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Advanced Global Illumination, Second Edition, 2nd Edition

Name: Advanced Global Illumination, Second Edition, 2nd Edition
ISBN: 9781439864951

by Philip Dutre, Philippe Bekaert, Kavita Bala

August 2006

Intermediate to advanced

384 pages

9h 36m

English

CRC Press

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Foreword
Preface
Preface to the Second Edition
Chapter 1 Introduction
1.1 What Is Realistic Image Synthesis?1.1.1 The Importance of Realistic Image SynthesisFilm and Visual EffectsArchitectureErgonomic Design of Buildings and OfficesComputer GamesLighting EngineeringPredictive SimulationsFlight and Car SimulatorsAdvertising1.1.2 History of Photorealistic RenderingPhotorealism in the Precomputer AgePrimitive Shading AlgorithmsRay TracingRadiosityThe Rendering EquationMultipass MethodsCurrent Developments1.1.3 A Framework for Global Illumination AlgorithmsMeasurement and AcquisitionLight TransportVisual Display1.2 Structure of this Book1.3 How to Use this Book
Chapter 2 The Physics of Light Transport
2.1 Brief History2.2 Models of Light2.2.1 Quantum Optics2.2.2 Wave Model2.2.3 Geometric Optics2.3 Radiometry2.3.1 Radiometric QuantitiesRadiant Power or FluxIrradianceRadiant Exitance or RadiosityRadianceTransport Theory2.3.2 Relationships between Radiometric QuantitiesWavelength Dependency2.3.3 Properties of RadianceProperty 1: Radiance is invariant along straight paths.Property 2: Sensors, such as cameras and the human eye, are sensitive to radiance.2.3.4 ExamplesExample (Diffuse Emitter)Example (Nondiffuse Emitter)Example (Sun, Earth, Mars)Example (Plate)2.4 Light Emission2.5 Interaction of Light with Surfaces2.5.1 BRDF2.5.2 Properties of the BRDF2.5.3 BRDF ExamplesDiffuse SurfacesSpecular SurfacesGlossy Surfaces2.5.4 Shading ModelsPhysically Based Shading ModelsEmpirical Models2.6 Rendering Equation2.6.1 Hemispherical Formulation2.6.2 Area Formulation2.6.3 Direct and Indirect Illumination Formulation2.7 Importance2.8 The Measurement Equation2.9 Summary2.10 ExercisesFigure 2.1Figure 2.2Figure 2.3Figure 2.4Figure 2.5Figure 2.6Figure 2.7Figure 2.8Figure 2.9Figure 2.10Figure 2.11
Chapter 3 Monte Carlo Methods
3.1 Brief History3.2 Why Are Monte Carlo Techniques Useful?3.3 Review of Probability Theory3.3.1 Discrete Random VariablesExpected ValueVariance and Standard DeviationFunctions of Random VariablesExample (Binomial Distribution)3.3.2 Continuous Random VariablesProbability Distribution Function and Cumulative Distribution FunctionExpected ValueVariance and Standard DeviationExample (Uniform Probability Distribution)3.3.3 Conditional and Marginal Probabilities3.4 Monte Carlo Integration3.4.1 Weighted Sum of Random Variables3.4.2 EstimatorExample (Monte Carlo Summation)Example (Simple MC Integration)3.4.3 Bias3.4.4 Accuracy3.4.5 Estimating the Variance3.4.6 Deterministic Quadrature versus Monte Carlo3.4.7 Multidimensional Monte Carlo IntegrationExample (Integration over a Hemisphere)3.4.8 Summary of Monte Carlo3.5 Sampling Random Variables3.5.1 Inverse Cumulative Distribution FunctionDiscrete Random VariablesContinuous Random VariablesExample (Cosine Lobe)3.5.2 Rejection Sampling3.5.3 Look-Up Table3.6 Variance Reduction3.6.1 Importance Sampling3.6.2 Stratified Sampling3.6.3 N-Rooks or Latin Hypercube Algorithm3.6.4 Combining Stratified Sampling and Importance SamplingExample (Stratified Sampling of Discrete Sums)3.6.5 Combining Estimators of Different DistributionsUsing VarianceMultiple Importance Sampling3.6.6 Control Variates3.6.7 Quasi-Monte CarloWhy Quasi–Monte Carlo?3.7 Summary3.8 ExercisesFigure 3.1Figure 3.2Figure 3.3Figure 3.4Figure 3.5Figure 3.6Figure 3.7Figure 3.8Figure 3.9Table 3.1
Chapter 4 Strategies for Computing Light Transport
4.1 Formulation of the Rendering Equation4.1.1 Exitant Radiance, Integration over the Hemisphere4.1.2 Exitant Radiance, Integration over Surfaces4.1.3 Incident Radiance, Integration over the Hemisphere4.1.4 Incident Radiance, Integration over Surfaces4.1.5 Radiant Flux4.2 The Importance Function4.2.1 Definition4.2.2 Incident and Exitant Importance4.2.3 Flux4.3 Adjoint Equations4.3.1 Linear Transport Operators4.3.2 Inner Product of Functions4.3.3 Adjoint Operators4.4 Global Reflectance Distribution Function4.4.1 Description4.4.2 Properties of the GRDFTransport EquationsTransforming ArgumentsFlux4.4.3 Significance of the GRDF4.5 Classification of Global Illumination Algorithms4.5.1 Incident and Exitant Representations4.5.2 Series Expansion4.5.3 Physical Interpretation4.5.4 Taxonomy4.6 Path Formulation4.7 Summary4.8 ExercisesFigure 4.1Figure 4.2Figure 4.3Figure 4.4Figure 4.5Figure 4.6Figure 4.7Figure 4.8Figure 4.9Figure 4.10Figure 4.11Figure 4.12
Chapter 5 Stochastic Path-Tracing Algorithms
5.1 Brief History5.2 Ray-Tracing Set-Up5.3 Simple Stochastic Ray Tracing5.3.1 Truly Random Paths5.3.2 Russian Roulette5.4 Direct Illumination5.4.1 Direct and Indirect Illumination5.4.2 Single Light Source Illumination5.4.3 Multiple Light Source Illumination5.4.4 Alternative Shadow Ray Sampling5.4.5 Further Optimizations5.5 Environment Map Illumination5.5.1 Capturing Environment MapsLight ProbeFisheye Lens5.5.2 Parameterizations5.5.3 Sampling Environment Maps5.6 Indirect Illumination5.6.1 Uniform Sampling for Indirect Illumination5.6.2 Importance Sampling for Indirect IlluminationCosine SamplingBRDF SamplingIncident Radiance Field Sampling5.6.3 Area Sampling5.6.4 Putting It All Together5.6.5 Classic Ray Tracing5.7 Light Tracing5.7.1 Light-Tracing Algorithm5.7.2 Light Tracing versus Ray Tracing5.7.3 Related Techniques and Optimizations5.8 Summary5.9 ExercisesFigure 5.1Figure 5.2Figure 5.3Figure 5.4Figure 5.5Figure 5.6Figure 5.7Figure 5.8Figure 5.9Figure 5.10Figure 5.11Figure 5.12Figure 5.13Figure 5.14Figure 5.15Figure 5.16Figure 5.17Figure 5.18Figure 5.19Figure 5.20Figure 5.21
Chapter 6 Stochastic Radiosity
6.1 Classic Radiosity6.1.1 Outline6.1.2 Mathematical Problem DescriptionThe General Rendering EquationThe Radiosity Integral EquationThe Radiosity System of Linear Equations6.1.3 The Classic Radiosity Method6.1.4 Problems6.2 The Form Factors6.2.1 Properties of the Form Factors6.2.2 Interpretation of Form Factors6.2.3 Form Factor Sampling Using Local Lines6.2.4 Form Factor Sampling Using Global Lines6.3 Stochastic Relaxation Radiosity6.3.1 The Jacobi Iterative Method for RadiosityThe Basic IdeaRegular Gathering of RadiosityRegular Shooting of PowerIncremental Shooting of PowerDiscussion6.3.2 Stochastic Jacobi RadiosityStochastic Incremental Shooting of PowerStochastic Regular Shooting of PowerStochastic Regular Gathering of Radiosity6.3.3 DiscussionIncremental ShootingRegular ShootingRegular GatheringOther Stochastic Relaxation Methods for Radiosity6.4 Discrete Random Walk Methods for Radiosity6.4.1 Random Walks in a Discrete State Space6.4.2 Shooting Random Walk Methods for RadiosityCollision EstimationAbsorption Estimation6.4.3 Adjoint Systems, Importance or Potential, and Gathering Random Walk Methods for Radiosity6.4.4 DiscussionShooting versus GatheringAbsorption, Survival, or Collision?Discrete Collision Shooting Random Walks versus Stochastic Jacobi Relaxation6.5 Photon Density Estimation Methods6.5.1 Photon Transport Simulation and Radiosity6.5.2 Histogram Methods6.5.3 Orthogonal Series Estimation6.5.4 Kernel Methods6.5.5 Nearest Neighbor Methods6.5.6 Instant Radiosity6.5.7 DiscussionContinuous Gathering Random WalksVarianceBiasContinuous versus Discrete Random Walks6.5.8 Stochastic Iteration Variants of Density Estimation Algorithms6.6 Variance Reduction and Low-Discrepancy Sampling6.6.1 View-Importance-Driven ShootingView ImportanceView-Importance-Driven Shooting Random WalksView-Importance-Driven Stochastic Relaxation Radiosity6.6.2 Control VariatesControl Variates for Linear SystemsConstant Control Variates in Random Walk RadiosityConstant Control Variates in Stochastic Relaxation Radiosity6.6.3 Gathering for FreeCombining Gathering and Shooting in Discrete Random Walk RadiosityCombining Gathering and Shooting in Stochastic Jacobi Radiosity6.6.4 Weighted Importance Sampling6.6.5 Low-Discrepancy Sampling6.7 Hierarchical Refinement and Clustering6.8 ExercisesFigure 6.1Figure 6.2Figure 6.3Figure 6.4Figure 6.5Figure 6.6Figure 6.7Figure 6.8Figure 6.9Figure 6.10Figure 6.11Figure 6.12Figure 6.13Figure 6.14Figure 6.15Figure 6.16Figure 6.17Figure 6.18Figure 6.19Figure 6.20Figure 6.21Figure 6.22Figure 6.23Figure 6.24Figure 6.25Figure 6.26Figure 6.27Figure 6.28Figure 6.29Figure 6.30Figure 6.31Table 6.1Table 6.2
Chapter 7 Hybrid Algorithms
7.1 Final Gathering7.1.1 Simple Hemisphere Sampling7.1.2 Importance Sampling7.1.3 Results7.2 Multipass Methods7.2.1 Regular Expressions7.2.2 Construction of a Multipass Algorithm7.2.3 Weighted Multipass Algorithms7.3 Bidirectional Tracing7.4 Metropolis Light TransportDetailed BalanceThe AlgorithmDiscussion7.5 Irradiance CachingInterpolationThe Irradiance Cache7.6 Photon MappingTracing Photons: Pass 1Reflected Radiance using Photon MapsComputing Images: Pass 27.7 Instant Radiosity7.8 Lightcuts and Multidimensional Lightcuts7.8.1 LightcutsUsing the CutFinding the Cut7.8.2 Multidimensional LightcutsSummary7.9 ExercisesFigure 7.1Figure 7.2Figure 7.3Figure 7.4Figure 7.5Figure 7.6Figure 7.7Figure 7.8Figure 7.9Figure 7.10Figure 7.11Figure 7.12Figure 7.13Figure 7.14Figure 7.15Figure 7.16Figure 7.17Figure 7.18Figure 7.19Figure 7.20Figure 7.21

Chapter 8 The Quest for Ultimate Realism and Speed
8.1 Beyond the Rendering Equation8.1.1 Participating Media8.1.2 Volume Emission8.1.3 Absorption8.1.4 Out-Scattering, Extinction Coefficient, and Albedo8.1.5 In-Scattering, Field-and Volume-Radiance, and the Phase FunctionExamples of Phase Functions8.1.6 The Rendering Equation in the Presence of Participating MediaSpatial Formulation8.1.7 Global Illumination Algorithms for Participating Media8.1.8 Tracing Photon Trajectories in Participating Media8.1.9 Volume Photon Density EstimationViewing Precomputed Illumination in Participating Media8.1.10 Light Transport as a Diffusion Process8.1.11 Subsurface Scattering8.1.12 Polarization, Interference, Diffraction, Fluorescence, Phosphorescence and Nonconstant MediaNonconstant Media: Mirages and Twinkling Stars and SuchFluorescence and Phosphorescence: Reflection at a Different Wavelength and a Different TimeInterference: Soap Bubbles and SuchDiffraction: Compact Discs and Brushed MetalsPolarization8.2 Image Display and Human Perception8.2.1 Tone Mapping8.2.2 Perception-Based Acceleration TechniquesVisual Difference PredictorMaximum Likelihood Difference ScalingPerceptually Based Global Illumination Algorithms8.3 Fast Global Illumination8.3.1 Sparse Sampling: Exploiting CoherenceImage SpaceWorld SpaceLine Space8.3.2 Dynamic Scenes8.3.3 Fast Ray Tracing8.3.4 Graphics Hardware and Precomputed Radiance Transfer8.3.5 Ambient Occlusion8.3.6 Precomputed Radiance TransferDiffuse PRTAll-Frequency PRTSummary of PRTFigure 8.1Figure 8.2Figure 8.3Figure 8.4Figure 8.5Figure 8.6Figure 8.7Figure 8.8Figure 8.9Figure 8.10Figure 8.11Figure 8.12Figure 8.13Figure 8.14Figure 8.15Figure 8.16Figure 8.17Figure 8.18Figure 8.19Figure 8.20
Chapter 9 Conclusion
9.1 Achievements of Photorealistic Rendering9.2 Unresolved Issues in Photorealistic Rendering9.3 Concluding Remarks
Appendix A A Class Library for Global Illumination
A.1 Path Node ClassesA.1.1 OverviewA.1.2 Common Interface: The PathNode Base ClassA.1.3 Pixel Filtering and Sampling: The EyeNode ClassA.1.4 Light Emission: The EmissionNode ClassesA.1.5 Light and Potential Scattering: The ScatteringNode ClassesSurface Scattering: SurfaceNode ClassPaths Disappearing into the Background: BackgroundNode ClassA.2 Light Source Sampling ClassesA.2.1 Surface Emission Sampling: The SurfaceEmissionSampler and WeightedSurfaceEmissionSampler ClassesWeighted Surface Emission SamplingA.2.2 Background Emission Sampling: The BackgroundEmissionSampler ClassA.2.3 The EmissionSampler Wrapper ClassA.3 Support ClassesA.3.1 A Pinhole Camera Virtual Screen Abstraction: The ScreenBuffer ClassA.3.2 Converting High to Low Dynamic Range Color Values: The ToneMapper ClassesA.3.3 Integration into an Application: The Browser and Tracer ClassesThe Browser ClassesThe Tracer ClassesA.4 Example Code FragmentsA.4.1 A Light TracerA.4.2 A Path TracerA.4.3 Multiple Importance Light Source SamplingA.4.4 A Bidirectional Path TracerFigure A.1Figure A.2
Appendix B Hemispherical Coordinates
B.1 Hemispherical CoordinatesB.2 Solid AngleB.3 Integrating over the HemisphereB.4 Hemisphere-Area TransformationFigure B.1Figure B.2Figure B.3Figure B.4Figure B.5
Appendix C Theoretical Analysis of Stochastic Relaxation Radiosity
Bibliography

Content preview from Advanced Global Illumination, Second Edition, 2nd Edition

Preface

This book is the result of our experience while teaching a course of the same name at the annual ACM SIGGRAPH conference during 2001 and 2002, as well as teaching various graduate-level courses and seminars covering advanced photorealistic rendering topics. When setting up these courses, we always felt that covering the fundamentals gives a much broader insight into how to design a global illumination algorithm, instead of presenting the student with a number of recipes and ready-to-compile code. By explaining the basic building blocks and underlying theory, the reader should be more able to design and implement his own photorealistic rendering algorithms.

We chose Advanced Global Illumination as the title because we present topics which ...

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Advanced Global Illumination, Second Edition, 2nd Edition

by Philip Dutre, Philippe Bekaert, Kavita Bala

Preface

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.