LIGHT transport

LIGHT TRANSPORT25/11/2011 Shinji Ogaki

4 Papers

• Progressive Photon Beams• Lightslice: Matrix Slice Sampling for Many-

Lights Problem• Modular Radiance Transfer• Practical Filtering for Efficient Ray-Traced

Directional Occlusion

PROGRESSIVE PHOTON BEAMSWojciech Jarosz et at.

1. Cast Photons2. Gather

Photon Mapping

PhotonQuery PointFixed Search Radius

• LS+DS+E Paths• Accurate Caustics• Unlimited # of Photons

Progressive Photon Mapping

PhotonReverse PhotonSearch Radius

• Extension to Volume (LS+MS+E Paths)

PPB (Progressive Photon Beam)

Photon Beam

Query Ray

• L: Radiance• Tr: Transmittance• s: Surface• m: Media• σs: Scattering Coefficient• f: Phase Function

Radiative Transport Equation

Photon Beam

Query Ray

XsS

Xw

W

Beam x Beam 1D Estimator

FluxKernel

Scattering Coef

Results

LIGHTSLICE: MATRIX SLICE SAMPLING FOR MANY-LIGHTS PROBLEM

Jiawei Ou et al.

Many-Lights Problem

• Global Illumination (Diffuse Indirect Illum.)• Matrix Interpretation of Many-Lights

VPL (Virtual Point Light)

Many-Lights Problem

• Global Illumination (Diffuse Indirect Illum.)• Matrix Interpretation of Many-Lights

VPL (Virtual Point Light)

Many-Lights Problem

• Global Illumination (Diffuse Indirect Illum.)• Matrix Interpretation of Many-Lights

VPL (Virtual Point Light)

Many-Lights Problem

• Global Illumination (Diffuse Indirect Illum.)• Matrix Interpretation of Many-Lights

VPL (Virtual Point Light)

Many-Lights Problem

• Global Illumination (Diffuse Indirect Illum.)• Matrix Interpretation of Many-Lights

VPL (Virtual Point Light)

Many-Lights Problem

• Global Illumination (Diffuse Indirect Illum.)• Matrix Interpretation of Many-Lights

VPL (Virtual Point Light) Sample

Transport Matrix

• Close to Low Rank

. .

. .

. . .

.

Algorithm1. Matrix Slicing2. Slice Sampling3. Initial Light Clustering4. Per Cluster Refinement

ResultsSl

ice

Visu

aliza

tion

Results (cont’d)Li

ghts

lice

MRC

SLi

ghtc

ut

Limitations

• Parameter Selection (# of Slices etc.)• Glossy Surface• Animation• Matrix Sparsity

• Comprehensive Comparison is missing (Coherent Light Cut and Pixelcuts?)

MODULAR RADIANCE TRANSFERBradford J. Loos et al.

Module

• Patched Local is Global

Module

Shapes

Transport Matrix (Local)

• F: Direct to Indirect Transfer (One Bounce)

Sample

dind FII

Reduced Direct-to-Indirect Transferin Shape

• Truncated SVD of F• Not so Sparse, Unfortunately

Sample

FFF

FFF

VU

VUF~~~

Reduced Direct-to-Indirect Transferin Shape (cont’d)

• Light Prior (Basis for Plausible Direct Lighting)

d

d

Tddd

Tdddd

S

UP

VU

VUL

~

~

~~~},,,{ 21 dmddd IIIL

Id1 Id2 Idm……

Reduced Direct-to-Indirect Transferin Shape (cont’d)

• Truncated SVD of M• Very Sparse

Sample

dT

dT

dind

IPMS

IPSFPS

FII

1

1)(

Reduced Direct-to-Indirect Transferbetween Shapes (Local to Global)

• Interface

Results

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

PRACTICAL FILTERING FOR EFFICIENT RAY-TRACED DIRECTIONAL OCCLUSION

Kevin Egan et al.

Ambient Occlusion

1

1

10

0

(1+0+1+0+1)/5=0.6

Hemisphere

1. Cast Rays2. Filter

Ambient Occlusionwith a Sparse Set of Rays

Expensive Cheap

Distant Lighting in Linear Sub-Domains

Frequency Analysisand Sheared Filtering

Light(y)

Receiver(x) x

y

Occluder SpectrumOccluder Spectrum

Bandlimited by Filter

Flatland Scene Occlusion Functionf(x, y)

0 Receiver(x) 1

0 Light(y) 1

x

y

Occluders

x

y

Frequency Analysisand Sheared Filtering (cont’d)

Rotationally-Invariant Filter

Infinitesimal Sub-domains

Results6+ mins to filter

Limitations

• Artifacts due to Undersampling in the 1st Pass• Smoothes out some Areas of Detail• Noise in Areas where Brute Force

Computation is used