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Clipless Dual-Space Bounds for Faster Stochastic Rasterization Samuli Laine Timo Aila Tero Karras Jaakko Lehtinen NVIDIA Research.

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Presentation on theme: "Clipless Dual-Space Bounds for Faster Stochastic Rasterization Samuli Laine Timo Aila Tero Karras Jaakko Lehtinen NVIDIA Research."— Presentation transcript:

1 Clipless Dual-Space Bounds for Faster Stochastic Rasterization Samuli Laine Timo Aila Tero Karras Jaakko Lehtinen NVIDIA Research

2

3

4

5 (x,y)

6 (x,y,u,v,t)

7 Motion Blur

8 t=0t=1

9 Motion Blur

10 t Accumulation Buffer [Haeberli ‘90]

11 t InterleaveUVT [Fatahalian ‘09]

12 t

13 t

14 t

15 4 samples/pixel (16 UVT triples)16 samples/pixel (64 UVT triples) Scene: Assassin’s Creed, courtesy of Ubisoft InterleaveUVT [Fatahalian ‘09]

16 Unique UVTs 4 samples/pixel, unique UVTs16 samples/pixel, unique UVTs Scene: Assassin’s Creed, courtesy of Ubisoft

17 5D Rasterization t=0t=1 t

18 5D Rasterization t=0t=1 t

19 5D Rasterization t=0t=1 t

20 5D Rasterization t

21 t=0t=1 t ?

22 5D Rasterization t=0t=1 t

23 5D Rasterization t=0t=1 t ?

24 5D Rasterization t=0t=1 t ?

25 Pixar Algorithm t=0t=1 t t=.5

26 Pixar Algorithm t=0t=1 t t=.5 XX

27 Pixar Algorithm t=0t=1 t t=.5 XX

28 Our Method t=0t=1 t

29 Our Method t=0t=1 t

30 Our Method t=0t=1 t XXX t range computed for pixel

31 Our Method  Determine pixels potentially covered by triangle  For each pixel  Compute time bounds t min,t max  Enumerate samples where t min ≤ t ≤ t max  For each such sample, perform 5D coverage test  Same for lens bounds u min,u max and v min,v max

32 Bound Computation  Lens bounds computed in screen space  Time bounds computed in dual space

33 Lens Bounds x y u=0u= –1u= +1 film lens focal plane u screen-space x is linear with u

34 Lens Bounds x y u= –1u= +1 screen-space x is linear with u

35 Lens Bounds x y u=au=b screen-space x is linear with u u a b

36 Time Bounds  World-space affine motion  Not affine in screen space, but affine in clip space  Perspective causes singularities in screen space  Operate in dual space

37 Clip Space and Dual Space x w t=0 t=1

38 Clip Space and Dual Space x w γ δ γ= –1 γ= +1

39 Clip Space and Dual Space x w γ δ δ { γ= –0.5

40 Clip Space and Dual Space x w γ δ

41 x w γ δ

42 x w γ δ

43 x w γ δ δ is linear in x and w δ = x – wγ x and w are linear in t δ is linear in t t=0 t=1 t=0 t=1

44 Clip Space and Dual Space x w γ δ t=0 t=1 t=0 t=1

45 Clip Space and Dual Space x w γ δ t=0 t=1 t=0 t=1

46 Clip Space and Dual Space x w γ δ t=0 t=1 t=0 t=1 t=a t

47 Clip Space and Dual Space x w γ δ t=0 t=1 t=0 t=1 t=a t a

48 Time Bounds  Compute separately for x and y  Intersect resulting spans  If intersection is empty, skip pixel

49 Recap  For each triangle  For each pixel  Compute t, u, v bounds  Cull samples outside bounds  Profit

50 Results  Measure sample test efficiency (STE)  Compare against methods that allow arbitrary sampling patterns # samples tested with full 5D test # samples hit STE =

51 Results Scene: Age of Conan PC MMO, courtesy of Funcom Bbox scan Pixar Our method static23.6 motion motion x defocus defocus x both both x STE in %, scene Conan

52 Results Scene: Assassin’s Creed, courtesy of Ubisoft Bbox scan Pixar Our method static23.2 motion motion x defocus defocus x both both x STE in %, scene Assassin

53 Results Bbox scan Pixar Our method static motion motion x defocus defocus x both both x STE in %, scene Cars

54 Conclusions  Each triangle processed once  Arbitrary sampling patterns  High STE  Future work  Combined motion + defocus case

55 Thank You


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