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LIGHT transport

25/11/2011 Shinji Ogaki . LIGHT transport. 4 Papers. Progressive Photon Beams Lightslice: Matrix Slice Sampling for Many-Lights Problem Modular Radiance Transfer Practical Filtering for Efficient Ray-Traced Directional Occlusion. Wojciech Jarosz et at. Progressive Photon Beams.

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LIGHT transport

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  1. 25/11/2011 Shinji Ogaki LIGHT transport

  2. 4 Papers • Progressive Photon Beams • Lightslice: Matrix Slice Sampling for Many-Lights Problem • Modular Radiance Transfer • Practical Filtering for Efficient Ray-Traced Directional Occlusion

  3. WojciechJarosz et at. Progressive Photon Beams

  4. Photon Mapping • Cast Photons • Gather Fixed Search Radius Query Point Photon

  5. Progressive Photon Mapping • LS+DS+E Paths • Accurate Caustics • Unlimited # of Photons Search Radius Reverse Photon Photon

  6. PPB (Progressive Photon Beam) • Extension to Volume (LS+MS+E Paths) Query Ray Photon Beam

  7. Radiative Transport Equation • L: Radiance • Tr: Transmittance • s: Surface • m: Media • σs: Scattering Coefficient • f: Phase Function Xs S Query Ray Xw W Photon Beam

  8. Beam x Beam 1D Estimator Scattering Coef Kernel Flux

  9. Results

  10. JiaweiOu et al. Lightslice: matrix slice sampling for many-lights problem

  11. Many-Lights Problem • Global Illumination (Diffuse Indirect Illum.) • Matrix Interpretation of Many-Lights VPL (Virtual Point Light)

  12. Many-Lights Problem • Global Illumination (Diffuse Indirect Illum.) • Matrix Interpretation of Many-Lights VPL (Virtual Point Light)

  13. Many-Lights Problem • Global Illumination (Diffuse Indirect Illum.) • Matrix Interpretation of Many-Lights VPL (Virtual Point Light)

  14. Many-Lights Problem • Global Illumination (Diffuse Indirect Illum.) • Matrix Interpretation of Many-Lights VPL (Virtual Point Light)

  15. Many-Lights Problem • Global Illumination (Diffuse Indirect Illum.) • Matrix Interpretation of Many-Lights VPL (Virtual Point Light)

  16. Many-Lights Problem • Global Illumination (Diffuse Indirect Illum.) • Matrix Interpretation of Many-Lights VPL (Virtual Point Light) Sample

  17. Transport Matrix • Close to Low Rank . . . . . . . .

  18. Algorithm • Matrix Slicing • Slice Sampling • Initial Light Clustering • Per Cluster Refinement

  19. Results Slice Visualization

  20. Results (cont’d) Lightslice MRCS Lightcut

  21. Limitations • Parameter Selection (# of Slices etc.) • Glossy Surface • Animation • Matrix Sparsity • Comprehensive Comparison is missing (Coherent Light Cut and Pixelcuts?)

  22. Bradford J. Loos et al. Modular radiance transfer

  23. Module • Patched Local is Global Module

  24. Shapes

  25. Transport Matrix (Local) • F: Direct to Indirect Transfer (One Bounce) Sample

  26. Reduced Direct-to-Indirect Transferin Shape • Truncated SVD of F • Not so Sparse, Unfortunately Sample

  27. Reduced Direct-to-Indirect Transferin Shape (cont’d) • Light Prior (Basis for Plausible Direct Lighting) Id1 Id2 …… Idm

  28. Reduced Direct-to-Indirect Transferin Shape (cont’d) • Truncated SVD of M • Very Sparse Sample

  29. Reduced Direct-to-Indirect Transferbetween Shapes (Local to Global) • Interface

  30. Results

  31. Limitations • Lighting Condition outside of the Light Prior • High Frequency Glossy Transport • Large Scale Indirect Shadows within Blocks • Dictionary Shapes (e.g. Internal Occluders) • User Interface

  32. Kevin Egan et al. Practical filtering for efficient ray-traced directional occlusion

  33. Ambient Occlusion Hemisphere 1 0 1 1 0 (1+0+1+0+1)/5=0.6

  34. Ambient Occlusionwith a Sparse Set of Rays • Cast Rays • Filter Expensive Cheap

  35. Distant Lighting in Linear Sub-Domains

  36. Frequency Analysisand Sheared Filtering Occlusion Function f(x, y) Flatland Scene Light(y) 0 Light(y) 1 y Occluders y 0 Receiver(x) 1 x Receiver(x) y x Occluder Spectrum Bandlimited by Filter Occluder Spectrum x

  37. Frequency Analysisand Sheared Filtering (cont’d)

  38. Rotationally-Invariant Filter Infinitesimal Sub-domains

  39. Results 6+ mins to filter 

  40. Limitations • Artifacts due to Undersampling in the 1st Pass • Smoothes out some Areas of Detail • Noise in Areas where Brute Force Computation is used

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