Lightcuts: A Scalable Approach to Illumination

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

3 Scalable Scalable solution for many point lights Thousands to millions Sub-linear cost 600 Time (secs) Standard Ward Lightcut Tableau Scene Number of Point Lights 3

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 4

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] 5

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

7 Lightcuts Problem Visible surface 7

8 Lightcuts Problem 8

9 Lightcuts Problem Camera 9

10 Key Concepts Light Cluster Approximate many lights by a single brighter light (the representative light) 10

11 Key Concepts Light Cluster Light Tree Binary tree of lights and clusters Clusters Individual Lights 11

12 Key Concepts Light Cluster Light Tree A Cut A set of nodes that partitions the lights into clusters 12

13 Simple Example #1 #2 #3 #4 Representative Light Light Tree Clusters Individual Lights 13

14 Three Example Cuts Three Cuts #1 #2 #4 #1 #3 #4 #1 #

15 Three Example Cuts Three Cuts #1 #2 #4 #1 #3 #4 #1 # Good Bad Bad 15

16 Three Example Cuts Three Cuts #1 #2 #4 #1 #3 #4 #1 # Bad Good Bad 16

17 Three Example Cuts Three Cuts #1 #2 #4 #1 #3 #4 #1 # Good Good Good 17

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

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 19

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 20

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 21

22 Illumination Equation result = M i G i V i I i Σlights Geometric term Material term Light intensity Visibility term Currently support diffuse, phong, and Ward 22

23 Illumination Equation result = M i G i V i I i Σlights Geometric term Material term Light intensity Visibility term 23

24 Illumination Equation result = M i G i V i I i Σlights Geometric term Material term Light intensity Visibility term 24

25 Cluster Approximation result ~ M j G j V j I i Σlights Cluster j is the representative light 25

26 Cluster Error Bound error < M ub G ub V ub I i Σlights Bound each term Cluster Visibility <= 1 (trivial) Intensity is known Bound material and geometric terms using cluster bounding volume ub == upper bound 26

27 Cut Selection Algorithm Start with coarse cut (eg, root node) Cut 27

28 Cut Selection Algorithm Select cluster with largest error bound Cut 28

29 Cut Selection Algorithm Refine if error bound > 2% of total Cut 29

30 Cut Selection Algorithm Cut 30

31 Cut Selection Algorithm Cut 31

32 Cut Selection Algorithm Cut 32

33 Cut Selection Algorithm Repeat until cut obeys 2% threshold Cut 33

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

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

36 Combined Illumination Lightcuts 128s Lights (Area lights only) Lightcuts 290s Lights (Area + Sun/sky + Indirect) 36

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

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 38

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

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) 40

41 Image Subdivision Divide into max block size (4x4 blocks) 4x4 block 41

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 42

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 43

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 Samples Reconstruction cut 44

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 45

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

47 Reconstruction Cut Recurse if samples differ significantly Not visited Recurse Occluded Interpolate Shadow ray 47

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

49 Reconstruction Cut Not visited Recurse Occluded Interpolate Shadow ray 49

50 Reconstruction Cut Not visited Recurse Occluded Interpolate Shadow ray 50

51 Reconstruction Cut Interpolate if sample impostors are similar Not visited Recurse Occluded Interpolate Shadow ray 51

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

53 Reconstruction Cut Not visited Recurse Occluded Interpolate Shadow ray 53

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

55 Temple, reconstruction cut block size

56 Result Statistics Temple model (2.1M polys, lights) Lightcut Cut type Reconstruction cut Avg. shadow rays per cut Image algorithm Lightcuts only Combined (anti-aliased) Avg. eye rays per pixel Image time 225s 189s 56

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

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

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

60 Conclusions Lightcuts Scalable, unified framework for complex illumination Analytic cluster error bounds & perceptual visibility metric Time (secs) Standard Ward 400 Lightcut Number of Point Lights Reconstruction cuts Exploits coherence High-resolution, anti-aliased images 60

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

62 Acknowledgements National Science Foundation grant ACI 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 62

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

64 Scalable Scalable solution for many point lights Thousands to millions Sub-linear cost 600 Tableau Scene 600 Kitchen Scene Time (secs) Standard Ward Lightcut Time (secs) Standard Ward Lightcuts Number of Point Lights Number of Point Lights 64

65 Lightcuts Reference Cut size Error x 16

66 Kitchen, 388K polygons, 59,672 Lights

67 Kitchen, shadow ray false color

68 Tableau, shadow ray false color

69 Kitchen with sample locations marked

70 Types of Point Lights Omni Spherical lights Oriented Area lights, indirect lights Directional HDR env maps, sun&sky 70

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