Yizhou Yu
From Global IlluminationFrom Global IlluminationTo Inverse Global IlluminationTo Inverse Global Illumination
From Global IlluminationFrom Global IlluminationTo Inverse Global IlluminationTo Inverse Global Illumination
Yizhou Yu
Computer Science Division
University of California at Berkeley
Yizhou Yu
Computer Science Division
University of California at Berkeley
Yizhou Yu
PublicationsPublicationsPublicationsPublications
• Y. Yu and H. Wu, A Rendering Equation for Specular Transfers and Its Integration into Global Illumination, Eurographics’97
• P. Debevec, Y. Yu and G. Borshukov, Efficient View-Dependent Image-Based Rendering with Projective Texture-Mapping, Eurographics Workshop on Rendering’98
• Y. Yu and J. Malik, Recovering Photometric Properties of Architectural Scenes from Photographs, Siggraph’98
• Y. Yu, P. Debevec, J. Malik and T. Hawkins, Inverse Global Illumination: Recovering Reflectance Models of Real Scenes from Photographs, Siggraph’99
• Y. Yu and H. Wu, A Rendering Equation for Specular Transfers and Its Integration into Global Illumination, Eurographics’97
• P. Debevec, Y. Yu and G. Borshukov, Efficient View-Dependent Image-Based Rendering with Projective Texture-Mapping, Eurographics Workshop on Rendering’98
• Y. Yu and J. Malik, Recovering Photometric Properties of Architectural Scenes from Photographs, Siggraph’98
• Y. Yu, P. Debevec, J. Malik and T. Hawkins, Inverse Global Illumination: Recovering Reflectance Models of Real Scenes from Photographs, Siggraph’99
http://www.cs.berkeley.edu/~yyz
Yizhou Yu
Computer Graphics & VisionComputer Graphics & VisionComputer Graphics & VisionComputer Graphics & Vision
• Graphics– Solving forward simulation
– Synthesizing images from geometry and reflectance
• Vision– Recovering geometry and reflectance
– Extracting data from images
• Graphics– Solving forward simulation
– Synthesizing images from geometry and reflectance
• Vision– Recovering geometry and reflectance
– Extracting data from images
Images Geometry & Reflectance Imagesvision graphics
Yizhou Yu
Global IlluminationGlobal IlluminationGlobal IlluminationGlobal Illumination
Reflectance Properties
Radiance Maps
Geometry Light Sources
LightTransport
Yizhou Yu
Reflectance PropertiesReflectance PropertiesReflectance PropertiesReflectance Properties
Bidirectional Reflectance Distribution Function(BRDF)(wavelength dependent)
i
rri dx
dxddx
along at Light Incident
along at Light Reflected),,(
Incident light
Diffuse
Specular
Yizhou Yu
GeometryGeometryGeometryGeometry
• A polygonal mesh and/or a set of curved surface patches
• A polygonal mesh and/or a set of curved surface patches
Yizhou Yu
Light SourcesLight SourcesLight SourcesLight Sources
• 3D positions and directional radiance distributions
• 3D positions and directional radiance distributions
Yizhou Yu
Light TransportLight TransportLight TransportLight Transport
rd
id
x
The Rendering Equation [ Kajiya’86 ]
ddxLddxdxLdxL iiirirerr cos),(),,(),(),(
Yizhou Yu
Example of Rendering Using Global Example of Rendering Using Global IlluminationIlluminationExample of Rendering Using Global Example of Rendering Using Global IlluminationIllumination
With a mirror Without the mirror
Yu & Wu [ Eurographics’97 ] use bi-directional wavefront tracingto calculate illumination from area sources via curved ideal specular reflectors.
Yizhou Yu
The ProblemThe ProblemThe ProblemThe Problem
• The physics of light transport has been well understood.
• In the absence of real-world geometry and reflectance, rendered images still look synthetic.
• Solution: Image-based Modeling and Rendering (IBMR)
• The physics of light transport has been well understood.
• In the absence of real-world geometry and reflectance, rendered images still look synthetic.
• Solution: Image-based Modeling and Rendering (IBMR)
Yizhou Yu
Image-based Modeling and RenderingImage-based Modeling and RenderingImage-based Modeling and RenderingImage-based Modeling and Rendering
• 1st Generation----vary viewpoint but not lighting– Recover geometry ( explicit or implicit )
– Acquire texture maps
– Facade, Virtualized Reality, View Morphing, Plenoptic Modeling etc.
• 1st Generation----vary viewpoint but not lighting– Recover geometry ( explicit or implicit )
– Acquire texture maps
– Facade, Virtualized Reality, View Morphing, Plenoptic Modeling etc.
Yizhou Yu
Input Photographs and Geometric ModelInput Photographs and Geometric ModelInput Photographs and Geometric ModelInput Photographs and Geometric Model
Yizhou Yu
A Synthetic Sunrise SequenceA Synthetic Sunrise SequenceA Synthetic Sunrise SequenceA Synthetic Sunrise Sequence
5:00am 5:30am 6:00am 6:30am
7:00am 8:00am 9:00am 10:00am
One Day at the End of March
Yizhou Yu
The ProblemThe ProblemThe ProblemThe Problem
• Texture Maps are not Reflectance Maps !
• Need to factorize images into lighting and reflectance maps
• Texture Maps are not Reflectance Maps !
• Need to factorize images into lighting and reflectance maps
Illumination Radiance
Reflectance
Yizhou Yu
Image-based Modeling and RenderingImage-based Modeling and RenderingImage-based Modeling and RenderingImage-based Modeling and Rendering
• 2nd Generation----vary viewpoint and lighting– Recover geometry
– Recover reflectance maps
– Permits rendering using physically based light transport methods
• 2nd Generation----vary viewpoint and lighting– Recover geometry
– Recover reflectance maps
– Permits rendering using physically based light transport methods
Yizhou Yu
Outline of the Rest of the TalkOutline of the Rest of the TalkOutline of the Rest of the TalkOutline of the Rest of the Talk
• 1st Generation IBMR– Real-Time View-Dependent Projective Texture-
Mapping
• 2nd Generation IBMR– General Problem: closely positioned multiple objects
– Simplified Situation: Isolated Objects
• 1st Generation IBMR– Real-Time View-Dependent Projective Texture-
Mapping
• 2nd Generation IBMR– General Problem: closely positioned multiple objects
– Simplified Situation: Isolated Objects
Yizhou Yu
OutlineOutlineOutlineOutline
• 1st Generation IBMRReal-Time View-Dependent Projective Texture-
Mapping
• 2nd Generation IBMR– General Problem: closely positioned multiple objects
– Simplified Situation: Isolated Objects
• 1st Generation IBMRReal-Time View-Dependent Projective Texture-
Mapping
• 2nd Generation IBMR– General Problem: closely positioned multiple objects
– Simplified Situation: Isolated Objects
Yizhou Yu
Real-Time View-Dependent Texture Real-Time View-Dependent Texture MappingMappingReal-Time View-Dependent Texture Real-Time View-Dependent Texture MappingMapping
• VDTM was originally from Façade [ Debevec, Taylor & Malik, Siggraph’96].– Software implementation
– 10 seconds per frame
• Real-Time VDTM– Software object-space visibility preprocessing +
hardware projective texture-mapping
– 20 frames per second on SGI RealityEngine
– 60 frames per second on SGI Onyx2 InfiniteReality
• VDTM was originally from Façade [ Debevec, Taylor & Malik, Siggraph’96].– Software implementation
– 10 seconds per frame
• Real-Time VDTM– Software object-space visibility preprocessing +
hardware projective texture-mapping
– 20 frames per second on SGI RealityEngine
– 60 frames per second on SGI Onyx2 InfiniteReality
Yizhou Yu
Motivation for Visibility Processing: Motivation for Visibility Processing: Artifacts Caused by HardwareArtifacts Caused by HardwareMotivation for Visibility Processing: Motivation for Visibility Processing: Artifacts Caused by HardwareArtifacts Caused by Hardware
Camera
Image
Geometry
Yizhou Yu
Visibility Processing ResultsVisibility Processing ResultsVisibility Processing ResultsVisibility Processing Results
The tower The rest of the campus
Yizhou Yu
OutlineOutlineOutlineOutline
• 1st Generation IBMR– Real-Time View-Dependent Projective Texture-
Mapping
• 2nd Generation IBMRGeneral Problem: closely positioned multiple objects
– Simplified Situation: Isolated Objects
• 1st Generation IBMR– Real-Time View-Dependent Projective Texture-
Mapping
• 2nd Generation IBMRGeneral Problem: closely positioned multiple objects
– Simplified Situation: Isolated Objects
Yizhou Yu
Previous WorkPrevious WorkPrevious WorkPrevious Work
• BRDF Measurement in the Laboratory– [ Ward 92 ], [Dana, Ginneken, Nayar & Koenderink 97]
• Isolated Objects under Direct Illumination– [ Sato, Wheeler & Ikeuchi 97 ]
• BRDF Measurement in the Laboratory– [ Ward 92 ], [Dana, Ginneken, Nayar & Koenderink 97]
• Isolated Objects under Direct Illumination– [ Sato, Wheeler & Ikeuchi 97 ]
General case of multiple objects under mutual illumination has not been studied.
Yizhou Yu
Image-based Reflectance RecoveryImage-based Reflectance RecoveryImage-based Reflectance RecoveryImage-based Reflectance Recovery
• Start from photographs
• Recover geometric model
• Measure and/or recover illumination
• Recover parametric models for reflectance
• Design or Predict Novel illumination
• Re-render the scene
• Start from photographs
• Recover geometric model
• Measure and/or recover illumination
• Recover parametric models for reflectance
• Design or Predict Novel illumination
• Re-render the scene
Yizhou Yu
Global IlluminationGlobal IlluminationGlobal IlluminationGlobal Illumination
Reflectance Properties
Radiance Maps
Geometry Light Sources
Yizhou Yu
Inverse Global IlluminationInverse Global IlluminationInverse Global IlluminationInverse Global Illumination
Reflectance Properties
Radiance Maps
Geometry Light Sources
Yizhou Yu
Geometry Recovered Using FacadeGeometry Recovered Using FacadeGeometry Recovered Using FacadeGeometry Recovered Using Facade
Yizhou Yu
Synthesized Images of the Room under Synthesized Images of the Room under Original and Novel LightingOriginal and Novel LightingSynthesized Images of the Room under Synthesized Images of the Room under Original and Novel LightingOriginal and Novel Lighting
Yizhou Yu
IGI OutlineIGI OutlineIGI OutlineIGI Outline
• IGI for Lambertian surfaces
• IGI for isolated specular surface
• IGI for general surfaces
• Computing diffuse albedo maps
• Results
• IGI for Lambertian surfaces
• IGI for isolated specular surface
• IGI for general surfaces
• Computing diffuse albedo maps
• Results
Yizhou Yu
Inverse Radiosity with Lambertian Inverse Radiosity with Lambertian SurfacesSurfacesInverse Radiosity with Lambertian Inverse Radiosity with Lambertian SurfacesSurfaces
nj
ijjiii FBEB1
nj
ijjiii FBEB1
• Bi, Bj, Ei measured using HDR photographs
• Fij known because geometry is known
• Solve for diffuse albedo
• Bi, Bj, Ei measured using HDR photographs
• Fij known because geometry is known
• Solve for diffuse albedo i
Cv
Ck
Aj
Pi
LPiAj
LCkAj
LCvPi
Yizhou Yu
Recovering Specular Properties from Recovering Specular Properties from Direct IlluminationDirect IlluminationRecovering Specular Properties from Recovering Specular Properties from Direct IlluminationDirect Illumination
2
1
))((min iisimi
di IrKIrL
2
1
))((min iisimi
di IrKIrL
• Specular Kernel Ki as in [ Ward 92 ]• Specular Kernel Ki as in [ Ward 92 ]
NH
Yizhou Yu
Parametric BRDF Model [ Ward 92 ]Parametric BRDF Model [ Ward 92 ]Parametric BRDF Model [ Ward 92 ]Parametric BRDF Model [ Ward 92 ]
)(
Ksd
2
22
4
]/tan[exp
coscos
1)(
ri
K
yx
yx
ri
K
4
)]/sin/cos(tan[exp
coscos
1)(
22222
Isotropic Kernel
Anisotropic Kernel
NHi
r
Yizhou Yu
Recovering Diffuse and Specular Recovering Diffuse and Specular Reflectance under Mutual IlluminationReflectance under Mutual IlluminationRecovering Diffuse and Specular Recovering Diffuse and Specular Reflectance under Mutual IlluminationReflectance under Mutual Illumination
• Specular component of LPiAj is not known. ( unlike diffuse case, where LPiAj = LCkAj )
• Specular component of LPiAj is not known. ( unlike diffuse case, where LPiAj = LCkAj )
nj nj
APCAPsAPAPdPC jivjijijiivsd
KLFLL1 1
2
,,)( min
nj nj
APCAPsAPAPdPC jivjijijiivsd
KLFLL1 1
2
,,)( min
Cv
Ck
Aj
Pi
LPiAj
LCkAj
LCvPi
Yizhou Yu
Solution: iteratively estimate specular Solution: iteratively estimate specular component.component.Solution: iteratively estimate specular Solution: iteratively estimate specular component.component.
jikjkji APCACAP SLL jikjkji APCACAP SLL
• Initialize
• Repeat– Estimate BRDF parameters for each surface
– Update and
• Initialize
• Repeat– Estimate BRDF parameters for each surface
– Update and
0jik APCS 0jik APCS
jik APCS jik APCS jiAPL
Yizhou Yu
Estimation of SEstimation of SEstimation of SEstimation of S
• Estimate specular component of by ray-tracing using current guess of reflectance parameters.
• Similarly for
• Difference gives S
• Currently we use one-bounce approximation, but could be generalized.
• Estimate specular component of by ray-tracing using current guess of reflectance parameters.
• Similarly for
• Difference gives S
• Currently we use one-bounce approximation, but could be generalized. Cv
Ck
Aj
Pi
LPiAj
LCkAj
LCvPi
LPiAj
LCkAj
Yizhou Yu
Inverse Global IlluminationInverse Global IlluminationInverse Global IlluminationInverse Global Illumination
• Detect specular highlight blobs on the surfaces.• Choose a set of sample points inside and around each highlight area.• Build hierarchical links between sample points and facets in the
environment and use ray tracing to detect occlusion.• Assign to each facet one photograph and one average radiance value
captured at the camera position.• Assign zero to Delta_S at each hierarchical link.• For iter = 1 to n
– For each hierarchical link, use its Delta_S to update its radiance value.– For each surface having highlight areas, optimize its BRDF parameters.– For each hierarchical link, estimate its Delta_S with the new BRDF
parameters.• End
• Detect specular highlight blobs on the surfaces.• Choose a set of sample points inside and around each highlight area.• Build hierarchical links between sample points and facets in the
environment and use ray tracing to detect occlusion.• Assign to each facet one photograph and one average radiance value
captured at the camera position.• Assign zero to Delta_S at each hierarchical link.• For iter = 1 to n
– For each hierarchical link, use its Delta_S to update its radiance value.– For each surface having highlight areas, optimize its BRDF parameters.– For each hierarchical link, estimate its Delta_S with the new BRDF
parameters.• End
Yizhou Yu
Recovering Diffuse Albedo MapsRecovering Diffuse Albedo MapsRecovering Diffuse Albedo MapsRecovering Diffuse Albedo Maps
• Estimate specular component at each pixel from the recovered BRDF parameters using Monte Carlo ray-tracing.
• Subtract specular component to get the diffuse component of radiance, Ld(x).
• Gather irradiance, Ir(x), using Form-Factors over each surface.
•
• Combine from multiple photographs by robust weighted average.
• Estimate specular component at each pixel from the recovered BRDF parameters using Monte Carlo ray-tracing.
• Subtract specular component to get the diffuse component of radiance, Ld(x).
• Gather irradiance, Ir(x), using Form-Factors over each surface.
•
• Combine from multiple photographs by robust weighted average.
)(/)()( xIrxLx dd )(xd
Yizhou Yu
Results for a Simulated Cubical Room: Results for a Simulated Cubical Room: IIResults for a Simulated Cubical Room: Results for a Simulated Cubical Room: II
Diffuse Albedo
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
1 2 3 4 5 6
Real
Recovered
Yizhou Yu
Results for a Simulated Cubical Room: Results for a Simulated Cubical Room: IIIIResults for a Simulated Cubical Room: Results for a Simulated Cubical Room: IIII
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
1 2 3 4 5 6
Real
Recovered
Specular Roughness
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
Yizhou Yu
Diffuse Albedo Maps of Identical Posters Diffuse Albedo Maps of Identical Posters in Different Parts of the Roomin Different Parts of the RoomDiffuse Albedo Maps of Identical Posters Diffuse Albedo Maps of Identical Posters in Different Parts of the Roomin Different Parts of the Room
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
Yizhou Yu
ContributionsContributionsContributionsContributions• A digital camera is the only data acquisition
equipment used.
• Adopt an iterative procedure to obtain radiance distributions from specular surfaces.
• Exploit spatial coherence to recover specular reflectance models from one single photograph.
• Make use of multiple photographs to recover high-resolution diffuse albedo maps.
• A digital camera is the only data acquisition equipment used.
• Adopt an iterative procedure to obtain radiance distributions from specular surfaces.
• Exploit spatial coherence to recover specular reflectance models from one single photograph.
• Make use of multiple photographs to recover high-resolution diffuse albedo maps.
Yizhou Yu
OutlineOutlineOutlineOutline
• 1st Generation IBMR– Real-Time View-Dependent Projective Texture-
Mapping
• 2nd Generation IBMR– General Problem: closely positioned multiple objectsSimplified Situation: Isolated Objects
• 1st Generation IBMR– Real-Time View-Dependent Projective Texture-
Mapping
• 2nd Generation IBMR– General Problem: closely positioned multiple objectsSimplified Situation: Isolated Objects
Yizhou Yu
Modeling the IlluminationModeling the IlluminationModeling the IlluminationModeling the Illumination
• The sun– Its diameter extends 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.
– Modeled as a set of oriented Lambertian facets.
• The sun– Its diameter extends 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.
– Modeled as a set of oriented Lambertian facets.
Yizhou Yu
A Recovered Sky Radiance ModelA Recovered Sky Radiance ModelA Recovered Sky Radiance ModelA Recovered Sky Radiance Model
R,G,B channels
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
• Take photographs at several times of day
• Project pixels into regions and obtain the average radiance
• Partition the lower hemisphere into small regions
• Take photographs at several times of day
• Project pixels into regions and obtain the average radiance
Yizhou Yu
Predicting Novel Illumination from the Predicting Novel Illumination from the EnvironmentEnvironmentPredicting Novel Illumination from the Predicting Novel Illumination from the EnvironmentEnvironment
• Use photometric stereo to recover a Lambertian facet model for each region
• Use photometric stereo to recover a Lambertian facet model for each region
Synthetic Real
Yizhou Yu
Comparison with Real PhotographsComparison with Real PhotographsComparison with Real PhotographsComparison with Real Photographs
Synthetic Real
Yizhou Yu
Facial Skin Reflectance and WrinklesFacial Skin Reflectance and WrinklesFacial Skin Reflectance and WrinklesFacial Skin Reflectance and Wrinkles
Yizhou Yu
AcknowledgmentsAcknowledgmentsAcknowledgmentsAcknowledgments
• This is joint work with Jitendra Malik, Paul Debevec, George Borshukov, Tim Hawkins, C.J. Taylor and Hong Wu.
• Supported by ONR BMDO, the California MICRO program, Philips Corporation, Interval Research Corporation and Microsoft Graduate Fellowship.
• This is joint work with Jitendra Malik, Paul Debevec, George Borshukov, Tim Hawkins, C.J. Taylor and Hong Wu.
• Supported by ONR BMDO, the California MICRO program, Philips Corporation, Interval Research Corporation and Microsoft Graduate Fellowship.
Yizhou Yu
PublicationsPublicationsPublicationsPublications
• Y. Yu and H. Wu, A Rendering Equation for Specular Transfers and Its Integration into Global Illumination, Eurographics’97
• P. Debevec, Y. Yu and G. Borshukov, Efficient View-Dependent Image-Based Rendering with Projective Texture-Mapping, Eurographics Workshop on Rendering’98
• Y. Yu and J. Malik, Recovering Photometric Properties of Architectural Scenes from Photographs, Siggraph’98
• Y. Yu, P. Debevec, J. Malik and T. Hawkins, Inverse Global Illumination: Recovering Reflectance Models of Real Scenes from Photographs, Siggraph’99
• Y. Yu and H. Wu, A Rendering Equation for Specular Transfers and Its Integration into Global Illumination, Eurographics’97
• P. Debevec, Y. Yu and G. Borshukov, Efficient View-Dependent Image-Based Rendering with Projective Texture-Mapping, Eurographics Workshop on Rendering’98
• Y. Yu and J. Malik, Recovering Photometric Properties of Architectural Scenes from Photographs, Siggraph’98
• Y. Yu, P. Debevec, J. Malik and T. Hawkins, Inverse Global Illumination: Recovering Reflectance Models of Real Scenes from Photographs, Siggraph’99
http://www.cs.berkeley.edu/~yyz
Yizhou Yu
Optimization TechniqueOptimization TechniqueOptimization TechniqueOptimization Technique
Isotropic Kernel ( Golden Section Search )
Anisotropic Kernel ( Simplex Search )
2
1
))()()(
( min iisimi
di IrKIrL
2
1
))()()(
( min iisimi
di IrKIrL
2xx
1
x
,,)),,(),,(
),,(( min
xiyiysi
mi
ydi IrKIrL
y
2xx
1
x
,,)),,(),,(
),,(( min
xiyiysi
mi
ydi IrKIrL
y
Yizhou Yu
Efficiency ConsiderationEfficiency ConsiderationEfficiency ConsiderationEfficiency Consideration
Indirect illumination is computed only at a sparse set of points and then linearly interpolated.
Yizhou Yu
Recovering Sky Radiance ModelRecovering Sky Radiance ModelRecovering Sky Radiance ModelRecovering Sky Radiance Model
) cos ) exp( 1 ))( /cos exp( 1 Lvz( 2 edcba f ) cos ) exp( 1 ))( /cos exp( 1 Lvz( 2 edcba f
• Recover a set of parameters for each color channel– Take photographs for parts of the sky
– Use Levenberg-Marquardt algorithm to fit data
• Recover a set of parameters for each color channel– Take photographs for parts of the sky
– Use Levenberg-Marquardt algorithm to fit data
sun
zenithSky element
Lvz, a, b, c, d, e, f
based on [Perez 93]
Yizhou Yu
A Local Facet Model for the EnvironmentA Local Facet Model for the EnvironmentA Local Facet Model for the EnvironmentA Local Facet Model for the Environment
• Recover a distinct model for each environment region– Obtain environment radiance maps.
– Set up over-determined systems as in photometric stereo and ignore inter-reflections.
– Solve for
• Recover a distinct model for each environment region– Obtain environment radiance maps.
– Set up over-determined systems as in photometric stereo and ignore inter-reflections.
– Solve for
otherwise.
,0n if
,
),n(
envsun
skysky
envsunsunsun
skysky
env
l
E
lEEI
otherwise.
,0n if
,
),n(
envsun
skysky
envsunsunsun
skysky
env
l
E
lEEI
envsunsky n ,,
nenvlsun
Yizhou Yu
Diffuse Pseudo-Albedo MapsDiffuse Pseudo-Albedo MapsDiffuse Pseudo-Albedo MapsDiffuse Pseudo-Albedo Maps
For the sky For the sun
Yizhou Yu
A Hybrid Visibility AlgorithmA Hybrid Visibility AlgorithmA Hybrid Visibility AlgorithmA Hybrid Visibility Algorithm
• Occlusion testing in image-space using Z-buffer hardware– Render polygons using their identifiers as their colors
– Retrieve occluding polygons’ ids from color buffer
• Object-space shallow clipping to generate fewer polygons
• Occlusion testing in image-space using Z-buffer hardware– Render polygons using their identifiers as their colors
– Retrieve occluding polygons’ ids from color buffer
• Object-space shallow clipping to generate fewer polygons
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]
give a method to recover this nonlinear mapping.
• Pixel brightness value is a nonlinear function of radiance.– Debevec & Malik[Siggraph’97]
give a method to recover this nonlinear mapping.
RadianceRadiance
IntensitySaturation
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