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LIGHT transport

This document discusses innovative methodologies in light transport, focusing on Progressive Photon Beams and the Lightslice approach to address the Many-Lights problem. Key concepts include Modular Radiance Transfer, efficient ray-traced directional occlusion, and practical filtering techniques. We explore global illumination, sampling strategies for virtual point lights, and effective strategies for handling caustics and scattering. Additionally, we address limitations of current algorithms and suggest avenues for future research to enhance lighting simulations in graphics applications.

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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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