topology 2. Compute partial primitives in leaf nodes 3. Compute corresponding bounds for t = 0 and t = 1

58 Algorithm t=0 t=1 1. Build the SBVH for t = 0.5 to determine topology 2. Compute partial primitives in leaf nodes 3. Compute corresponding bounds for t = 0 and t = 1 4. Propagate bounds to the parent nodes

59 Algorithm t=0 t=1 1. Build the SBVH for t = 0.5 to determine topology 2. Compute partial primitives in leaf nodes 3. Compute corresponding bounds for t = 0 and t = 1 4. Propagate bounds to the parent nodes 5. Interpolate these bounds during traversal

60 Triangles and AABB-Hierarchies under Linear Motion t=0 t=0.5 t=1 1. Use Sutherland-Hodgman to clip against leaf AABB 2. Results in barycentric coordinates of polygon vertices

61 Triangles and AABB-Hierarchies under Linear Motion t=0 t=0.5 t=1 1. Use Sutherland-Hodgman to clip against leaf AABB 2. Results in barycentric coordinates of polygon vertices 3. Compute transformed polygon for t = 0 and t = 1

62 Triangles and AABB-Hierarchies under Linear Motion t=0 t=0.5 t=1 1. Use Sutherland-Hodgman to clip against leaf AABB 2. Results in barycentric coordinates of polygon vertices 3. Compute transformed polygon for t = 0 and t = 1 4. Bound the transformed polygon

63 Triangles and AABB-Hierarchies under Linear Motion t=0 t=0.5 t=1 1. Use Sutherland-Hodgman to clip against leaf AABB 2. Results in barycentric coordinates of polygon vertices 3. Compute transformed polygon for t = 0 and t = 1 4. Bound the transformed polygon 5. No extra storage necessary

64 Clipping Displaced Subdivision Surfaces

65 Clipping Displaced Subdivision Surfaces 1. Subdivide along surface parametrization 2. Bound individual elements, e.g. using interval arithmetic

66 Clipping Displaced Subdivision Surfaces 1. Subdivide along surface parametrization 2. Bound individual elements, e.g. using interval arithmetic 3. Clip resulting bounding boxes 4. The union conservatively bounds the clipped primitive

67 Extensions two-level hierarchy: animated instances

68 Extensions two-level hierarchy: animated instances interpolate transformation matrix elements to force linear motion A(t)

69 Extensions two-level hierarchy: animated instances interpolate transformation matrix elements to force linear motion A(1/3) A(2/3) A(0) A(1)

70 Extensions two-level hierarchy: animated instances interpolate transformation matrix elements to force linear motion multiple motion segments

71 Extensions two-level hierarchy: animated instances interpolate transformation matrix elements to force linear motion multiple motion segments restricted to powers of two for propagation up the hierarchy

72 Extensions two-level hierarchy: animated instances interpolate transformation matrix elements to force linear motion multiple motion segments restricted to powers of two for propagation up the hierarchy

73 Extensions two-level hierarchy: animated instances interpolate transformation matrix elements to force linear motion multiple motion segments restricted to powers of two for propagation up the hierarchy higher-order interpolation

74 Extensions two-level hierarchy: animated instances interpolate transformation matrix elements to force linear motion multiple motion segments restricted to powers of two for propagation up the hierarchy higher-order interpolation refitting over multiple frames

75 Results BVH traversal with linear interpolation I reduced SAH cost I significantly less intersection tests Video

76 Results BVH traversal with linear interpolation I reduced SAH cost I significantly less intersection tests I often less traversal steps I about 20% rendering speed-up for many scenes

77 Summary In practice, works well for single frames helps well whenever SBVH helps increased build times (between BVH and kd-tree) prototype implemention in OptiX

78 Summary In practice, works well for single frames helps well whenever SBVH helps increased build times (between BVH and kd-tree) prototype implemention in OptiX spatial splits only avoid overlap for t = 0.5 topology determined for t = 0.5 problematic for incoherent motion

79 Weta Digital is hiring!

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