Surface Light Fields for 3D Photography Daniel N. Wood University of Washington SIGGRAPH 2001...
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Surface Light Fieldsfor 3D Photography
Daniel N. WoodUniversity of Washington
SIGGRAPH 2001 Course
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Collaborators(and co-authors on SIGGRAPH 2000
paper)Daniel Azuma Wyvern Aldinger
Brian Curless Tom DuchampDavid Salesin Werner Stuetzle
University of Washington
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Outline
1. Surface light field representation
2. Compression primer
3. Surface light fields for 3D photography• With details of compression• And a preliminary look at a new
compression algorithm
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Surface light fields
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Lumisphere-valued “texture” maps
Lumisphere
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Surface light fields in flatland
s
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Surface light fields in flatland
s
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Surface light fields in flatland
s
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Compression primer
• Singular value decomposition(or principal components analysis)
– Handling color
– Using regions
– Reflection parameterization
• Vector quantization
• Others… (Wavelets, DCT)
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Singular value decomposition
SLF
U VT
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SVD in two matrices
EigenTextures
EigenLumispheres
SLF
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First eigenvectors
EigenTextures
EigenLumispheres
SLF First eigentexture and corresponding first eigenlumisphere
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Truncated SVD
Outer product of first eigen-texture and first eigen-lumisphere is closest rank 1 (separable) matrix.
=
Eigen-lumisphere
Eig
en
-textu
re
~~
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Handling color
Separatematrices
Colors insurface texture
(columns)
Colors indirections
(rows)
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Handling color
Separatematrices
Nishino et al.Wood et al.Chen et al.
Colors insurface texture
(columns)
Colors indirections
(rows)
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SVD in color
EigenTextures
EigenLumispheres
SLF
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Zooming in…
EigenTextures
EigenLumispheres
SLF
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Zoom into important vectors
... ...SLF
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Reconstruction using SVD
Original Rank 1 Rank 7 (1:20)
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Surface light field structure
Rows are points on surface
What are the columns? And, can they be made more coherent?
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Surface light field structure
Rows are points on surface
Increasing column coherence:
1. Break into regions, and / or
2. Use reflection parameterization
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Separate SVD for regions
EigenTextures
EigenLumispheres
EigenTextures
EigenLumispheres
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Reconstruction using regions
Original One region(Rank 5)
Two regions(Rank 5)
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Reflection reparameterization
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Reflection reparameterization
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Reflection reparameterization
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Reflection in flatland*
*sort of
Un-reflected Reflected
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Reflection doesn’t happenin the plane
Un-reflected Reflected
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Reflection in 3 space
Un-reflected
Reflected
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Reflection in “flatland”
Un-reflected Reflected
Original Rank 5 Original Rank 5
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Other compression strategies
• Discrete cosine transform[ Miller et al. 1999
– Eurographics Workshop on Rendering]
• Vector quantization[ Wood et al. 2000 - SIGGRAPH ]
• Wavelet decomposition[ Magnor and Girod 2000 - SPIE VCIP]
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Vector quantization (unreflected)
Uncompressed Codebook Vector-quantized
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Vector quantization (reflected)
Uncompressed Codebook Vector-quantized
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Surface light fields for 3D photography
(SIGGRAPH 2000)Goals
Rendering and editing
InputsPhotographs and geometry
RequirementsEstimation and compression
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Overview
Dataacquisition
Estimationand
compressionRendering
Editing
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Overview
Dataacquisition
Estimationand
compressionRendering
Editing
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Scan and reconstruct geometry
Reconstructed geometryRange scans(only a few shown . . .)
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Take photographs
Camera positions Photographs
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Register photographs to geometry
GeometryPhotographs
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Register photographs to geometry
User selected correspondences (rays)
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Parameterizing the geometry
Base mesh Scanned geometry
Map
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Sample base mesh faces
Base mesh Detailed geometry
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Assembling data lumispheres
Data lumisphere
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Overview
Dataacquisition
Estimationand
compressionRendering
Editing
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Pointwise fairing
Faired lumisphereData lumisphere
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Pointwise fairing results
Input photograph Pointwise faired(177 MB)
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Pointwise fairing
Many input data lumispheres Many faired lumispheres
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Compression
Small set of prototypes
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Compression / Estimation
Small set of prototypesMany input data lumispheres
![Page 50: Surface Light Fields for 3D Photography Daniel N. Wood University of Washington SIGGRAPH 2001 Course.](https://reader030.fdocuments.us/reader030/viewer/2022032521/56649d5d5503460f94a3b739/html5/thumbnails/50.jpg)
Median removal
+
Reflected
Median(“diffuse”)
Median-removed(“specular”)
+
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Median removal
Median values Specular Result
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Function quantization
Codebook of lumispheres
Input data lumisphere
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Lloyd iteration
Input data lumispheres
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Lloyd iteration
Codeword
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Lloyd iteration
Perturb codewords to create larger codebook
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Lloyd iteration
Form clusters around each codeword
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Lloyd iteration
Optimize codewords based on clusters
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Lloyd iteration
Create new clusters
![Page 59: Surface Light Fields for 3D Photography Daniel N. Wood University of Washington SIGGRAPH 2001 Course.](https://reader030.fdocuments.us/reader030/viewer/2022032521/56649d5d5503460f94a3b739/html5/thumbnails/59.jpg)
Function quantization results
Input photograph Function quantized(1010 codewords, 2.6 MB)
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Principal function analysis
Subspace of lumispheres
Input data lumisphere
Prototype lumisphere
![Page 61: Surface Light Fields for 3D Photography Daniel N. Wood University of Washington SIGGRAPH 2001 Course.](https://reader030.fdocuments.us/reader030/viewer/2022032521/56649d5d5503460f94a3b739/html5/thumbnails/61.jpg)
Principal function analysis
Approximating subspace
Prototype lumisphere
![Page 62: Surface Light Fields for 3D Photography Daniel N. Wood University of Washington SIGGRAPH 2001 Course.](https://reader030.fdocuments.us/reader030/viewer/2022032521/56649d5d5503460f94a3b739/html5/thumbnails/62.jpg)
Principal function analysis
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Principal function analysis
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Principal function analysis
++
+
Median
PFA decomposition
![Page 65: Surface Light Fields for 3D Photography Daniel N. Wood University of Washington SIGGRAPH 2001 Course.](https://reader030.fdocuments.us/reader030/viewer/2022032521/56649d5d5503460f94a3b739/html5/thumbnails/65.jpg)
Principal function analysis
+ + …
Median
PFA decomposition
=Final
approximation
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Principal function analysis results
Input photograph PFA compressed(Order 5 - 2.5 MB)
![Page 67: Surface Light Fields for 3D Photography Daniel N. Wood University of Washington SIGGRAPH 2001 Course.](https://reader030.fdocuments.us/reader030/viewer/2022032521/56649d5d5503460f94a3b739/html5/thumbnails/67.jpg)
Compression comparison
Pointwise fairing(177 MB)
Function quantization(2.6 MB)
Principal functionanalysis (2.5 MB)
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Rewind…what we didn’t want to do
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Instead, a middle ground
Regularly sampled directions,but not all there.
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New method:Principal components with missing
data
SLF with missing data
Hallucinatedata
Use either fairing or pair-wise present covariance matrix to fill holes
Hole-filled SLF
![Page 71: Surface Light Fields for 3D Photography Daniel N. Wood University of Washington SIGGRAPH 2001 Course.](https://reader030.fdocuments.us/reader030/viewer/2022032521/56649d5d5503460f94a3b739/html5/thumbnails/71.jpg)
New method:Principal components with missing
data
SLF with missing data Low rankapproximation
Hallucinatedata
Find best low rank
approximation
Hole-filled SLF
![Page 72: Surface Light Fields for 3D Photography Daniel N. Wood University of Washington SIGGRAPH 2001 Course.](https://reader030.fdocuments.us/reader030/viewer/2022032521/56649d5d5503460f94a3b739/html5/thumbnails/72.jpg)
New method:Principal components with missing
data
SLF with missing data Low rankapproximation
Hallucinatedata
Improvehallucination
![Page 73: Surface Light Fields for 3D Photography Daniel N. Wood University of Washington SIGGRAPH 2001 Course.](https://reader030.fdocuments.us/reader030/viewer/2022032521/56649d5d5503460f94a3b739/html5/thumbnails/73.jpg)
Preliminary comparison
Principal function analysisOrder 3, RMS error 26.9
SVD with missing dataOrder 3, RMS error 26.1
![Page 74: Surface Light Fields for 3D Photography Daniel N. Wood University of Washington SIGGRAPH 2001 Course.](https://reader030.fdocuments.us/reader030/viewer/2022032521/56649d5d5503460f94a3b739/html5/thumbnails/74.jpg)
Overview
Dataacquisition
Estimationand
compressionRendering
Editing
![Page 75: Surface Light Fields for 3D Photography Daniel N. Wood University of Washington SIGGRAPH 2001 Course.](https://reader030.fdocuments.us/reader030/viewer/2022032521/56649d5d5503460f94a3b739/html5/thumbnails/75.jpg)
Interactive rendererscreen capture
![Page 76: Surface Light Fields for 3D Photography Daniel N. Wood University of Washington SIGGRAPH 2001 Course.](https://reader030.fdocuments.us/reader030/viewer/2022032521/56649d5d5503460f94a3b739/html5/thumbnails/76.jpg)
Overview
Dataacquisition
Estimationand
compressionRendering
Editing
![Page 77: Surface Light Fields for 3D Photography Daniel N. Wood University of Washington SIGGRAPH 2001 Course.](https://reader030.fdocuments.us/reader030/viewer/2022032521/56649d5d5503460f94a3b739/html5/thumbnails/77.jpg)
Lumisphere filtering
Original surface light field Glossier coat
![Page 78: Surface Light Fields for 3D Photography Daniel N. Wood University of Washington SIGGRAPH 2001 Course.](https://reader030.fdocuments.us/reader030/viewer/2022032521/56649d5d5503460f94a3b739/html5/thumbnails/78.jpg)
Lumisphere filtering
![Page 79: Surface Light Fields for 3D Photography Daniel N. Wood University of Washington SIGGRAPH 2001 Course.](https://reader030.fdocuments.us/reader030/viewer/2022032521/56649d5d5503460f94a3b739/html5/thumbnails/79.jpg)
Rotating the environment
Original surface light field Rotated environment
![Page 80: Surface Light Fields for 3D Photography Daniel N. Wood University of Washington SIGGRAPH 2001 Course.](https://reader030.fdocuments.us/reader030/viewer/2022032521/56649d5d5503460f94a3b739/html5/thumbnails/80.jpg)
Deformation
Original Deformed
![Page 81: Surface Light Fields for 3D Photography Daniel N. Wood University of Washington SIGGRAPH 2001 Course.](https://reader030.fdocuments.us/reader030/viewer/2022032521/56649d5d5503460f94a3b739/html5/thumbnails/81.jpg)
Deformation
![Page 82: Surface Light Fields for 3D Photography Daniel N. Wood University of Washington SIGGRAPH 2001 Course.](https://reader030.fdocuments.us/reader030/viewer/2022032521/56649d5d5503460f94a3b739/html5/thumbnails/82.jpg)
Summary
1. Estimation and compression• Function quantization• Principal function analysis
2. Rendering• From compressed representation• With view-dependent level-of-detail
3. Editing• Lumisphere filtering• Geometric deformations and transformations
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Future work
• Better geometry-to-image registration
• Derive geometry from images
• More complex surfaces (mirrored, refractive, fuzzy…) under more complex illumination
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Acknowledgements
• Marc Levoy and Pat Hanrahan– (Thanks for the use of the Stanford
Spherical Gantry)
• Michael Cohen and Richard Szeliski
• National Science Foundation
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Nearly the end
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For more information
http://graphics.cs.washington.edu/projects/slf
Talks, papers, … and raw data.