1. Lightcuts: A Scalable Approach to Illumination Bruce Walter, Sebastian Fernandez, Adam Arbree, Mike Donikian, Kavita Bala, Donald Greenberg Program of Computer Graphics, Cornell University

2. LIGHTCUTS SIGGRAPH 2005 2 Lightcuts Efficient, accurate complex illumination Environment map lighting & indirect Time 111s Textured area lights & indirect Time 98s (640x480, Anti-aliased, Glossy materials)

3. LIGHTCUTS SIGGRAPH 2005 3 Scalable Scalable solution for many point lights – Thousands to millions – Sub-linear cost Tableau Scene

4. LIGHTCUTS SIGGRAPH 2005 4 Complex Lighting Simulate complex illumination using point lights – Area lights – HDR environment maps – Sun & sky light – Indirect illumination Unifies illumination – Enables tradeoffs between components Area lights + Sun/sky + Indirect

5. LIGHTCUTS SIGGRAPH 2005 5 Related Work Hierarchical techniques – Hierarchical radiosity [eg, Hanrahan et al. 91, Smits et al. 94] – Light hierarchy [Paquette et al. 98] Many lights – [eg, Teller & Hanrahan 93, Ward 94, Shirley et al. 96, Fernandez et al. 2002, Wald et al. 2003] Illumination coherence – [eg, Kok & Jensen 92, Ward 92, Scheel et al. 2002, Krivanek et al. 2005] Env map illumination – [Debevec 98, Agarwal et al. 2003, Kollig & Keller 2003, Ostromoukhov et al. 2004] Instant Radiosity – [Keller 97, Wald et al. 2002]

6. LIGHTCUTS SIGGRAPH 2005 6 Talk Overview Lightcuts – Scalable accurate solution for complex illumination Reconstruction cuts – Builds on lightcuts – Use smart interpolation to further reduce cost

7. LIGHTCUTS SIGGRAPH 2005 7 Lightcuts Problem Visible surface

8. LIGHTCUTS SIGGRAPH 2005 8 Lightcuts Problem

9. LIGHTCUTS SIGGRAPH 2005 9 Lightcuts Problem Camera

10. LIGHTCUTS SIGGRAPH 2005 10 Key Concepts Light Cluster – Approximate many lights by a single brighter light (the representative light)

11. LIGHTCUTS SIGGRAPH 2005 11 Key Concepts Light Cluster Light Tree – Binary tree of lights and clusters Clusters Individual Lights

12. LIGHTCUTS SIGGRAPH 2005 12 Key Concepts Light Cluster Light Tree A Cut – A set of nodes that partitions the lights into clusters

13. LIGHTCUTS SIGGRAPH 2005 13 Simple Example #1 #2#3 #4 1234 14 Light Tree Clusters Individual Lights Representative Light 4

14. LIGHTCUTS SIGGRAPH 2005 14 Three Example Cuts 1234 14 4 1234 14 4 1234 14 4 Three Cuts #1 #2 #4 #1 #3 #4 #1 #4

15. LIGHTCUTS SIGGRAPH 2005 15 Three Example Cuts 1234 14 4 1234 14 4 1234 14 4 Three Cuts #1 #2 #4 #1 #3 #4 #1 #4 GoodBad

16. LIGHTCUTS SIGGRAPH 2005 16 Three Example Cuts 1234 14 4 1234 14 4 1234 14 4 Three Cuts #1 #2 #4 #1 #3 #4 #1 #4 BadGoodBad

17. LIGHTCUTS SIGGRAPH 2005 17 Three Example Cuts 1234 14 4 1234 14 4 1234 14 4 Three Cuts #1 #2 #4 #1 #3 #4 #1 #4 Good

18. LIGHTCUTS SIGGRAPH 2005 18 Algorithm Overview Pre-process – Convert illumination to point lights – Build light tree For each eye ray – Choose a cut to approximate the illumination

19. LIGHTCUTS SIGGRAPH 2005 19 Convert Illumination HDR environment map – Apply captured light to scene – Convert to directional point lights using [Agarwal et al. 2003] Indirect Illumination – Convert indirect to direct illumination using Instant Radiosity [Keller 97] Caveats: no caustics, clamping, etc. – More lights = more indirect detail

20. LIGHTCUTS SIGGRAPH 2005 20 Algorithm Overview Pre-process – Convert illumination to point lights – Build light tree For each eye ray – Choose a cut to approximate the local illumination Cost vs. accuracy Avoid visible transition artifacts

21. LIGHTCUTS SIGGRAPH 2005 21 Perceptual Metric Weber’s Law – Contrast visibility threshold is fixed percentage of signal – Used 2% in our results Ensure each cluster’s error < visibility threshold – Transitions will not be visible – Used to select cut

22. LIGHTCUTS SIGGRAPH 2005 22 Illumination Equation Material term result =  M i G i V i I i  lights Geometric term Visibility term Light intensity Currently support diffuse, phong, and Ward

23. LIGHTCUTS SIGGRAPH 2005 23 Illumination Equation Material term result =  M i G i V i I i  lights Geometric term Visibility term Light intensity

24. LIGHTCUTS SIGGRAPH 2005 24 Illumination Equation Material term result =  M i G i V i I i  lights Geometric term Visibility term Light intensity

25. LIGHTCUTS SIGGRAPH 2005 25 Cluster Approximation Cluster result ~ M j G j V j  I i  lights ~ j is the representative light

26. LIGHTCUTS SIGGRAPH 2005 26 error  M ub G ub V ub  I i Cluster Error Bound Cluster  lights  Bound each term – Visibility <= 1 (trivial) – Intensity is known – Bound material and geometric terms using cluster bounding volume ub == upper bound

27. LIGHTCUTS SIGGRAPH 2005 27 Cut Selection Algorithm Cut Start with coarse cut (eg, root node)

28. LIGHTCUTS SIGGRAPH 2005 28 Cut Selection Algorithm Cut Select cluster with largest error bound

29. LIGHTCUTS SIGGRAPH 2005 29 Cut Selection Algorithm Cut Refine if error bound > 2% of total

30. LIGHTCUTS SIGGRAPH 2005 30 Cut Selection Algorithm Cut

31. LIGHTCUTS SIGGRAPH 2005 31 Cut Selection Algorithm Cut

32. LIGHTCUTS SIGGRAPH 2005 32 Cut Selection Algorithm Cut

33. LIGHTCUTS SIGGRAPH 2005 33 Cut Selection Algorithm Cut Repeat until cut obeys 2% threshold

34. Lightcuts (128s)Reference (1096s) Kitchen, 388K polygons, 4608 lights (72 area sources)

35. Lightcuts (128s)Reference (1096s) ErrorError x16 Kitchen, 388K polygons, 4608 lights (72 area sources)

36. LIGHTCUTS SIGGRAPH 2005 36 Combined Illumination Lightcuts 128s 4 608 Lights (Area lights only) Lightcuts 290s 59 672 Lights (Area + Sun/sky + Indirect)

37. LIGHTCUTS SIGGRAPH 2005 37 Combined Illumination Lightcuts 128s 4 608 Lights (Area lights only) Avg. 259 shadow rays / pixel Lightcuts 290s 59 672 Lights (Area + Sun/sky + Indirect) Avg. 478 shadow rays / pixel (only 54 to area lights)

38. LIGHTCUTS SIGGRAPH 2005 38 Lightcuts Recap Unified illumination handling Scalable solution for many lights – Locally adaptive representation (the cut) Analytic cluster error bounds – Most important lights always sampled Perceptual visibility metric Lightcuts implementation sketch, Petree Hall C, ~4:30pm

39. LIGHTCUTS SIGGRAPH 2005 39 Talk Overview Lightcuts – Scalable accurate solution for complex illumination Reconstruction cuts – Builds on lightcuts – Use smart interpolation to further reduce cost

40. LIGHTCUTS SIGGRAPH 2005 40 Reconstruction Cuts Subdivide image into blocks – Generate samples at corners Within blocks – Interpolate smooth illumination – Use shadow rays when needed to preserve features Shadow boundaries, glossy highlights, etc. Anti-aliasing – (5-50 samples per pixel)

41. LIGHTCUTS SIGGRAPH 2005 41 Image Subdivision Divide into max block size (4x4 blocks) 4x4 block

42. LIGHTCUTS SIGGRAPH 2005 42 Image Subdivision Divide into max block size (4x4 blocks) Trace multiple eye rays per pixel Subdivide blocks if needed – Based on material, surface normal, and local shadowing configuration

43. LIGHTCUTS SIGGRAPH 2005 43 Image Subdivision Divide into max block size (4x4 blocks) Trace multiple eye rays per pixel Subdivide blocks if needed – Based on material, surface normal, and local shadowing configuration Compute samples at corners Samples

44. LIGHTCUTS SIGGRAPH 2005 44 Image Subdivision Divide into max block size (4x4 blocks) Trace multiple eye rays per pixel Subdivide blocks if needed – Based on material, surface normal, and local shadowing configuration Compute samples at corners Shade eye rays using reconstruction cuts SamplesReconstruction cut

45. LIGHTCUTS SIGGRAPH 2005 45 Sample Construction Compute a lightcut at each sample For each node on or above the cut – Create impostor light (directional light) – Reproduce cluster’s effect at sample Cluster Impostor Directional light

46. LIGHTCUTS SIGGRAPH 2005 46 Reconstruction Cut Top-down traversal of light tree – Comparing impostors from nearby samples Not visited Recurse Occluded Interpolate Shadow ray

47. LIGHTCUTS SIGGRAPH 2005 47 Reconstruction Cut Recurse if samples differ significantly Not visited Recurse Occluded Interpolate Shadow ray

48. LIGHTCUTS SIGGRAPH 2005 48 Reconstruction Cut Discard if cluster occluded at all samples Not visited Recurse Occluded Interpolate Shadow ray

49. LIGHTCUTS SIGGRAPH 2005 49 Reconstruction Cut Not visited Recurse Occluded Interpolate Shadow ray

50. LIGHTCUTS SIGGRAPH 2005 50 Reconstruction Cut Not visited Recurse Occluded Interpolate Shadow ray

51. LIGHTCUTS SIGGRAPH 2005 51 Reconstruction Cut Interpolate if sample impostors are similar Not visited Recurse Occluded Interpolate Shadow ray

52. LIGHTCUTS SIGGRAPH 2005 52 Reconstruction Cut If cluster contribution small enough, shoot shadow ray to representative light – Lightcut-style evaluation Not visited Recurse Occluded Interpolate Shadow ray

53. LIGHTCUTS SIGGRAPH 2005 53 Reconstruction Cut Not visited Recurse Occluded Interpolate Shadow ray

54. Temple, 2.1M polygons, 505 064 lights, (Sun/sky+Indirect)

55. Temple, reconstruction cut block size

56. LIGHTCUTS SIGGRAPH 2005 56 Result Statistics Temple model (2.1M polys, 505 064 lights) Image algorithm Avg. eye rays per pixel Image time Lightcuts only 1225s Combined (anti-aliased) 5.5189s Cut typeAvg. shadow rays per cut Lightcut 373 Reconstruction cut 9.4

57. Grand Central, 1.46M polygons, 143 464 lights, (Area+Sun/sky+Indirect) Avg. shadow rays per eye ray 46 (0.03%)

58. Tableau, 630K polygons, 13 000 lights, (EnvMap+Indirect) Avg. shadow rays per eye ray 17 (0.13%)

59. Bigscreen, 628K polygons, 639 528 lights, (Area+Indirect) Avg. shadow rays per eye ray 17 (0.003%)

60. LIGHTCUTS SIGGRAPH 2005 60 Conclusions Lightcuts – Scalable, unified framework for complex illumination – Analytic cluster error bounds & perceptual visibility metric Reconstruction cuts – Exploits coherence – High-resolution, anti-aliased images

61. LIGHTCUTS SIGGRAPH 2005 61 Future Work Visibility bounds More light types – Spot lights etc. More BRDF types – Need cheap tight bounds Other illumination types – Eg, caustics

62. LIGHTCUTS SIGGRAPH 2005 62 Acknowledgements National Science Foundation grant ACI-0205438 Intel corporation for support and equipment The modelers – Kitchen: Jeremiah Fairbanks – Bigscreen: Will Stokes – Grand Central: Moreno Piccolotto, Yasemin Kologlu, Anne Briggs, Dana Gettman – Temple: Veronica Sundstedt, Patrick Ledda, and the graphics group at University of Bristol – Stanford and Georgia Tech for Buddha and Horse geometry

63. LIGHTCUTS SIGGRAPH 2005 63 The End Questions? Lightcuts implementation sketch, Petree Hall C, ~4:30pm

64. LIGHTCUTS SIGGRAPH 2005 64 Scalable Scalable solution for many point lights – Thousands to millions – Sub-linear cost Tableau SceneKitchen Scene

65. Lightcuts Reference Error x 16 Cut size

66. Kitchen, 388K polygons, 59,672 Lights

67. Kitchen, shadow ray false color 0750 1500

68. Tableau, shadow ray false color 0750 1500

69. Kitchen with sample locations marked

70. LIGHTCUTS SIGGRAPH 2005 70 Types of Point Lights Omni – Spherical lights Oriented – Area lights, indirect lights Directional – HDR env maps, sun&sky