PRT Summary

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PRT Summary
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Motivation for Precomputed Transfer

• better light integration and light transport
• dynamic, area lights
• shadowing
• interreflections
• in real-time

point light
area light
area lighting, no shadows
area lighting, shadows
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Precomputed Radiance Transfer (PRT)

• represent lighting using spherical harmonics
• low frequency few coefficients

n4
n9
n25
n676
original
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Tabulating PRT
Lighting Basis i
LightingBasis i1
illuminate
store coefs on surface
LightingBasis i2
PRT is a linear operator (matrix) per surface
point maps incident lighting to exit radiance
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Self-Transfer Results (Diffuse)
No Shadows/Inter Shadows
ShadowsInter
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Self-Transfer Results (Glossy)
No Shadows/Inter Shadows
ShadowsInter
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PRT Terminology
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PRT Terminology
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PRT Terminology
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PRT Terminology
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PRT as a Linear Operator

• l light vector (in source basis)
• Mp source-to-exit transfer matrix
• ep exit radiance vector (in exit basis)
• y(vp) exit basis evaluated in direction vp
• ep(vp) exit radiance in direction vp

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PRT Special Case Diffuse Objects
transfer vector rather than matrix
PRT02 SH Xi03 Directional Ng03 Haar Ash
ikhmin02 Steerable

• independent of view (constant exit basis)
• matrix is row vector
• previous work uses different light bases
• image relighting

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PRT Special Case Surface Light Fields
transfer vector rather than matrix
Miller98 Nishino99 Wood00 Chen02 Matusik0
2

• frozen lighting environment
• matrix is column vector

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Factoring PRT (BRDFs)

• Tp source ? transferred incident radiance
• Rp rotate to local frame
• B integrate against BRDF Westin92
• y(vp) ep evaluate exit radiance at vp

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Hemispherical Projection

• exit radiance is defined over hemisphere, not
  sphere
• spherical harmonics not orthogonal over
  hemisphere
• how to project hemispherical functions using SH?
• naïve projection assumes underside is zero
• least squares projection minimizes approximation
  error
• see appendix

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Factoring PRT (BRDFs)
Technique LightB ExitB Note
Sloan02 SH SH Phong
Kautz02 SH Dir Arb
Lehtinen03 SH Dir Lsq
Matusik02 Dir Dir IBR
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Extending PRT to BSSRDFs

• already handled by original equation
• use Jensen02, only multiple scattering (matrix
  with only 1 row)
• mix with conventional BRDF

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Problems With PRT

• Big matrices at each surface point
• 25-vectors for diffuse, x3 for spectral
• 25x25-matrices for glossy
• at 50,000 vertices
• Slows glossy rendering (4hz)
• Frozen View/Light can increase performance
• Not as GPU friendly
• Limits diffuse lighting order
• Only very soft shadows

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Compression Goals

• Decode efficiently
• As much on the GPU as possible
• Render compressed representation directly
• Increase rendering performance
• Make non-diffuse case practical
• Reduce memory consumption
• Not just on disk

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Compression Example
Surface is curve, signal is normal
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Compression Example
Signal Space
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VQ
Cluster normals
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VQ
Replace samples with cluster mean
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PCA
Replace samples with mean linear combination
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CPCA
Compute a linear subspace in each cluster
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CPCA

• Clusters with low dimensional affine models
• How should clustering be done?
• Static PCA
• VQ, followed by one-time per-cluster PCA
• optimizes for piecewise-constant reconstruction
• Iterative PCA
• PCA in the inner loop, slower to compute
• optimizes for piecewise-affine reconstruction

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Static vs. Iterative
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Related Work

• VQPCA Kambhatla94 (static)
• VQPCA Khambhatla97 (iterative)
• Mixture PC Dony95 (iterative)
• More sophisticated models exist
• Brand03, Roweis02
• Mapping to current GPUs is challenging
• Variable storage per vertex
• Partitioning is more difficult (or requires more
  passes)

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Equal Rendering Cost
VQ
PCA
CPCA
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Rendering with CPCA
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Rendering with CPCA
Constant per cluster precompute on the
CPU Rendering is a dot product Compute linear
combination of vectors Only depends on rows of
M
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Non-Local Viewer

• Assume
• vp constant across object (distant viewer)

Rendering independent of view light orders -
linear combination of colors
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Rendering
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Overdraw

• faces belong to 1-3 clusters
• OD 1 ? face drawn once
• OD 2 ? face drawn 2x
• OD 3 ? face drawn 3x
• coherence optimization
• reclassification
• superclustering

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GPU Dataflow
Vertices
Vertex Shader
PixelShader
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Demo
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Results
All examples have 25x25 matrices, 256 clusters,
8 PCA vectors
Model Pts SPCA IPCA FPS
Buddha 49.9k 3m30s 1h51m 27
BuddhaSS 49.9k 6m12s 4h32m 27
Bird Anis 48.7k 6m34s 3h43m 45
Bird Diff 48.7k 43s 3m26s 227
Head 50k 4m20s 2h12m 58.5
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Conclusions

• CPCA
• works in signal space, not surface space
• uses affine subspace per-cluster
• compresses PRT well
• is used directly without blowing out signal
• requires small, uniform state storage
• provides
• faster rendering
• higher-frequency lighting

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Future Work

• time-dependent and parameterized geometry
• higher-frequency lighting
• combination with bi-scale rendering
• better signal continuity

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Questions?

• DirectX SDK for PRT available soon.
• Jason Mitchell, Hugues Hoppe, Jason Sandlin,
  David Kirk
• Stanford, MPI for models