recovering brdf models for architectural scenes
DESCRIPTION
SIGGRAPH 2000 Course on Image-Based Surface Details. Recovering BRDF Models for Architectural Scenes. Yizhou Yu Computer Science Division University of California at Berkeley. Image-based Rendering versus Traditional Graphics ( circa 1997 ). + Improved photorealism - PowerPoint PPT PresentationTRANSCRIPT
Yizhou Yu
Recovering BRDF Models for Recovering BRDF Models for Architectural ScenesArchitectural Scenes
Recovering BRDF Models for Recovering BRDF Models for Architectural ScenesArchitectural Scenes
Yizhou Yu
Computer Science Division
University of California at Berkeley
Yizhou Yu
Computer Science Division
University of California at Berkeley
SIGGRAPH 2000 Course onImage-Based Surface Details
Yizhou Yu
Image-based Rendering versus Image-based Rendering versus Traditional Graphics ( circa 1997 )Traditional Graphics ( circa 1997 )Image-based Rendering versus Image-based Rendering versus Traditional Graphics ( circa 1997 )Traditional Graphics ( circa 1997 )
+ Improved photorealism
- Static scene configuration
- Fixed lighting condition
Yizhou Yu
Image-based Modeling and RenderingImage-based Modeling and RenderingImage-based Modeling and RenderingImage-based Modeling and Rendering
• Vary lighting– Recover reflectance properties for multiple objects in a
mutual illumination environment
• Vary lighting– Recover reflectance properties for multiple objects in a
mutual illumination environment
5:00am 6:00am 7:00am 10:00am
Yizhou Yu
The ProblemThe ProblemThe ProblemThe Problem
• Forward Problem: Global Illumination– Couple lighting and reflectance to generate images
• Backward Problem: Inverse Global Illumination– Factorize images into lighting and reflectance
• Forward Problem: Global Illumination– Couple lighting and reflectance to generate images
• Backward Problem: Inverse Global Illumination– Factorize images into lighting and reflectance
Illumination Reflected Light
Reflectance
Yizhou Yu
Global IlluminationGlobal IlluminationGlobal IlluminationGlobal Illumination
Reflectance Properties Images
Geometry Light Sources
LightTransport
Yizhou Yu
Inverse Global IlluminationInverse Global IlluminationInverse Global IlluminationInverse Global Illumination
Reflectance Properties Images
Geometry Light Sources
Yizhou Yu
Input ImagesInput ImagesInput ImagesInput Images
Every surface should be covered by at least one photographA specular highlight should be captured for every specular surface
Yizhou Yu
Camera Radiance Response CurveCamera Radiance Response CurveCamera Radiance Response CurveCamera Radiance Response Curve
• Pixel brightness value is a nonlinear function of radiance.– Debevec & Malik[Siggraph’97]
gives a method to recover this nonlinear mapping.
• Pixel brightness value is a nonlinear function of radiance.– Debevec & Malik[Siggraph’97]
gives a method to recover this nonlinear mapping.
Radiance
IntensitySaturation
Yizhou Yu
In Detail ... In Detail ... In Detail ... In Detail ...
Yizhou Yu
Recovered Geometry and Camera PoseRecovered Geometry and Camera PoseRecovered Geometry and Camera PoseRecovered Geometry and Camera Pose
Yizhou Yu
Light SourcesLight SourcesLight SourcesLight Sources
Spherical light sources are easier to modelLight source intensity can be calibrated from dynamic range images
Yizhou Yu
Synthesized ImagesSynthesized ImagesSynthesized ImagesSynthesized Images
Original Lighting Novel Lighting
Yizhou Yu
A ComparisonA ComparisonA ComparisonA Comparison
Hand-crafted Recovered
Yizhou Yu
OutlineOutlineOutlineOutline
• Diffuse surfaces under mutual illumination
• Non-diffuse surfaces under direct illumination
• Non-diffuse surfaces under mutual illumination
• Diffuse surfaces under mutual illumination
• Non-diffuse surfaces under direct illumination
• Non-diffuse surfaces under mutual illumination
Yizhou Yu
Lambertian Surfaces under Lambertian Surfaces under Mutual IlluminationMutual IlluminationLambertian Surfaces under Lambertian Surfaces under Mutual IlluminationMutual Illumination
j
ijjiii FBEB j
ijjiii FBEB
• Bi, Bj, Ei measured
• Form-factor Fij known
• Solve for diffuse albedo
• Bi, Bj, Ei measured
• Form-factor Fij known
• Solve for diffuse albedo i
iB
jBijF
Source
Target
Yizhou Yu
Parametric BRDF Model [ Ward 92 ]Parametric BRDF Model [ Ward 92 ]Parametric BRDF Model [ Ward 92 ]Parametric BRDF Model [ Ward 92 ]
Isotropic Kernel
Anisotropic Kernel
NHi
r
),(
Ksd
2
22
4
]/tan[exp
coscos
1),(
ri
K
yx
yx
ri
K
4
)]/sin/cos(tan[exp
coscos
1),(
22222
( 3 parameters)
( 5 parameters)
Yizhou Yu
Non-diffuse Surfaces underNon-diffuse Surfaces underDirect IlluminationDirect IlluminationNon-diffuse Surfaces underNon-diffuse Surfaces underDirect IlluminationDirect Illumination
2
,,)),(( min arg iisi
i
di IKIL
sd
2
,,)),(( min arg iisi
i
di IKIL
sd
NH
iisid
i IKIL )),((
iisid
i IKIL )),((
P1P2
P1
P2
Yizhou Yu
Non-diffuse Surfaces under Non-diffuse Surfaces under Mutual IlluminationMutual IlluminationNon-diffuse Surfaces under Non-diffuse Surfaces under Mutual IlluminationMutual Illumination
• Problem: LPiAj is not known. ( unlike diffuse case, where LPiAj = LCkAj )
• Solution: iterative estimation
• Problem: LPiAj is not known. ( unlike diffuse case, where LPiAj = LCkAj )
• Solution: iterative estimation
Cv
Ck
Aj
Pi
LPiAj
LCkAj
LCvPi
Source
Target
Yizhou Yu
Estimation of Specular Difference SEstimation of Specular Difference SEstimation of Specular Difference SEstimation of Specular Difference S
• Estimate specular component of by Monte Carlo ray-tracing using current guess of reflectance parameters.
• Similarly for
• Difference gives S
• Estimate specular component of by Monte Carlo ray-tracing using current guess of reflectance parameters.
• Similarly for
• Difference gives S Cv
Ck
Aj
Pi
LPiAj
LCkAj
LCvPi
LPiAj
LCkAj
Yizhou Yu
Recovering Diffuse Albedo MapsRecovering Diffuse Albedo MapsRecovering Diffuse Albedo MapsRecovering Diffuse Albedo Maps
• Specular properties assumed uniform across each surface, but diffuse albedo allowed to vary.
• Subtract specular component
• Recover pointwise diffuse albedo
• Specular properties assumed uniform across each surface, but diffuse albedo allowed to vary.
• Subtract specular component
• Recover pointwise diffuse albedo
Yizhou Yu
ResultsResultsResultsResults
• A simulated cubical room• A simulated cubical room
Yizhou Yu
Results for the Simulated CaseResults for the Simulated CaseResults for the Simulated CaseResults for the Simulated Case
Diffuse Albedo
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
1 2 3 4 5 60
0.05
0.1
0.15
0.2
0.25
0.3
0.35
1 2 3 4 5 6
Specular Roughness
Yizhou Yu
ResultsResultsResultsResults
• A real conference room• A real conference room
Yizhou Yu
Real vs. Synthetic for Original Lighting Real vs. Synthetic for Original Lighting Real vs. Synthetic for Original Lighting Real vs. Synthetic for Original Lighting
Real
Synthetic
Yizhou Yu
Diffuse Albedo Maps of Identical Diffuse Albedo Maps of Identical Posters in Different PositionsPosters in Different PositionsDiffuse Albedo Maps of Identical Diffuse Albedo Maps of Identical Posters in Different PositionsPosters in Different Positions
Poster A Poster B Poster C
Yizhou Yu
Inverting Color BleedInverting Color BleedInverting Color BleedInverting Color Bleed
Input Photograph Output Albedo Map
Yizhou Yu
Real vs. Synthetic for Novel LightingReal vs. Synthetic for Novel LightingReal vs. Synthetic for Novel LightingReal vs. Synthetic for Novel Lighting
Real
Synthetic
Yizhou Yu
Modeling Outdoor IlluminationModeling Outdoor IlluminationModeling Outdoor IlluminationModeling Outdoor Illumination
• The sun– Diameter 31.8’ seen from the earth.
• The sky– A hemispherical area light source.
• The surrounding environment– May contribute more light than the
sky on shaded side.
• The sun– Diameter 31.8’ seen from the earth.
• The sky– A hemispherical area light source.
• The surrounding environment– May contribute more light than the
sky on shaded side.
Yizhou Yu
A Recovered Sky Radiance ModelA Recovered Sky Radiance ModelA Recovered Sky Radiance ModelA Recovered Sky Radiance Model
) cos ) exp( 1 ))( /cos exp( 1 Lvz( 2 edcba f ) cos ) exp( 1 ))( /cos exp( 1 Lvz( 2 edcba f
Yizhou Yu
Coarse-grain Environment Radiance MapsCoarse-grain Environment Radiance MapsCoarse-grain Environment Radiance MapsCoarse-grain Environment Radiance Maps
• Partition the lower hemisphere into small regions
• Project pixels into regions and obtain the average radiance
• Partition the lower hemisphere into small regions
• Project pixels into regions and obtain the average radiance
Yizhou Yu
Comparison with Real PhotographsComparison with Real PhotographsComparison with Real PhotographsComparison with Real Photographs
Synthetic Real
Yizhou Yu
Inverse Global IlluminationInverse Global IlluminationInverse Global IlluminationInverse Global Illumination
• Detect specular highlights on the surfaces.• Choose sample points inside and around highlights.• Build links between sample points and facets in the
environment• Assign to each facet one photograph and one average
radiance value • Assign zero to Delta_S at each link.• For iter = 1 to n
– For each link, use its Delta_S to update its radiance value.– For each surface having highlights, optimize its BRDF parameters.– For each link, estimate its Delta_S with the new BRDF parameters.
• End
• Detect specular highlights on the surfaces.• Choose sample points inside and around highlights.• Build links between sample points and facets in the
environment• Assign to each facet one photograph and one average
radiance value • Assign zero to Delta_S at each link.• For iter = 1 to n
– For each link, use its Delta_S to update its radiance value.– For each surface having highlights, optimize its BRDF parameters.– For each link, estimate its Delta_S with the new BRDF parameters.
• End