Lightcuts: A Scalable Lightcuts: A Scalable Approach to Illumination Approach to Illumination Bruce Walter, Sebastian Fernandez, Adam Arbree, Mike Donikian, Kavita Bala, Donald Greenberg Program of Computer Graphics, Cornell University
SIGGRAPH 2005 Lightcuts Lightcuts LIGHTCUTS • Efficient, accurate complex illumination Environment map lighting & indirect Textured area lights & indirect Time 111s Time 98s (640x480, Anti-aliased, Glossy materials) 2
SIGGRAPH 2005 Scalable Scalable LIGHTCUTS • Scalable solution for many point lights – Thousands to millions – Sub-linear cost 600 Standard 500 Ward Time (secs) 400 Lightcut 300 200 100 Tableau Scene 0 0 1000 2000 3000 4000 Number of Point Lights 3
SIGGRAPH 2005 Complex Lighting Complex Lighting LIGHTCUTS • Simulate complex illumination using point lights – Area lights – HDR environment maps – Sun & sky light – Indirect illumination • Unifies illumination – Enables tradeoffs Area lights + Sun/sky + Indirect between components 4
SIGGRAPH 2005 Related Work Related Work LIGHTCUTS • 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
SIGGRAPH 2005 Talk Overview Talk Overview LIGHTCUTS • Lightcuts – Scalable accurate solution for complex illumination • Reconstruction cuts – Builds on lightcuts – Use smart interpolation to further reduce cost 6
SIGGRAPH 2005 Lightcuts Problem Lightcuts Problem LIGHTCUTS Visible surface 7
SIGGRAPH 2005 Lightcuts Problem Lightcuts Problem LIGHTCUTS 8
SIGGRAPH 2005 Lightcuts Problem Lightcuts Problem LIGHTCUTS Camera 9
SIGGRAPH 2005 Key Concepts Key Concepts LIGHTCUTS • Light Cluster – Approximate many lights by a single brighter light (the representative light) 10
SIGGRAPH 2005 Key Concepts Key Concepts LIGHTCUTS • Light Cluster • Light Tree – Binary tree of lights and clusters Clusters Individual Lights 11
SIGGRAPH 2005 Key Concepts Key Concepts LIGHTCUTS • Light Cluster • Light Tree • A Cut – A set of nodes that partitions the lights into clusters 12
SIGGRAPH 2005 Simple Example Simple Example LIGHTCUTS Light Tree Representative 4 #1 #4 #2 #3 Light Clusters 1 4 Individual 1 2 3 4 Lights 13
SIGGRAPH 2005 Three Example Cuts Three Example Cuts LIGHTCUTS Three Cuts #1 #2 #1 #1 #4 #3 #4 #4 4 4 4 1 4 1 4 1 4 1 2 3 4 1 2 3 4 1 2 3 4 14
SIGGRAPH 2005 Three Example Cuts Three Example Cuts LIGHTCUTS Three Cuts #1 #2 #1 #1 #4 #3 #4 #4 4 4 4 1 4 1 4 1 4 1 2 3 4 1 2 3 4 1 2 3 4 Good Bad Bad 15
SIGGRAPH 2005 Three Example Cuts Three Example Cuts LIGHTCUTS Three Cuts #1 #2 #1 #1 #4 #3 #4 #4 4 4 4 1 4 1 4 1 4 1 2 3 4 1 2 3 4 1 2 3 4 Bad Good Bad 16
SIGGRAPH 2005 Three Example Cuts Three Example Cuts LIGHTCUTS Three Cuts #1 #2 #1 #1 #4 #3 #4 #4 4 4 4 1 4 1 4 1 4 1 2 3 4 1 2 3 4 1 2 3 4 Good Good Good 17
SIGGRAPH 2005 Algorithm Overview Algorithm Overview LIGHTCUTS • Pre-process – Convert illumination to point lights – Build light tree • For each eye ray – Choose a cut to approximate the illumination 18
SIGGRAPH 2005 Convert Illumination Convert Illumination LIGHTCUTS • 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
SIGGRAPH 2005 Algorithm Overview Algorithm Overview LIGHTCUTS • 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
SIGGRAPH 2005 Perceptual Metric Perceptual Metric LIGHTCUTS • 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
SIGGRAPH 2005 Illumination Equation Illumination Equation LIGHTCUTS Σ M i G i V i I i result = lights V L M G i i e g a s o h t i b e m t i r l i i i n e a t y t t l e r t i t n e c e s r r t m m i e t y r m Currently support diffuse, phong, and Ward 22
SIGGRAPH 2005 Illumination Equation Illumination Equation LIGHTCUTS Σ M i G i V i I i result = lights V L M G i i e g a s o h t i b e m t i r l i i i n e a t y t t l e r t i t n e c e s r r t m m i e t y r m 23
SIGGRAPH 2005 Illumination Equation Illumination Equation LIGHTCUTS Σ M i G i V i I i result = lights V L M G i i e g a s o h t i b e m t i r l i i i n e a t y t t l e r t i t n e c e s r r t m m i e t y r m 24
SIGGRAPH 2005 Cluster Approximation Cluster Approximation LIGHTCUTS Σ result ~ M j G j V j I i ~ lights j is the representative light Cluster 25
SIGGRAPH 2005 Cluster Error Bound Cluster Error Bound LIGHTCUTS Σ error < M ub G ub V ub I i − lights • Bound each term – Visibility <= 1 (trivial) Cluster – Intensity is known – Bound material and geometric terms using cluster bounding volume ub == upper bound 26
SIGGRAPH 2005 Cut Selection Algorithm Cut Selection Algorithm LIGHTCUTS • Start with coarse cut (eg, root node) Cut 27
SIGGRAPH 2005 Cut Selection Algorithm Cut Selection Algorithm LIGHTCUTS • Select cluster with largest error bound Cut 28
SIGGRAPH 2005 Cut Selection Algorithm Cut Selection Algorithm LIGHTCUTS • Refine if error bound > 2% of total Cut 29
SIGGRAPH 2005 Cut Selection Algorithm Cut Selection Algorithm LIGHTCUTS Cut 30
SIGGRAPH 2005 Cut Selection Algorithm Cut Selection Algorithm LIGHTCUTS Cut 31
SIGGRAPH 2005 Cut Selection Algorithm Cut Selection Algorithm LIGHTCUTS Cut 32
SIGGRAPH 2005 Cut Selection Algorithm Cut Selection Algorithm LIGHTCUTS • Repeat until cut obeys 2% threshold Cut 33
Lightcuts (128s) Reference (1096s) Kitchen, 388K polygons, 4608 lights (72 area sources)
Lightcuts (128s) Reference (1096s) Error Error x16 Kitchen, 388K polygons, 4608 lights (72 area sources)
SIGGRAPH 2005 Combined Illumination Combined Illumination LIGHTCUTS Lightcuts 128s Lightcuts 290s 4 608 Lights 59 672 Lights (Area lights only) (Area + Sun/sky + Indirect) 36
SIGGRAPH 2005 Combined Illumination Combined Illumination LIGHTCUTS Lightcuts 128s Lightcuts 290s 4 608 Lights 59 672 Lights (Area lights only) (Area + Sun/sky + Indirect) Avg. 259 shadow rays / pixel Avg. 478 shadow rays / pixel (only 54 to area lights) 37
SIGGRAPH 2005 Lightcuts Recap Lightcuts Recap LIGHTCUTS • 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
SIGGRAPH 2005 Talk Overview Talk Overview LIGHTCUTS • Lightcuts – Scalable accurate solution for complex illumination • Reconstruction cuts – Builds on lightcuts – Use smart interpolation to further reduce cost 39
SIGGRAPH 2005 Reconstruction Cuts Reconstruction Cuts LIGHTCUTS • 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
SIGGRAPH 2005 Image Subdivision Image Subdivision LIGHTCUTS • Divide into max block size (4x4 blocks) 4x4 block 41
SIGGRAPH 2005 Image Subdivision Image Subdivision LIGHTCUTS • 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
SIGGRAPH 2005 Image Subdivision Image Subdivision LIGHTCUTS • 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
SIGGRAPH 2005 Image Subdivision Image Subdivision LIGHTCUTS • 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
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