Lightcuts: A Scalable Lightcuts: A Scalable Approach to - - PowerPoint PPT Presentation

Lightcuts: A Scalable Approach to Illumination Lightcuts: A Scalable Approach to Illumination

Bruce Walter, Sebastian Fernandez, Adam Arbree, Mike Donikian, Kavita Bala, Donald Greenberg

Program of Computer Graphics, Cornell University

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Lightcuts Lightcuts

• Efficient, accurate complex illumination

Environment map lighting & indirect Time 111s Textured area lights & indirect Time 98s

(640x480, Anti-aliased, Glossy materials)

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Scalable Scalable

• Scalable solution for many point lights

– Thousands to millions – Sub-linear cost

100 200 300 400 500 600 1000 2000 3000 4000

Number of Point Lights Time (secs) Standard Ward Lightcut

Tableau Scene

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Complex Lighting 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

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Related Work 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]

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Talk Overview Talk Overview

• Lightcuts

– Scalable accurate solution for complex illumination

• Reconstruction cuts

– Builds on lightcuts – Use smart interpolation to further reduce cost

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Lightcuts Problem Lightcuts Problem

Visible surface

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Lightcuts Problem Lightcuts Problem

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Lightcuts Problem Lightcuts Problem

Camera

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Key Concepts Key Concepts

• Light Cluster

– Approximate many lights by a single brighter light

(the representative light)

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Key Concepts Key Concepts

• Light Cluster
• Light Tree

– Binary tree of lights and clusters

Clusters Individual Lights

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Key Concepts Key Concepts

• Light Cluster
• Light Tree
• A Cut

– A set of nodes that partitions the lights into clusters

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Simple Example Simple Example

#1 #2 #3 #4

1 2 3 4 1 4

Light Tree

Clusters Individual Lights Representative Light

4

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Three Example Cuts Three Example Cuts

1 2 3 4 1 4 4 1 2 3 4 1 4 4 1 2 3 4 1 4 4

Three Cuts #1 #2 #4 #1 #3 #4 #1 #4

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Three Example Cuts Three Example Cuts

1 2 3 4 1 4 4 1 2 3 4 1 4 4 1 2 3 4 1 4 4

Three Cuts #1 #2 #4 #1 #3 #4 #1 #4

Good Bad Bad

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Three Example Cuts Three Example Cuts

1 2 3 4 1 4 4 1 2 3 4 1 4 4 1 2 3 4 1 4 4

Three Cuts #1 #2 #4 #1 #3 #4 #1 #4

Bad Good Bad

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Three Example Cuts Three Example Cuts

1 2 3 4 1 4 4 1 2 3 4 1 4 4 1 2 3 4 1 4 4

Three Cuts #1 #2 #4 #1 #3 #4 #1 #4

Good Good Good

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Algorithm Overview Algorithm Overview

• Pre-process

– Convert illumination to point lights – Build light tree

• For each eye ray

– Choose a cut to approximate the illumination

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Convert Illumination 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

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Algorithm Overview 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

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Perceptual Metric 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

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Illumination Equation Illumination Equation

M a t e r i a l t e r m

result =

Mi Gi Vi Ii

Σ

lights

G e

• m

e t r i c t e r m V i s i b i l i t y t e r m L i g h t i n t e n s i t y

Currently support diffuse, phong, and Ward

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Illumination Equation Illumination Equation

M a t e r i a l t e r m

result =

Mi Gi Vi Ii

Σ

lights

G e

• m

e t r i c t e r m V i s i b i l i t y t e r m L i g h t i n t e n s i t y

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Illumination Equation Illumination Equation

M a t e r i a l t e r m

result =

Mi Gi Vi Ii

Σ

lights

G e

• m

e t r i c t e r m V i s i b i l i t y t e r m L i g h t i n t e n s i t y

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Cluster Approximation Cluster Approximation

Cluster

result ~ Mj Gj Vj

Ii

Σ

lights

~

j is the representative light

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error < Mub Gub Vub

Ii

Cluster Error Bound Cluster Error Bound

Cluster

Σ

lights

−

• Bound each term

– Visibility <= 1 (trivial) – Intensity is known – Bound material and

geometric terms using cluster bounding volume

ub == upper bound

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Cut Selection Algorithm Cut Selection Algorithm

Cut

• Start with coarse cut (eg, root node)

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Cut Selection Algorithm Cut Selection Algorithm

Cut

• Select cluster with largest error bound

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Cut Selection Algorithm Cut Selection Algorithm

Cut

• Refine if error bound > 2% of total

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Cut Selection Algorithm Cut Selection Algorithm

Cut

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Cut Selection Algorithm Cut Selection Algorithm

Cut

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Cut Selection Algorithm Cut Selection Algorithm

Cut

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Cut Selection Algorithm Cut Selection Algorithm

Cut

• Repeat until cut obeys 2% threshold

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)

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Combined Illumination Combined Illumination

Lightcuts 128s 4 608 Lights (Area lights only) Lightcuts 290s 59 672 Lights (Area + Sun/sky + Indirect)

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Combined Illumination 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)

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Lightcuts Recap 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

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Talk Overview Talk Overview

• Lightcuts

– Scalable accurate solution for complex illumination

• Reconstruction cuts

– Builds on lightcuts – Use smart interpolation to further reduce cost

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Reconstruction Cuts 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)

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Image Subdivision Image Subdivision

• Divide into max block size (4x4 blocks)

4x4 block

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Image Subdivision 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

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Image Subdivision 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

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Image Subdivision 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

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Sample Construction 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

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Reconstruction Cut Reconstruction Cut

• Top-down traversal of light tree

– Comparing impostors from nearby samples

Not visited Recurse Occluded Interpolate Shadow ray

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Reconstruction Cut Reconstruction Cut

• Recurse if samples differ significantly

Not visited Recurse Occluded Interpolate Shadow ray

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Reconstruction Cut Reconstruction Cut

• Discard if cluster occluded at all samples

Not visited Recurse Occluded Interpolate Shadow ray

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Reconstruction Cut Reconstruction Cut

Not visited Recurse Occluded Interpolate Shadow ray

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Reconstruction Cut Reconstruction Cut

Not visited Recurse Occluded Interpolate Shadow ray

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Reconstruction Cut Reconstruction Cut

• Interpolate if sample impostors are similar

Not visited Recurse Occluded Interpolate Shadow ray

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Reconstruction Cut Reconstruction Cut

• If cluster contribution small enough, shoot

shadow ray to representative light

– Lightcut-style evaluation

Not visited Recurse Occluded Interpolate Shadow ray

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Reconstruction Cut Reconstruction Cut

Not visited Recurse Occluded Interpolate Shadow ray

Temple, 2.1M polygons, 505064 lights, (Sun/sky+Indirect)

Temple, reconstruction cut block size

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Result Statistics Result Statistics

• Temple model (2.1M polys, 505064 lights)

5.5 1

• Avg. eye rays

per pixel

189s 225s

Image time Image algorithm

Combined (anti-aliased) Lightcuts only Reconstruction cut Lightcut

Cut type

• Avg. shadow rays per cut

9.4 373

Grand Central, 1.46M polygons, 143464 lights, (Area+Sun/sky+Indirect)

• Avg. shadow rays per eye ray 46 (0.03%)

Tableau, 630K polygons, 13000 lights, (EnvMap+Indirect)

• Avg. shadow rays per eye ray 17 (0.13%)

Bigscreen, 628K polygons, 639528 lights, (Area+Indirect)

• Avg. shadow rays per eye ray 17 (0.003%)

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Conclusions Conclusions

• Lightcuts

– Scalable, unified framework

for complex illumination

– Analytic cluster error bounds

& perceptual visibility metric

• Reconstruction cuts

– Exploits coherence – High-resolution, anti-aliased images

100 200 300 400 500 600 1000 2000 3000 4000

Number of Point Lights Time (secs) Standard Ward Lightcut

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Future Work Future Work

• Visibility bounds
• More light types

– Spot lights etc.

• More BRDF types

– Need cheap tight bounds

• Other illumination types

– Eg, caustics

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Acknowledgements 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

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The End The End

• Questions?

Lightcuts implementation sketch, Petree Hall C, ~4:30pm

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Scalable Scalable

• Scalable solution for many point lights

– Thousands to millions – Sub-linear cost

100 200 300 400 500 600 1000 2000 3000 4000 5000

Number of Point Lights Time (secs) Standard Ward Lightcuts

100 200 300 400 500 600 1000 2000 3000 4000

Number of Point Lights Time (secs) Standard Ward Lightcut

Tableau Scene Kitchen Scene

Lightcuts Reference Error x 16 Cut size

Kitchen, 388K polygons, 59,672 Lights

Kitchen, shadow ray false color

750 1500

Tableau, shadow ray false color

750 1500

Kitchen with sample locations marked

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Types of Point Lights Types of Point Lights

• Omni

– Spherical lights

• Oriented

– Area lights, indirect lights

• Directional

– HDR env maps, sun&sky