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Clipless Dual-Space Bounds for Faster Stochastic Rasterization

Clipless Dual-Space Bounds for Faster Stochastic Rasterization. Samuli Laine Timo Aila Tero Karras Jaakko Lehtinen NVIDIA Research. ( x,y ). ( x,y, u,v,t ). Motion Blur. Motion Blur. t= 0. t= 1. Motion Blur. Accumulation Buffer [Haeberli ‘90]. t. InterleaveUVT [Fatahalian ‘09].

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Clipless Dual-Space Bounds for Faster Stochastic Rasterization

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

  2. (x,y)

  3. (x,y,u,v,t)

  4. Motion Blur

  5. Motion Blur t=0 t=1

  6. Motion Blur

  7. Accumulation Buffer [Haeberli ‘90] t

  8. InterleaveUVT [Fatahalian ‘09] t

  9. InterleaveUVT [Fatahalian ‘09] t

  10. InterleaveUVT [Fatahalian ‘09] t

  11. InterleaveUVT [Fatahalian ‘09] t

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

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

  14. 5D Rasterization t t=0 t=1

  15. 5D Rasterization t t=0 t=1

  16. 5D Rasterization t t=0 t=1

  17. 5D Rasterization t

  18. 5D Rasterization t ? t=0 t=1

  19. 5D Rasterization t t=0 t=1

  20. 5D Rasterization ? t t=0 t=1

  21. 5D Rasterization ? t t=0 t=1

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

  23. Pixar Algorithm t X X t=0 t=.5 t=1

  24. Pixar Algorithm t X X t=0 t=.5 t=1

  25. Our Method t t=0 t=1

  26. Our Method t t=0 t=1

  27. Our Method t X X X t range computed for pixel t=0 t=1

  28. Our Method • Determine pixels potentially covered by triangle • For each pixel • Compute time bounds tmin ,tmax • Enumerate samples where tmin ≤ t ≤tmax • For each such sample, perform 5D coverage test • Same for lens bounds umin ,umaxandvmin ,vmax

  29. Bound Computation • Lens bounds computed in screen space • Time bounds computed in dual space

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

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

  32. Lens Bounds y a b u u=a u=b x screen-space x is linear with u

  33. 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

  34. Clip Space and Dual Space w t=1 t=0 x

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

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

  37. Clip Space and Dual Space w δ x γ

  38. Clip Space and Dual Space w δ x γ

  39. Clip Space and Dual Space w δ x γ

  40. Clip Space and Dual Space w δ t=1 t=0 t=1 t=0 x γ δ = x – wγ δis linear in x and w x and w are linear in t δis linear in t

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

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

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

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

  45. Time Bounds • Compute separately for x and y • Intersect resulting spans • If intersection is empty, skip pixel

  46. Recap • For each triangle • For each pixel • Compute t, u, v bounds • Cull samples outside bounds • Profit

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

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