Global Illumination Techniquesfor the Computation of High Quality Images          in General Environments                 ...
Talk Outline   1.   Introduction   2.   Global Illumination Fundamentals   3.   Participating Media Resolution Methods   4...
Talk Outline   1.   Introduction   2.   Global Illumination Fundamentals   3.   Participating Media Resolution Methods   4...
Subject of this Work            s     Objective                  – Rendering of high quality (HQ) images                  ...
Outline of Contributions  s   Participating Media (PM)      – Study of single and multiple scattering methods  s   Two firs...
Talk Outline   1.   Introduction   2.   Global Illumination Fundamentals   3.   Participating Media Resolution Methods   4...
Global Illumination in Vacuum              Vacuum, time-invariant, gray radiance equation:           L(x, ωo) = Le(x, ωo) ...
Global Illumination with PM           emission            absorption      out-scattering          in-scattering         PM...
Radiance Equation                         x0   L(x0 )                                                  J(u)               ...
Single vs Multiple ScatteringSource radiance (L = Lri +Lm) J(x) = (1 − Ω(x))Le(x)                     Je(x)      Ω(x)    +...
Solving the GI Problem 1. Source radiance                                Ω(x)       J(x) = (1 − Ω(x))Le(x) +              ...
Talk Outline   1.   Introduction   2.   Global Illumination Fundamentals   3.   Participating Media Resolution Methods   4...
Surveys on Resolution Methods with PM   s   J. Howell       Thermal radiation in participating media: The past, the presen...
Classification of SS Methods                                                                                Type of Media  ...
Classification of MS Methods                                               1s   Deterministic MS methods                   ...
Classification of MS Methods                                                               2    s   Stochastic MS methods  ...
Applications vs Methods                                               1    s   Applications restrict the set of        pos...
GI Methods for PM (SS and MS)    s GI methods for PM with Single Scattering      – Ray tracing    s GI methods for PM with...
Global Illumination Methods (FE and MC)     1    s   Finite elements    s   Monte Carlo        – Object space         – Im...
Global Illumination Methods (FE and MC)               2             s   Advantages/disadvantages                 – Finite ...
Combining Finite Elements with Monte Carlo    s   Main idea (hybrid method):        Using a Finite elements coarse solutio...
Importance Sampling    s   Variance reduction technique        – Use the “best” samples to evaluate the integral:         ...
Previous Work           s Chen et al. ‘91             – Progressive Multi-Pass           s Jensen ’95             – Import...
Talk Outline   1.   Introduction   2.   Global Illumination Fundamentals   3.   Participating Media Resolution Methods   4...
Two-Pass Algorithms: Two Combinations  Acceleration of Monte Carlo Path Tracing in General      Environments (in Proceedin...
First Pass: Clustered Radiance                           1 s   Clustered Radiance with participating media   Link hierarch...
First Pass: Clustered Radiance                             2    s Radiant intensity   directional distributions      – Sur...
First Pass: Clustered Radiance Results      Isotropic scattering    Backward scattering   Forward scattering4. 1st Pass: R...
First Pass: Extended HMCR               1 Extended HMCR – Shoot particles from sources    *   Interact with media and surf...
First Pass: Extended HMCR                                       2         s   HMCR with Participating Media             – ...
First Pass: Extended HMCR                                   3    s   Example        – Simple room (direct + indirect illum...
First Pass: Extended HMCR                                  4  s   HMCR: Isotropic media   Interactive rendering      – Sou...
Talk Outline   1.   Introduction   2.   Global Illumination Fundamentals   3.   Participating Media Resolution Methods   4...
Second Pass  s   Importance sampling based on the incoming light      – Use 1st pass results to guide 2nd pass  s   Simila...
2nd Pass: MCPT and Constant Basis Functions  s   Piecewise Constant Basis Functions      – Constant basis representation o...
Constant Basis Functions: Limitations  s   Small projections                                  !                           ...
Link Probabilities  s   Link based approach: Link Probabilities (LPs)      – Using the structure of links directly        ...
Second Pass using Link Probabilities      s   High quality images: Two algorithms          –   Monte Carlo Path Tracing   ...
Link Probabilities in MCPT  Importance Sampling in a random walk step  For each bounce at x             –L     find leaf(x)...
Link Probabilities: Issues                                       1  s   Link overlap                                      ...
Link Probabilities: Issues                                          2  s   PDF accuracy and visibility                    ...
Link Probabilities: Issues                                                 3  s   PDF accuracy and visibility             ...
MCPT: Results                                                       1    s   Simple test scene                  Top view  ...
MCPT: Results                                               2    s   Office environment                      16 samples    ...
Link Probabilities in Ray Tracing                         1 Ray Tracer with Final GatherFor each line of sight (pixel)– Ge...
Link Probabilities in Ray Tracing                            2 Importance Sampling with LPsFor each gathering point x– Com...
Second Pass: Example of refinement     s   Simple room (direct + indirect illumination)                  Model             ...
RT with FG: Results with no Media                                   1    s   Office environment                            ...
RT with FG: Results with no Media                                2    s   Office environment                   16 samples  ...
RT with FG: Results with Media                                      1    s   Office environment                            ...
RT with FG: Results with Media                                     2    s   Office environment                    16 sample...
RT with FG: More Results                                        1     s   Office room (indirect illumination)              ...
RT with FG: More Results                                                     2     s   Kitchen scene (direct illumination)...
RT with FG: More Results                  3     s   Kitchen scene: More views                         1794s       1891s5. ...
RT with FG: More Results                          4    s   Simple scene with a participating medium               HMCR sol...
RT with FG: More Results                                 5    s   Another scene in vacuum and inside a PM                 ...
Talk Outline   1.   Introduction   2.   Global Illumination Fundamentals   3.   Participating Media Resolution Methods   4...
Progressive Rendering      s Exhaustive rendering   Time consuming      s Goals        – Generate an approximate image qui...
Previous Work on Progressive Rendering   s   Recursive Image Subdivision       –   kd-tree (Painter and Sloan ’89)       –...
Our Approach: Using Conductance Maps   s Conductance Maps: used in Anisotropic Filtering   s Isotropic vs Anisotropic Filt...
Anisotropic Diffusion/Filtering: Process      s Like heat diffusion on a thin plate made of        inhomogeneous material ...
Conductance Maps: Simple Example                                                       1                                  ...
Conductance Maps: Simple Example                     2       Orientation                                                  ...
Conductance Maps: Simple Example                       3  s   Final conductance maps cu, cv:                        ×     ...
Progressive Rendering: Basic Process                                          1. Obtain a sample point       the real issu...
Progressive Rendering       s   Our 1st step: Obtaining a sample point           – Chosen from an edge of a Constrained De...
Conductance Maps: Edge Conductances  s   Ordinary Edge  s   Diagonal Edge                                      E+S        ...
Progressive Rendering: Low Resolution Example   s   Straight          4%           8%   16%   32%   s   Regions           ...
Progressive Rendering Results                                  1    s   Kitchen scene           Input model               ...
Progressive Rendering Results                                                   2                       Straight Method   ...
Progressive Rendering Results (Regions M.)   3s   Kitchen                                      10%s   Jaguar              ...
Talk Outline   1.   Introduction   2.   Global Illumination Fundamentals   3.   Participating Media Resolution Methods   4...
Summary of Contributions  s   Participating Media (PM)      – Study of single and multiple scattering methods  s   Two firs...
Publications  Global Illumination Techniques for the Simulation of      Participating Media (in Rendering Techniques ’97) ...
Current Work   s   More general environments: Natural lighting       – Sun and sky light integrated in the HMCR algorithm ...
Future Research                                  1    s   Improve Final Gather        – Scheel et al.’s Grid Based Final G...
Future Research                                    2             s   Extend/Improve                 Progressive Radiance C...
Acknowledgmentss   Girona Graphics Groups   iMAGIS Groups   LSI at UPCs   Sponsors    – ERCIM Computer Graphics Network   ...
Thank you for your attention       Global Illumination Techniquesfor the Computation of High Quality Images          in Ge...
Index for Quick Navigation Through Numbers  Front page 1  Talk Outline 21. Introduction 3 4 52. Global Illumination Fundam...
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Global illumination techniques for the computation of hight quality images in general environments

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Doctorate degree with European Doctor mention by the Universitat Politècnica de Catalunya, May 2003

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Global illumination techniques for the computation of hight quality images in general environments

  1. 1. Global Illumination Techniquesfor the Computation of High Quality Images in General Environments ´ Frederic Perez Advisors: Prof. Xavier Pueyo and Dr. Ignacio Mart´n ı May, 2003 ` Universitat Politecnica de Catalunya ` Departament de Llenguatges i Sistemes Informatics Programa de doctorat de software Dissertation Defense
  2. 2. Talk Outline 1. Introduction 2. Global Illumination Fundamentals 3. Participating Media Resolution Methods 4. First Pass: Rough Solution 5. Second Pass: Link Probabilities 6. Progressive Radiance Computation 7. Conclusions and Future Work 2/78
  3. 3. Talk Outline 1. Introduction 2. Global Illumination Fundamentals 3. Participating Media Resolution Methods 4. First Pass: Rough Solution 5. Second Pass: Link Probabilities 6. Progressive Radiance Computation 7. Conclusions and Future Work 3/78
  4. 4. Subject of this Work s Objective – Rendering of high quality (HQ) images * For general environments · Possibly including participating media · General optical properties * Accounting for the global illumination (GI) s Strategy: Two-pass methods 1. Obtain coarse representation of GI 2. Refine the solution to get a HQ image1. Introduction 4/78
  5. 5. Outline of Contributions s Participating Media (PM) – Study of single and multiple scattering methods s Two first pass methods to solve the Global Illumination (GI) problem – Hierarchical Radiosity with Clustering – Hierarchical Monte Carlo Radiosity s Link Probabilities (LPs) – Probability density functions (PDFs) for importance sampling s Progressive radiance computation – Methods based on conductance maps1. Introduction 5/78
  6. 6. Talk Outline 1. Introduction 2. Global Illumination Fundamentals 3. Participating Media Resolution Methods 4. First Pass: Rough Solution 5. Second Pass: Link Probabilities 6. Progressive Radiance Computation 7. Conclusions and Future Work 6/78
  7. 7. Global Illumination in Vacuum Vacuum, time-invariant, gray radiance equation: L(x, ωo) = Le(x, ωo) + BDF(x, ωo, ωi)Li(x, ωi) cos θi dσωi Ω total emitted reflected + transmitted GI problem: Scene + {BDF, L }   GI solver  {L} e geometry2. GI Fundamentals 7/78
  8. 8. Global Illumination with PM emission absorption out-scattering in-scattering PM, time-invariant, differential gray radiance equation: dL(x) κs(x) = κa(x) Le(x) + L(x, ωi) p(ωo, ωi) dσωi dx 4π Ω emission in-scattering − κa(x) L(x) − κs(x) L(x) absorption out-scattering = κt(x)J(x) − κt(x)L(x) being s κa, κs, κt = κa + κs media coefficients s p(ωo, ωi) the phase function s J(x) the source radiance2. GI Fundamentals 8/78
  9. 9. Radiance Equation x0 L(x0 ) J(u) L(x) x τ(x0 , x) τ(u, x) PM, time-invariant, integral gray radiance equation: x L(x) = τ(x0, x) L(x0) + τ(u, x) κt(u) J(u) du x0 Lri(x) Lm(x) being τ(x0, x) = exp(− x0 κt(u)du) x the transmittance2. GI Fundamentals 9/78
  10. 10. Single vs Multiple ScatteringSource radiance (L = Lri +Lm) J(x) = (1 − Ω(x))Le(x) Je(x) Ω(x) + Lri(x, ωi) p(ωo, ωi) dσωi 4π Ω Jri(x) Multiple Scattering (MS) Ω(x) + Lm(x, ωi) p(ωo, ωi) dσωi 4π Ω Jm(x)being Ω = κaκs s the scattering albedo +κs Single scattering: Jm → 0 Single Scattering (SS)2. GI Fundamentals 10/78
  11. 11. Solving the GI Problem 1. Source radiance Ω(x) J(x) = (1 − Ω(x))Le(x) + L(x, ωi) p(ωo, ωi) dσωi 4π Ω 2. Integral transport equation x L(x) = τ(x0, x) L(x0) + τ(u, x) κt(u) J(u) du x0 Lri(x) Lm(x)2. GI Fundamentals 11/78
  12. 12. Talk Outline 1. Introduction 2. Global Illumination Fundamentals 3. Participating Media Resolution Methods 4. First Pass: Rough Solution 5. Second Pass: Link Probabilities 6. Progressive Radiance Computation 7. Conclusions and Future Work 12/78
  13. 13. Surveys on Resolution Methods with PM s J. Howell Thermal radiation in participating media: The past, the present and some possible futures The Journal of Heat Transfer, 110:1220–1229, Nov. 1988 s H. Rushmeier Rendering Participating Media: Problems and Solutions from Application Areas Fifth Eurographics Workshop on Rendering, June 1994 ´ F. Perez, X. Pueyo and F. Sillion Global Illumination Techniques for the Simulation of Participating Media Eighth Eurographics Workshop on Rendering, June 1997 ´ ´ E. Cerezo, F. Perez, X. Pueyo, F. Seron and F. Sillion A Survey on Participating Media Resolution Methods (to be submitted)3. PM Resolution Methods 13/78
  14. 14. Classification of SS Methods Type of Media Atmospheric effects Determ. General Analytic Clouds Atmos. Smoke Others Stoch. Shafts Fog Reference Blinn Kakiya and Von Herzen Max Klassen Nishita et al. Willis Rushmeier Inakage Ebert and Parent Sakas Kaneda et al. Stam and Fiume Tadamura et al. Nishita et al. Stam Irwin Lecocq et al. Dobashi et al.3. PM Resolution Methods 14/78
  15. 15. Classification of MS Methods 1s Deterministic MS methods Space of directions Isotropic Anisotropic Constant Spherical Discrete Implicit basis functions harmonics ordinates representation Local Global 3d-filter/N dir. interactions interactions Progr. Ref. Sweeps Sweeps Diffusion Zonal method Hierarchies Progr. HR Ref.3. PM Resolution Methods 15/78
  16. 16. Classification of MS Methods 2 s Stochastic MS methods Distance sampling Constant Random CDF = f (κs ) CDF = f (κt ) τa (δ) τa (δ) τa (δ) (k+1) (k) interaction point P = τa (δ)P P (k+1) = ΩP (k) bundle bundle bundle bundle view ind. Light tracing Light tracing Bidirectional path-tracing view dep. Light tracing Photon maps Metropolis Light Transport3. PM Resolution Methods 16/78
  17. 17. Applications vs Methods 1 s Applications restrict the set of possible methods to use Application Image Approach type characterization safety analyses high quality bidirectional PT training systems real time hierarchies, importance entertainment visually pleasant isotropic methods3. PM Resolution Methods 17/78
  18. 18. GI Methods for PM (SS and MS) s GI methods for PM with Single Scattering – Ray tracing s GI methods for PM with Multiple Scattering – Two-pass methods: 1) Illumination pass: View independent Computing illumination of surfaces + source radiances 2) Visualization pass: View dependent Computing the pixel radiances3. PM Resolution Methods 18/78
  19. 19. Global Illumination Methods (FE and MC) 1 s Finite elements s Monte Carlo – Object space – Image space3. PM Resolution Methods 19/78
  20. 20. Global Illumination Methods (FE and MC) 2 s Advantages/disadvantages – Finite elements * Low computation time * High memory requirements # * Biased # – Monte Carlo * Unbiased * Low memory requirements * Noise   High computation time #3. PM Resolution Methods 20/78
  21. 21. Combining Finite Elements with Monte Carlo s Main idea (hybrid method): Using a Finite elements coarse solution to speed up a Monte Carlo pass – Finite elements 1st pass * Low computation time * Coarse solution   not high memory requirements – Monte Carlo 2nd pass * Unbiased * Use of FE’s coarse solution (for importance sampling)   reducing computation time3. PM Resolution Methods 21/78
  22. 22. Importance Sampling s Variance reduction technique – Use the “best” samples to evaluate the integral: Probability Density Function (PDF) prop. to kernel s Reflectance/transmittance equation L(x, ωo) = BDF(x, ωo, ωi) Li(x, ωi) cos θi dσωi Ω = BDF(x, ωo, ωi) dHi(x, ωi) Ω s Importance sampling – Use of approximate Hi(x, ωi) for PDF   by means of a first pass3. PM Resolution Methods 22/78
  23. 23. Previous Work s Chen et al. ‘91 – Progressive Multi-Pass s Jensen ’95 – Importance Driven Photon Maps s Szirmay-Kalos et al. ’98 – Importance Driven Quasi-Random Walk ¨ ˜ s Sturzlinger ’96, Urena Torres ’97 – Final Gathering3. PM Resolution Methods 23/78
  24. 24. Talk Outline 1. Introduction 2. Global Illumination Fundamentals 3. Participating Media Resolution Methods 4. First Pass: Rough Solution 5. Second Pass: Link Probabilities 6. Progressive Radiance Computation 7. Conclusions and Future Work 24/78
  25. 25. Two-Pass Algorithms: Two Combinations Acceleration of Monte Carlo Path Tracing in General Environments (in Proceedings of Pacific Graphics 2000) – 1st Pass: Radiance Clustering * Produces Hierarchical Links * Coarse solution: not for rendering – 2nd Pass: Monte Carlo Path Tracing High Quality Final Gathering for Hierarchical Monte Carlo Radiosity for General Environments (in Proceedings of Computer Graphics International 2002) – 1st Pass: Extended HMCR * Bekaert et al.’s Hierarchical Monte Carlo Radiosity * Coarse solution: not for rendering – 2nd Pass: Ray Tracing + Final Gather4. 1st Pass: Rough Solution 25/78
  26. 26. First Pass: Clustered Radiance 1 s Clustered Radiance with participating media   Link hierarchy – Link = { S *, R *, form factor, . . . } – Cluster = Set of surfaces/media or other clusters4. 1st Pass: Rough Solution 26/78
  27. 27. First Pass: Clustered Radiance 2 s Radiant intensity   directional distributions – Surfaces: dI(x, ωxy) = cos θx L(x, ωxy) dAx – Volumes: dI(x, ωxy) = κt(x) J(x, ωxy) dVx s Convenient form factors definitions s Far field light transport approximation 1. Pull 2. Gather+Push+Refl. Lin I I4. 1st Pass: Rough Solution 27/78
  28. 28. First Pass: Clustered Radiance Results Isotropic scattering Backward scattering Forward scattering4. 1st Pass: Rough Solution 28/78
  29. 29. First Pass: Extended HMCR 1 Extended HMCR – Shoot particles from sources * Interact with media and surfaces * Bounce at specular surfaces (mirror, glass) – Store energy hierarchically * Diffuse component of BRDF – Iterate distributing energy4. 1st Pass: Rough Solution 29/78
  30. 30. First Pass: Extended HMCR 2 s HMCR with Participating Media – Light interacts stochastically at volumes – Oracle uses transmittance Interaction between a surface and a volume4. 1st Pass: Rough Solution 30/78
  31. 31. First Pass: Extended HMCR 3 s Example – Simple room (direct + indirect illumination)   Model HMCR (Rough Global Illumination)4. 1st Pass: Rough Solution 31/78
  32. 32. First Pass: Extended HMCR 4 s HMCR: Isotropic media   Interactive rendering – Source radiances at leaves   3d-textures Direct viewing vs. interpolation Showing the subdivision or not4. 1st Pass: Rough Solution 32/78
  33. 33. Talk Outline 1. Introduction 2. Global Illumination Fundamentals 3. Participating Media Resolution Methods 4. First Pass: Rough Solution 5. Second Pass: Link Probabilities 6. Progressive Radiance Computation 7. Conclusions and Future Work 33/78
  34. 34. Second Pass s Importance sampling based on the incoming light – Use 1st pass results to guide 2nd pass s Similarly for participating media5. 2nd Pass: Link Probabilities 34/78
  35. 35. 2nd Pass: MCPT and Constant Basis Functions s Piecewise Constant Basis Functions – Constant basis representation of irradiance computed from the links S2 S3 S1 S4 R PDF Fixed subdivision of (hemi)sphere   shortcomings * Cannot adapt to irradiance changes * Sources do not fit solid angles * Missing samples5. 2nd Pass: Link Probabilities 35/78
  36. 36. Constant Basis Functions: Limitations s Small projections ! !   Wasting samples s Large elements !   Bad PDF5. 2nd Pass: Link Probabilities 36/78
  37. 37. Link Probabilities s Link based approach: Link Probabilities (LPs) – Using the structure of links directly * Estimate the irradiance per link * PDF = Array of probabilities tied to the links P3 = 0.55 P2 = 0.1 P4 = 0.15 P1 = 0.2 x5. 2nd Pass: Link Probabilities 37/78
  38. 38. Second Pass using Link Probabilities s High quality images: Two algorithms – Monte Carlo Path Tracing – Ray Tracing assisted by Local Gather s Link Probabilities guide the sampling process – First pass stores the set of links5. 2nd Pass: Link Probabilities 38/78
  39. 39. Link Probabilities in MCPT Importance Sampling in a random walk step For each bounce at x   –L find leaf(x) – Collect links arriving at L – Compute probabilities p 2 = .1 p 1 = .3 – Choose link – Sample related solid x p 3 = .2 angle * obtain x’ p 4 = .4 (or sample link’s sender) x’5. 2nd Pass: Link Probabilities 39/78
  40. 40. Link Probabilities: Issues 1 s Link overlap R y S1 x’ x S2   Check if link’s sender is hit5. 2nd Pass: Link Probabilities 40/78
  41. 41. Link Probabilities: Issues 2 s PDF accuracy and visibility R S1 S2 Finite element pass   two links arriving at leaf R5. 2nd Pass: Link Probabilities 41/78
  42. 42. Link Probabilities: Issues 3 s PDF accuracy and visibility R R y2 S1 S1 y1 S2 S2 Solution: Adaptive PDFs #triesi−#failuresi × Hi if #triesi 0 Pi = #triesi ε otherwise5. 2nd Pass: Link Probabilities 42/78
  43. 43. MCPT: Results 1 s Simple test scene Top view Side view Front view Basic MCPT LPs in MCPT5. 2nd Pass: Link Probabilities 43/78
  44. 44. MCPT: Results 2 s Office environment 16 samples 64 samples 128 samples Basic MCPT   131s 522s   1047s LPs in MCPT     232s 702s 1321s5. 2nd Pass: Link Probabilities 44/78
  45. 45. Link Probabilities in Ray Tracing 1 Ray Tracer with Final GatherFor each line of sight (pixel)– Generate gathering points * 1 for the visible surface * N for the media traversed– For each gathering point p1 p2 * Compute link probabilities (LPs) * Estimate irradiance based on LPs p3– Estimate eye radiance using p4 obtained estimations5. 2nd Pass: Link Probabilities 45/78
  46. 46. Link Probabilities in Ray Tracing 2 Importance Sampling with LPsFor each gathering point x– Compute links arriving at x * Refinement procedure: p 1 = .3 p 2 = .1 element x– Compute probabilities x p 3 = .2– Choose link– Sample related solid angle p 4 = .4 * obtain x’ (or sample link’s sender) x’– Estimate incoming light5. 2nd Pass: Link Probabilities 46/78
  47. 47. Second Pass: Example of refinement s Simple room (direct + indirect illumination) Model     HMCR Example links at a gathering point5. 2nd Pass: Link Probabilities 47/78
  48. 48. RT with FG: Results with no Media 1 s Office environment Model MCPT: 362s Final mesh Radiances Ex. links Final gather HMCR solution: 25s5. 2nd Pass: Link Probabilities 48/78
  49. 49. RT with FG: Results with no Media 2 s Office environment 16 samples 64 samples 128 samples MC FG   25+16 s 25+58 s   25+113 s LP FG     26.6+50 s 26.6+91 s 26.6+142 s5. 2nd Pass: Link Probabilities 49/78
  50. 50. RT with FG: Results with Media 1 s Office environment Model MCPT: 379s Final mesh Radiances Ex. links Final gather HMCR solution: 25.5s5. 2nd Pass: Link Probabilities 50/78
  51. 51. RT with FG: Results with Media 2 s Office environment 16 samples 64 samples 128 samples MC FG   25.5+24 s 25.5+69 s   25.5+130 s LP FG   27.1+81 s   27.1+130 s 27.1+189 s5. 2nd Pass: Link Probabilities 51/78
  52. 52. RT with FG: More Results 1 s Office room (indirect illumination)     20s 552s Model HMCR   Final Gather for different views 510s5. 2nd Pass: Link Probabilities 52/78
  53. 53. RT with FG: More Results 2 s Kitchen scene (direct illumination) – Lambertian light sources (windows)     12s 1651s Model HMCR (Rough Final Gather c LightWork Design Global Illumination)5. 2nd Pass: Link Probabilities 53/78
  54. 54. RT with FG: More Results 3 s Kitchen scene: More views 1794s 1891s5. 2nd Pass: Link Probabilities 54/78
  55. 55. RT with FG: More Results 4 s Simple scene with a participating medium HMCR solution: 7s 2nd Pass: 242s5. 2nd Pass: Link Probabilities 55/78
  56. 56. RT with FG: More Results 5 s Another scene in vacuum and inside a PM Vacuum All pervading medium HMCR solution: 23s HMCR solution: 25s 2nd Pass: 100s 2nd Pass: 1065s5. 2nd Pass: Link Probabilities 56/78
  57. 57. Talk Outline 1. Introduction 2. Global Illumination Fundamentals 3. Participating Media Resolution Methods 4. First Pass: Rough Solution 5. Second Pass: Link Probabilities 6. Progressive Radiance Computation 7. Conclusions and Future Work 57/78
  58. 58. Progressive Rendering s Exhaustive rendering   Time consuming s Goals – Generate an approximate image quickly – Gradually refine it towards the final result s Benefits 1. Designer can stop the rendering process at any time * Changing camera or scene parameters 2. Visually satisfactory images can be obtained evaluating a small fraction of the total number of pixels   High reduction of computing time6. Progressive Radiance Computation 58/78
  59. 59. Previous Work on Progressive Rendering s Recursive Image Subdivision – kd-tree (Painter and Sloan ’89) – quadtree (Maillot et al. ’92) * Directional Coherence Map (Guo ’98, Scheel et al. 2001) s Triangulation Based – Constrained Delaunay triangulation * Polyhedral scenes + point light sources (Pighin et al. ’97) – Delaunay triangulation + Voronoi diagram (Notkin and Gotsman ’97, Reisman et al. ’97, 2000) s Others – Holodeck Ray Cache (Ward and Simmons ’99) – RenderCache (Walter et al. ’99) – Contour extraction + CDT for urban scenery (Sillion et al. ’97) s Used by rendering packages: RADIANCE, RenderPark. . .6. Progressive Radiance Computation 59/78
  60. 60. Our Approach: Using Conductance Maps s Conductance Maps: used in Anisotropic Filtering s Isotropic vs Anisotropic Filtering (Example) Original Image Isotropic Diffusion Anisotropic Diffusion s M. D. McCool Anisotropic Diffusion for Monte Carlo Noise Reduction, ACM Transactions on Graphics, 18(2):171–194, 1999. ´ F. Perez, I. Mart´n and X. Pueyo ı Progressive Radiance Computation Based on Conductance Maps (submitted to The Visual Computer).6. Progressive Radiance Computation 60/78
  61. 61. Anisotropic Diffusion/Filtering: Process s Like heat diffusion on a thin plate made of inhomogeneous material – (Homog. material   Isotropic Diffusion) s How heat is spread is governed by “Conductance Maps” (cu, cv) Conductance function c is bounded between 0 and 1 (0 = barrier)6. Progressive Radiance Computation 61/78
  62. 62. Conductance Maps: Simple Example 1 Room scene Ray trace Normals img. Comp. cond. co,u,co,v Combine Scene (Compute Depths img. Comp. cond. cd,u,cd,v cond. cu,cv Camera false images) Mat. img. Comp. cond. cc,u,cc,v maps Preprocess for cu, cv computation6. Progressive Radiance Computation 62/78
  63. 63. Conductance Maps: Simple Example 2 Orientation   co,u co,v Depths   cd,u cd,v Materials   cc,u cc,v6. Progressive Radiance Computation 63/78
  64. 64. Conductance Maps: Simple Example 3 s Final conductance maps cu, cv: × × = co,u cd,u cc,u cu × × = co,v cd,v cc,v cv6. Progressive Radiance Computation 64/78
  65. 65. Progressive Rendering: Basic Process   1. Obtain a sample point the real issue – Adaptive progressive radiance computation: * establish sample where variance/constrast is highest * update the current representation of the image · kd-trees, quadtrees, Delaunay triangulations (disc. meshing) 2. Compute radiance – Ex.: Bidirectional ray tracing, final gather. . . 3. Reconstruct the image – Usually by interpolation6. Progressive Radiance Computation 65/78
  66. 66. Progressive Rendering s Our 1st step: Obtaining a sample point – Chosen from an edge of a Constrained Delaunay Triangulation – Inhomogeneity measure based on   * edge conductance using conductance maps * edge length * color difference of edge’s vertices s Two methods: – Straight Method: Four Initial Vertices – Regions Method: Segmented Conductance Map * Regions established by conductance map · Region = connected part of the image · Neighbors with interpixel conductance threshold6. Progressive Radiance Computation 66/78
  67. 67. Conductance Maps: Edge Conductances s Ordinary Edge s Diagonal Edge E+S S+E6. Progressive Radiance Computation 67/78
  68. 68. Progressive Rendering: Low Resolution Example s Straight 4% 8% 16% 32% s Regions 4% 8% 16% 32%6. Progressive Radiance Computation 68/78
  69. 69. Progressive Rendering Results 1 s Kitchen scene Input model Combined Complete rendering c Goods, S.L. cu, cv 2731s6. Progressive Radiance Computation 69/78
  70. 70. Progressive Rendering Results 2 Straight Method Regions Method 5% 10% 5% 10%Common pre. 6.1s Further pre. – 1.5s ∆ care 2.93s 5.87s 0.96s 2.39s Total time 150.40s 308.98s 176.55s 345.53s6. Progressive Radiance Computation 70/78
  71. 71. Progressive Rendering Results (Regions M.) 3s Kitchen 10%s Jaguar 10%s Vase 20%6. Progressive Radiance Computation 71/78
  72. 72. Talk Outline 1. Introduction 2. Global Illumination Fundamentals 3. Participating Media Resolution Methods 4. First Pass: Rough Solution 5. Second Pass: Link Probabilities 6. Progressive Radiance Computation 7. Conclusions and Future Work 72/78
  73. 73. Summary of Contributions s Participating Media (PM) – Study of single and multiple scattering methods s Two first pass methods to solve the Global Illumination problem – Hierarchical Radiosity with Clustering – Hierarchical Monte Carlo Radiosity s Link Probabilities – Probability density functions for importance sampling s Progressive radiance computation – Methods based on conductance maps7. Conclusions and Future Work 73/78
  74. 74. Publications Global Illumination Techniques for the Simulation of Participating Media (in Rendering Techniques ’97) Acceleration of Monte Carlo Path Tracing in General Environments (in Proceedings of Pacific Graphics 2000) High Quality Final Gathering for Hierarchical Monte Carlo Radiosity for General Environments (in Proceedings of Computer Graphics International 2002) Progressive Radiance Computation Based on Conductance Maps (submitted to The Visual Computer) A Survey on Participating Media Resolution Methods (to be submitted)7. Conclusions and Future Work 74/78
  75. 75. Current Work s More general environments: Natural lighting – Sun and sky light integrated in the HMCR algorithm Sunrise Sunset7. Conclusions and Future Work 75/78
  76. 76. Future Research 1 s Improve Final Gather – Scheel et al.’s Grid Based Final Gather s Caustics s General Materials7. Conclusions and Future Work 76/78
  77. 77. Future Research 2 s Extend/Improve Progressive Radiance Computation – Participating media – Accounting for perceptual issues * Masking effects7. Conclusions and Future Work 77/78
  78. 78. Acknowledgmentss Girona Graphics Groups iMAGIS Groups LSI at UPCs Sponsors – ERCIM Computer Graphics Network – ESPRIT Open LTR project #35772: SIMULGEN – Generalitat de Catalunya * Aire grants + 2001/SGR/00296 – MCYT’s projects * TIC92-1031-C02-01, TIC95-0630-C05-05, TIC95-0614-C03-03, TIC98-0586-C03-02, TIC2001-2392-C03-01 78/78
  79. 79. Thank you for your attention Global Illumination Techniquesfor the Computation of High Quality Images in General Environments ´ Frederic Perez frederic@ima.udg.es http://ima.udg.es/˜frederic PhD Dissertation Programa de doctorat de software
  80. 80. Index for Quick Navigation Through Numbers Front page 1 Talk Outline 21. Introduction 3 4 52. Global Illumination Fundamentals 6 7 8 9 10 113. Participating Media Resolution Methods 12 13 14 15 16 17 18 19 20 21 22 234. First Pass: Rough Solution 24 25 26 27 28 29 30 31 325. Second Pass: Link Probabilities 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 566. Progressive Radiance Computation 57 58 59 60 61 62 63 64 65 66 67 68 69 70 717. Conclusions and Future Work 72 73 74 75 76 77 78

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