Introduction to Bidirectional Path Tracing (BDPT) & Implementation using OpenCL (CEDEC 2015)

1. INTRODUCTION TO BIDIRECTIONAL PATH TRACING & ITS IMPLEMENTATION USING OPENCL 双方向パストレーシング(BDPT)の基礎からOPENCLによる実装まで TAKAHIRO HARADA (AMD) SHO IKEDA (RICOH) SYOYO FUJITA (LTE)

2. 2 | OPENCL BDPT | AUGUST 28, 2015 | HARADA, IKEDA, FUJITA OVERVIEW OF THIS TALK y  IntroducJon (Harada, 20min -‐ 25min) ‒ Start with path tracing review ‒ Transforming path tracing to bidirecJonal path tracing y  BidirecJonal Path Tracing (Ikeda, 20min -‐ 25min) ‒ Classic BDPT ‒ Instant BDPT ‒ Lvc BDPT y  IntegraJon of BidirecJonal Path Tracing to the Engine (Harada, 5min) y  Examples (Harada, 5min)

3. 3 | OPENCL BDPT | AUGUST 28, 2015 | HARADA, IKEDA, FUJITA PATH TRACING (MONTE CARLO RAY TRACING) y  != Raster graphics (game) y  BeauJful rendering y  Global illuminaJon

4. 4 | OPENCL BDPT | AUGUST 28, 2015 | HARADA, IKEDA, FUJITA PATH TRACING (MONTE CARLO RAY TRACING) y  ComputaJonally expensive ‒ Take Jme to converge ‒ Noisy at ﬁrst, gebng cleaner later y  Make 1 step rendering faster ‒ Total Jme decreases ‒ OpJmizaJon (using more CPU cores, GPUs) ‒  OpenCL (my talk@CEDEC2014) y  Make 1 step less noisy ‒ Need less steps ‒ Beeer sampling techniques ‒ Clever algorithm ‒  BDPT (this talk)

5. PATH TRACING IMPORTANCE SAMPLING MULTIPLE IMPORTANCE SAMPLING

6. 6 | OPENCL BDPT | AUGUST 28, 2015 | HARADA, IKEDA, FUJITA FIRST PATH TRACING

7. 7 | OPENCL BDPT | AUGUST 28, 2015 | HARADA, IKEDA, FUJITA PATH TRACING IMPLEMENTATION #1 for(int i=0; i<nx; i++) for(int j=0; j<ny; j++)! {! Ray ray = genPrimaryRay( i, j );! ! float4 coeff = 1.f;! float4& output = pixel[i,j];! for(int depth=0; depth<maxDepth; depth++)! {! Hit hit = intersect( ray );! if( !hit.hasHit() ) break;! ! if( hit.isEmissive() )! {// Implicit Connection! output += coeff * getEmission( hit );! break;! }! ! ray, f, pdf = randomSample( hit );! coeff *= f * dot( hit.m_n, ray.m_dir ) / pdf;! }! }!

8. 8 | OPENCL BDPT | AUGUST 28, 2015 | HARADA, IKEDA, FUJITA SAMPLING NEXT RAY y  For non transmission surface, no need to sample the other side RANDOM SAMPLING for(int i=0; i<nx; i++) for(int j=0; j<ny; j++)! {! Ray ray = genPrimaryRay( i, j );! ! float4 coeff = 1.f;! float4& output = pixel[i,j];! for(int depth=0; depth<maxDepth; depth++)! {! Hit hit = intersect( ray );! if( !hit.hasHit() ) break;! ! if( hit.isEmissive() )! {// Implicit Connection! output += coeff * getEmission( hit );! break;! }! ! ray, f, pdf = randomSample( hit );! coeff *= f * dot( hit.m_n, ray.m_dir ) / pdf;! }! }!

9. 9 | OPENCL BDPT | AUGUST 28, 2015 | HARADA, IKEDA, FUJITA SAMPLING NEXT RAY y  Random sampling is not always efficient ‒ Diffuse surface => OK ‒ Glossy surface => Hmm L ‒ Specular surface => Bad L y  We usually know the reflecJon characterisJcs about surface ‒ Why not use them? ‒ “Importance sampling” (BRDF) RANDOM SAMPLING

10. 10 | OPENCL BDPT | AUGUST 28, 2015 | HARADA, IKEDA, FUJITA IMPORTANCE SAMPLING (BRDF) y  Specular surface ‒ Only reﬂect in the mirrored direcJon ‒ Always sample the direcJon (pdf = 1) y  Glossy surface ‒ Mostly reﬂect around mirrored direcJon ‒ Sample around the mirrored direcJon Specular (R) Glossy Mae Specular (T)

11. 11 | OPENCL BDPT | AUGUST 28, 2015 | HARADA, IKEDA, FUJITA PATH TRACING IMPLEMENTATION #2 for(int i=0; i<nx; i++) for(int j=0; j<ny; j++)! {! Ray ray = genPrimaryRay( i, j );! ! float4 coeff = 1.f;! float4& output = pixel[i,j];! for(int depth=0; depth<maxDepth; depth++)! {! Hit hit = intersect( ray );! if( !hit.hasHit() ) break;! ! if( hit.isEmissive() )! {// Implicit Connection! output += coeff * getEmission( hit );! break;! }! ! ray, f, pdf = randomSample( hit );! coeff *= f * dot( hit.m_n, ray.m_dir ) / pdf;! }! }! for(int i=0; i<nx; i++) for(int j=0; j<ny; j++)! {! Ray ray = genPrimaryRay( i, j );! ! float4 coeff = 1.f;! float4& output = pixel[i,j];! for(int depth=0; depth<maxDepth; depth++)! {! Hit hit = intersect( ray );! if( !hit.hasHit() ) break;! ! if( hit.isEmissive() )! {// Implicit Connection! output += coeff * getEmission( hit );! break;! }! ! ray, f, pdf = brdfSample( hit );! coeff *= f * dot( hit.m_n, ray.m_dir ) / pdf;! }! }! Random Sampling Brdf Importance Sampling

12. 12 | OPENCL BDPT | AUGUST 28, 2015 | HARADA, IKEDA, FUJITA PATH TRACING IMPLEMENTATION #2 for(int i=0; i<nx; i++) for(int j=0; j<ny; j++)! {! Ray ray = genPrimaryRay( i, j );! ! float4 coeff = 1.f;! float4& output = pixel[i,j];! for(int depth=0; depth<maxDepth; depth++)! {! Hit hit = intersect( ray );! if( !hit.hasHit() ) break;! ! if( hit.isEmissive() )! {// Implicit Connection! output += coeff * getEmission( hit );! break;! }! ! ray, f, pdf = randomSample( hit );! coeff *= f * dot( hit.m_n, ray.m_dir ) / pdf;! }! }! for(int i=0; i<nx; i++) for(int j=0; j<ny; j++)! {! Ray ray = genPrimaryRay( i, j );! ! float4 coeff = 1.f;! float4& output = pixel[i,j];! for(int depth=0; depth<maxDepth; depth++)! {! Hit hit = intersect( ray );! if( !hit.hasHit() ) break;! ! if( hit.isEmissive() )! {// Implicit Connection! output += coeff * getEmission( hit );! break;! }! ! ray, f, pdf = brdfSample( hit );! coeff *= f * dot( hit.m_n, ray.m_dir ) / pdf;! }! }! Random Sampling Brdf Importance Sampling

13. 13 | OPENCL BDPT | AUGUST 28, 2015 | HARADA, IKEDA, FUJITA INEFFICIENT CASE y  Rough surface + Small light y  Brdf sampling rarely hits the light y  SoluJon ‒ We know where the lights are ‒ Why ignoring?? ‒ Generate ray from lights ‒ “Importance sampling” (Light) Rough Reference Brdf sampling

14. 14 | OPENCL BDPT | AUGUST 28, 2015 | HARADA, IKEDA, FUJITA INEFFICIENT CASE y  Rough surface + Small light y  Brdf sampling rarely hits the light y  SoluJon ‒ We know where the lights are ‒ Why ignoring?? ‒ Generate ray from lights ‒ “Importance sampling” (Light) Rough Reference Brdf sampling Light sampling

15. 15 | OPENCL BDPT | AUGUST 28, 2015 | HARADA, IKEDA, FUJITA PATH TRACING IMPLEMENTATION #3 for(int i=0; i<nx; i++) for(int j=0; j<ny; j++)! {! Ray ray = genPrimaryRay( i, j );! ! float4 coeff = 1.f;! float4& output = pixel[i,j];! for(int depth=0; depth<maxDepth; depth++)! {! Hit hit = intersect( ray );! if( !hit.hasHit() ) break;! ! if( hit.isEmissive() )! {// Implicit Connection! output += coeff * getEmission( hit );! break;! }! ! ray, f, pdf = brdfSample( hit );! coeff *= f * dot( hit.m_n, ray.m_dir ) / pdf;! }! }! for(int i=0; i<nx; i++) for(int j=0; j<ny; j++)! {! Ray ray = genPrimaryRay( i, j );! ! float4 coeff = 1.f;! float4& output = pixel[i,j];! for(int depth=0; depth<maxDepth; depth++)! {! Hit hit = intersect( ray );! if( !hit.hasHit() ) break;! {// Explicit Connection! ray, pdf = lightSample( hit );! Hit hit1 = intersect( ray );! if( !hit1.hasHit() )! {! output+=coeff*getEmission( hit1 )/pdf;! }! }! ray, f, pdf = brdfSample( hit );! coeff *= f * dot( hit.m_n, ray.m_dir ) / pdf;! }! }! Brdf Importance Sampling Light Importance Sampling

16. 16 | OPENCL BDPT | AUGUST 28, 2015 | HARADA, IKEDA, FUJITA IMPLICIT CONNECTION, EXPLICIT CONNECTION y  Implicit ConnecJon ‒ A ray accidentally hit a light source ‒  Ater primary rays are generated from camera ‒  Ater bounced rays are sampled from BRDF y  Explicit ConnecJon ‒ A path is intenJonally connected to a light source ‒  Direct illuminaJon (sample a vertex on a light source) Camera Light Object Camera Light Object

17. 17 | OPENCL BDPT | AUGUST 28, 2015 | HARADA, IKEDA, FUJITA PATH TRACING IMPLEMENTATION #3 y  We should not accumulaJng implicit hits for this implementaBon y  Even if a ray hits an emissive surface, we ignore it y  Sounds like we are wasJng something NOTE

18. 18 | OPENCL BDPT | AUGUST 28, 2015 | HARADA, IKEDA, FUJITA BRDF SAMPLING + LIGHT SAMPLING y  Both generate image ‒ Each technique has strong & weak points ‒ Converges to the same image at the end Brdf sampling (16spp) Light sampling (16spp) Brdf sampling (inf. spp) Light sampling (inf. spp) =

19. 19 | OPENCL BDPT | AUGUST 28, 2015 | HARADA, IKEDA, FUJITA PATH TRACING IMPLEMENTATION #3 y  We should not accumulaJng implicit hits for this implementaBon y  Even if a ray hits an emissive surface, we ignore it y  Sounds like we are wasJng something y  Can we use it somehow? NOTE

20. 20 | OPENCL BDPT | AUGUST 28, 2015 | HARADA, IKEDA, FUJITA BRDF SAMPLING + LIGHT SAMPLING y  Then simply… Take an average? ‒ Works ‒  but not the best y  We can set any coeﬃcients if they sum up to 1.0 ‒ brdfSampledImage * 0.2 + lightSampledImage * 0.8 ? ‒ brdfSampledImage * 0.8 + lightSampledImage * 0.2 ? ‒ Hmm… x 0.5 + x 0.5 Brdf sample Light sample x 0.2 + x 0.8 Brdf sample Light sample

21. 21 | OPENCL BDPT | AUGUST 28, 2015 | HARADA, IKEDA, FUJITA J L COMPARING BRDF SAMPLING AND LIGHT SAMPLING y  Light sampling ‒ Good for rough surface ‒ Bad for sharp surface y  Brdf sampling ‒ Bad for rough surface ‒ Good for sharp surface L J

22. 22 | OPENCL BDPT | AUGUST 28, 2015 | HARADA, IKEDA, FUJITA BRDF SAMPLING + LIGHT SAMPLING y  Then simply… Take an average? ‒ Works ‒  but not the best y  We can set any coeﬃcients if they sum up to 1.0 ‒ brdfSampledImage * 0.2 + lightSampledImage * 0.8 ? ‒ brdfSampledImage * 0.8 + lightSampledImage * 0.2 ? ‒ Hmm y  We do not have to se the same coeﬃcients for all the pixels J ‒ “MulJple importance sampling” ‒ Per pixel weight x 0.5 + x 0.5 Brdf sample Light sample x 0.2 + x 0.8 Brdf sample Light sample x + x = Brdf sample Light sample w_b w_l MIS

23. 23 | OPENCL BDPT | AUGUST 28, 2015 | HARADA, IKEDA, FUJITA BRDF SAMPLING + LIGHT SAMPLING y  Uniform weighJng (Average) y  Per pixel weighJng (MulJple Importance Sampling, MIS) VISUALIZATION OF MIS x + x = Brdf sample Light sample w_b w_l MIS + = 1.0 w_b w_l x + x = Brdf sample Light sample w_b w_l Average 0.5 0.5 0.5 0.5 + = 1.0 w_b w_l

24. 24 | OPENCL BDPT | AUGUST 28, 2015 | HARADA, IKEDA, FUJITA MULTIPLE IMPORTANCE SAMPLING y  Light sampling y  Brdf sampling 0.1 0.9 0.9 0.1 0.5 0.5

25. 25 | OPENCL BDPT | AUGUST 28, 2015 | HARADA, IKEDA, FUJITA MULTIPLE IMPORTANCE SAMPLING WEIGHT y  Detailed look at brdf & light sampling (direct illuminaJon) y  Possible to sample the same direcJon using both sampling technique ‒ Only difference is the PDF (probability density funcJon) y  Rough surface ‒ PDF of Brdf sampling is almost uniform ‒  Low confidence in the sample => Higher noise ‒ PDF of light sampling has a strong spike at light direcJon ‒  High confidence in the sample => Lower noise Brdf Sampling Light Sampling

26. 26 | OPENCL BDPT | AUGUST 28, 2015 | HARADA, IKEDA, FUJITA MULTIPLE IMPORTANCE SAMPLING WEIGHT y  Detailed look at brdf & light sampling (direct illuminaJon) y  Possible to sample the same direcJon using both sampling technique ‒ Only difference is the PDF (probability density funcJon) y  Sharp surface ‒ PDF of Brdf sampling is almost uniform ‒  Very high confidence in the sample => Lower noise ‒ PDF of light sampling has a strong spike at light direcJon ‒  RelaJvely lower confidence in the sample => Higher noise Brdf Sampling Light Sampling

27. 27 | OPENCL BDPT | AUGUST 28, 2015 | HARADA, IKEDA, FUJITA MULTIPLE IMPORTANCE SAMPLING WEIGHT y  Use sampling PDF to compute weight y  Weight for light sample (Explicit hit) y  Weight for brdf sample (Implicit hit) y  This is very important for an eﬃcient BDPT

28. 28 | OPENCL BDPT | AUGUST 28, 2015 | HARADA, IKEDA, FUJITA PATH TRACING IMPLEMENTATION #4 (FINAL) for(int i=0; i<nx; i++) for(int j=0; j<ny; j++)! {! Ray ray = genPrimaryRay( i, j );! ! float4 coeff = 1.f;! float4& output = pixel[i,j];! float pdfb = 0.f;! for(int depth=0; depth<maxDepth; depth++)! {! Hit hit = intersect( ray );! if( !hit.hasHit() ) break;! if( hit.isEmissive() )! {// Implicit Connection! pdfl = lightPdf( hit );! w = pdfb / (pdfb + pdfl);! output += coeff * getEmission( hit ) * w;! break;! }! {// Explicit Connection! ray, pdfl = lightSample( hit );! Hit hit1 = intersect( ray );! if( !hit1.hasHit() )! {! w = pdfl / (pdfb + pdfl);! output += coeff * getEmission( hit1 ) * w / pdfl;! }! }! ray, f, pdfb = brdfSample( hit );! coeff *= f * dot( hit.m_n, nextRay.m_dir ) / pdfb;! }! }!

29. BEYOND PATH TRACING

30. 30 | OPENCL BDPT | AUGUST 28, 2015 | HARADA, IKEDA, FUJITA OUTDOOR

31. 31 | OPENCL BDPT | AUGUST 28, 2015 | HARADA, IKEDA, FUJITA INDOOR/OUTDOOR

32. 32 | OPENCL BDPT | AUGUST 28, 2015 | HARADA, IKEDA, FUJITA INDOOR

33. 33 | OPENCL BDPT | AUGUST 28, 2015 | HARADA, IKEDA, FUJITA PATH TRACING IS NOT PERFECT y  Path tracing is really good at an out door scene y  But not for a scene mostly lit by indirect illuminaJon (indoor, day Jme)

34. 34 | OPENCL BDPT | AUGUST 28, 2015 | HARADA, IKEDA, FUJITA PATH TRACING IS NOT PERFECT 64 spp 256 spp 1024 spp x 4 x 4 Indoor Indoor/outdoor Outdoor Good Ok Bad Path Tracing is..

35. 35 | OPENCL BDPT | AUGUST 28, 2015 | HARADA, IKEDA, FUJITA BAD CONVERGENCE FOR INDOOR SCENE y  Ray needs to be bounced more than once before connecJng to the light source y  As we bounce, we get less & less conﬁdent about the path ‒ Lower probability => More noise

36. 36 | OPENCL BDPT | AUGUST 28, 2015 | HARADA, IKEDA, FUJITA LIGHT TRACING y  If traced from the light, it is beeer (more conﬁdent) y  Path Tracing y  Light Tracing Camera Light Object Camera Light Object Path Tracing BDPT

37. 37 | OPENCL BDPT | AUGUST 28, 2015 | HARADA, IKEDA, FUJITA PATH TRACING + 1 LIGHT PATH y  Can we mix path tracing & light tracing ‒ Trace 1 light path ‒ Connect to camera path Camera Light Object

38. 38 | OPENCL BDPT | AUGUST 28, 2015 | HARADA, IKEDA, FUJITA PATH TRACING + 1 LIGHT PATH y  Can we mix path tracing & light tracing ‒ Trace 1 light path ‒ Connect to camera path y  Improves some situaJons (not all) ‒ Example. A room is lit by bounced light y  Can we trace more than 1 light path, and combine? ‒ => BidirecJonal Path Tracing Camera Light Object Path Tracing Path Trace + 1 light path 64 spp

39. BDPT

40. Bi-Directional Path Tracing (BDPT) Camera path Light path Connection Camera Light Object •  BDPT traces paths from the camera and the light source  •  BDPT combines various path sampling strategies

44. BDPT I = w0 w1 + + … wk · · · x0 xk •  The contributions of the sampling strategies are weighted

45. BDPT + Multiple Importance Sampling (MIS) Xs = x0, · · · xs + xs+1, · · · xk x0 xk xs xs+1 •  MIS weight computation ws(X) = ps(X) Pk i=0 pi(X)

46. x0 xk xs xs+1 Xs = x0, · · · xs + xs+1, · · · xk ps(X) = !p 0 · !p 1 · · · !p s · p s+1 · · · p k 1 · p k BDPT + Multiple Importance Sampling (MIS) (2 The product of the pdfs of the path sampling

47. The problem of BDPT on GPU implementation 0 1 2 3Global Memory 0 1 2 •  BDPT stores all the vertices on the camera and light paths  •  The global memory consumption is high

48. 0 1 2 3Global Memory 0 1 2 •  BDPT stores all the vertices on the camera and light paths  •  The global memory consumption is high The problem of BDPT on GPU implementation

51. p  Instant BDPT  p  Lvc BDPT BDPT algorithm with low memory consumption

52. Instant BDPT Implicit view ( ) path Explicit view ( ) path Implicit light ( ) path Explicit light ( ) path VI VE LE LI •  Path sampling strategies are limited to 4 (PT and LT)  •  Low memory consumption  •  If the camera has no collision detection, 3 sampling strategies

53. Instant BDPT + MIS Explicit view ( ) path VE wVE (X) = pVE (X) pVE (X) + pVI (X) + pLE (X) 1 wVE (X) = 1 + pVI (X) pVE (X) + pLE (X) pVE (X)

54. Instant BDPT + MIS (2 1 wVE (X) = 1 + pVI (X) pVE (X) + pLE (X) pVE (X) pVI (X) pVE (X) = !p k 1 p k+1 !p k 1 p k+1

55. Instant BDPT + MIS (3 1 wVE (X) = 1 + pVI (X) pVE (X) + pLE (X) pVE (X) x0 xk s0 = 1 P(x 1 ! x0 ! x1) pLE (X) pVE (X) = s0s1 · · · sk x1

56. Instant BDPT + MIS (3 1 wVE (X) = 1 + pVI (X) pVE (X) + pLE (X) pVE (X) x0 xk s1 = 1 P(x0 ! x1 ! x2)G(x0 $ x1) pLE (X) pVE (X) = s0s1 · · · sk x1

57. Instant BDPT + MIS (3 1 wVE (X) = 1 + pVI (X) pVE (X) + pLE (X) pVE (X) x0 xk pLE (X) pVE (X) = s0s1 · · · sk sk = P(xk 1 xk xk+1)G(xk 1 $ xk) x1

58. Instant BDPT OpenCL implementation

59. Instant BDPT : Bad case1 x0 xk

60. x0 xk Instant BDPT : Bad case1

63. x0 xk Specular Instant BDPT : Bad case

67. p  Instant BDPT  p  Lvc BDPT BDPT algorithm with low memory consumption

68. Lvc BDPT light vertex cache 0 1 2 3Light Vertex Cache •  Light vertex cache •  Recursive MIS

71. Lvc BDPT light vertex cache 0 1 2 3Light Vertex Cache •  Light vertex cache •  Recursive MIS  •  LvcBDPT doesn’t have the global memory for the camera path

72. Recursive MIS x0 xk xs ws(X) = ps(X) Pk i=0 pi(X) 1 ws(X) = Pk i=0 pi(X) ps(X) p s+1 · dE s + 1 + !p s · dL s+1=

73. 1 ws(X) = p s+1 · dE s + 1 + !p s · dL s+1 dE 1 = 1 !p 0 dE s = 1 + p sdE s 1 !p s 1 dL k = 1 p k+1 Recursive MIS x0 xk xs dL s+1 = 1 + !p s+1 · dL s+2 p s+2

74. 1 ws(X) = p s+1 · dE s + 1 + !p s · dL s+1 Lvc BDPT + Recursive MIS x0 xk xs dE 1 = 1 !p 0

75. 1 ws(X) = p s+1 · dE s + 1 + !p s · dL s+1 x0 xk xs dE s = 1 + p sdE s 1 !p s 1 Lvc BDPT + Recursive MIS

76. 1 ws(X) = p s+1 · dE s + 1 + !p s · dL s+1 x0 xk xs dE s = 1 + p sdE s 1 !p s 1 dL k = 1 p k+1 Lvc BDPT + Recursive MIS

77. 1 ws(X) = p s+1 · dE s + 1 + !p s · dL s+1 x0 xk xs dE s = 1 + p sdE s 1 !p s 1 dL k 1 = 1 + !p k 1 · dL k p k Lvc BDPT + Recursive MIS

78. 1 ws(X) = p s+1 · dE s + 1 + !p s · dL s+1 x0 xk xs dE s = 1 + p sdE s 1 !p s 1 dL s+1 = 1 + !p s+1 · dL s+2 p s+2 Lvc BDPT + Recursive MIS

79. Lvc BDPT OpenCL implementation

80. InstantBDPT LvcBDPT

83. INTEGRATION TO THE ENGINE

84. 84 | OPENCL BDPT | AUGUST 28, 2015 | HARADA, IKEDA, FUJITA Back End World GPU (OCL) -‐  Path Tracing -‐  Light Tracing -‐  -‐  World CPU -‐  Path Tracing (OpJmized) Front End ENGINE ARCHITECTURE OVERVIEW Data Loader -‐  Obj -‐  Eson -‐  Fbx Data Manager -‐  Texture -‐  Material -‐  Mesh Tahoe API Texture Loader -‐  Png -‐  Hdr -‐  OpenExr -‐  … Factory (CPU) Camera -‐  PerspecJve -‐  Parallel -‐  Bake -‐  VR Material System -‐  Default -‐  Graph -‐  OSL Light Sampler -‐  Uniform -‐  Group -‐  StochasJc Factory (OCL)

85. 85 | OPENCL BDPT | AUGUST 28, 2015 | HARADA, IKEDA, FUJITA Back End World GPU (OCL) -‐  Path Tracing -‐  Light Tracing -‐  BidirecJonal Path Tracing (LvcBDPT) -‐  BidirecJonal Path Tracing (InstantBDPT) World CPU -‐  Path Tracing (OpJmized) Front End ENGINE ARCHITECTURE OVERVIEW Data Loader -‐  Obj -‐  Eson -‐  Fbx Data Manager -‐  Texture -‐  Material -‐  Mesh Tahoe API Texture Loader -‐  Png -‐  Hdr -‐  OpenExr -‐  … Factory (CPU) Camera -‐  PerspecJve -‐  Parallel -‐  Bake -‐  VR Material System -‐  Default -‐  Graph -‐  OSL Light Sampler -‐  Uniform -‐  Group -‐  StochasJc Factory (OCL)

86. 86 | OPENCL BDPT | AUGUST 28, 2015 | HARADA, IKEDA, FUJITA PATH TRACING CODE y  Not employing a mega kernel implementaJon y  There are many small kernels for path tracing ‒ Easier to debug ‒ Performance ‒ Extendibility => Key to add BDPT y  (See my slides @ CEDEC 2014)

87. 87 | OPENCL BDPT | AUGUST 28, 2015 | HARADA, IKEDA, FUJITA PATH TRACING KERNELS y  Prepare ‒ Camera ray set up y  Loop ‒ Ray cast ‒ Implicit hit accumulaJon ‒ Material prepare ‒ Shadow ray set up ‒ Material evaluaJon ‒ Ray cast ‒ Explicit hit accumulaJon ‒ Sample next ray Path Tracing Each line is one or more OCL kernels

88. 88 | OPENCL BDPT | AUGUST 28, 2015 | HARADA, IKEDA, FUJITA INSTANT BDPT y  Prepare ‒ Set up camera ray ‒ Sample light point y  Loop ‒ Ray cast ‒ Material prepare (sampleBrdf) ‒ Implicit connecJon ‒ Explicit connecJon to light ‒  Make shadow ray ‒  Ray cast ‒  Connect to light ‒ Sample next ray ‒ Update MIS weight term Trace Light Path Trace Camera Path Orange: New kernels wrieen for BDPT White : Using path tracing kernels

89. 89 | OPENCL BDPT | AUGUST 28, 2015 | HARADA, IKEDA, FUJITA INSTANT BDPT y  Prepare ‒ Set up light ray ‒ Sample lens point ‒ Explicit connecJon light to camera y  Loop ‒ Ray cast ‒ Material prepare (sampleBrdf) ‒ Explicit connecJon to camera ‒  Make shadow ray ‒  Ray cast ‒  Set pixel index ‒  Connect to camera ‒ Sample next ray ‒ Update MIS weight term y  Prepare ‒ Set up camera ray ‒ Sample light point y  Loop ‒ Ray cast ‒ Material prepare (sampleBrdf) ‒ Implicit connecJon ‒ Explicit connecJon to light ‒  Make shadow ray ‒  Ray cast ‒  Connect to light ‒ Sample next ray ‒ Update MIS weight term Trace Light Path Trace Camera Path Orange: New kernels wrieen for BDPT White : Using path tracing kernels

90. 90 | OPENCL BDPT | AUGUST 28, 2015 | HARADA, IKEDA, FUJITA LVCBDPT y  More complicated than Instant BDPT y  ImplementaJon is not that far from Instant BDPT

91. EXAMPLES

92. 92 | OPENCL BDPT | AUGUST 28, 2015 | HARADA, IKEDA, FUJITA BDPT PT

95. 95 | OPENCL BDPT | AUGUST 28, 2015 | HARADA, IKEDA, FUJITA CLOSING y  References ‒ AddiJonal note ‒  To be published in CEDEC library ‒  Detailed explanaJon of Instant BDPT, Lvc BDPT ‒ CEDEC 2013 ‒  hep://www.slideshare.net/takahiroharada/introducJon-‐to-‐monte-‐carlo-‐ray-‐tracing-‐cedec2013 ‒ CEDEC 2014 ‒  hep://www.slideshare.net/takahiroharada/introducJon-‐to-‐monte-‐carlo-‐ray-‐tracing-‐opencl-‐implementaJon-‐cedec-‐2014 y  Feedback is welcome ‒ For next years presentaJon

96. 96 | OPENCL BDPT | AUGUST 28, 2015 | HARADA, IKEDA, FUJITA NON SYMMETRIC BRDF Without Fix With Fix

97. 97 | OPENCL BDPT | AUGUST 28, 2015 | HARADA, IKEDA, FUJITA NON SYMMETRIC BRDF With Fix With Fix

Introduction to Bidirectional Path Tracing (BDPT) & Implementation using OpenCL (CEDEC 2015)

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Introduction to Bidirectional Path Tracing (BDPT) & Implementation using OpenCL (CEDEC 2015)

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