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From Global Illumination To Inverse Global Illumination

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From Global Illumination To Inverse Global Illumination. Yizhou Yu Computer Science Division University of California at Berkeley. Publications. http://www.cs.berkeley.edu/~yyz. - PowerPoint PPT Presentation

Text of From Global Illumination To Inverse Global Illumination

Ongoing Work: Recovering Photometric PropertiesYizhou Yu
Publications
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
Vision
A polygonal mesh and/or a set of curved surface patches
Yizhou Yu
Light Sources
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Light Transport
Yizhou Yu
With a mirror
Without the mirror
to calculate illumination from area sources via curved ideal specular
reflectors.
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
Recover geometry ( explicit or implicit )
Acquire texture maps
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Yizhou Yu
Synthetic Renderings
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Yizhou Yu
The Problem
Need to factorize images into lighting and reflectance maps
Illumination
Radiance
Reflectance
Recover geometry
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1st Generation IBMR
Simplified Situation: Isolated Objects
Simplified Situation: Isolated Objects
VDTM was originally from Façade [ Debevec, Taylor & Malik, Siggraph’96].
Software implementation
20 frames per second on SGI RealityEngine
60 frames per second on SGI Onyx2 InfiniteReality
Yizhou Yu
Yizhou Yu
Simplified Situation: Isolated Objects
[ 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.
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Re-render the scene
Synthesized Images of the Room under Original and Novel Lighting
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IGI Outline
IGI for general surfaces
Computing diffuse albedo maps
Bi, Bj, Ei measured using HDR photographs
Fij known because geometry is known
Solve for diffuse albedo
Specular Kernel Ki as in [ Ward 92 ]
N
H
Isotropic Kernel
Anisotropic Kernel
Recovering Diffuse and Specular Reflectance under Mutual Illumination
Specular component of LPiAj is not known. ( unlike diffuse case, where LPiAj = LCkAj )
Cv
Ck
Aj
Pi
LPiAj
LCkAj
LCvPi
Initialize
Repeat
Update and
Yizhou Yu
Estimation of S
Estimate specular component of by ray-tracing using current guess of reflectance parameters.
Similarly for
Cv
Ck
Aj
Pi
LPiAj
LCkAj
LCvPi
LPiAj
LCkAj
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
Recovering 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.
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Diffuse Albedo
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Specular Roughness
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Diffuse Albedo Maps of Identical Posters in Different Parts of the Room
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Yizhou Yu
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.
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Simplified Situation: Isolated Objects
The sky
The surrounding environment
May contribute more light than the sky on shaded side.
Modeled as a set of oriented Lambertian facets.
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R,G,B channels
Project pixels into regions and obtain the average radiance
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Predicting Novel Illumination from the Environment
Use photometric stereo to recover a Lambertian facet model for each region
Synthetic
Real
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Acknowledgments
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
Publications
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
Yizhou Yu
Optimization Technique
Anisotropic Kernel ( Simplex Search )
set of points and then linearly interpolated.
Yizhou Yu
Take photographs for parts of the sky
Use Levenberg-Marquardt algorithm to fit data
sun
zenith
based on [Perez 93]
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
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
Pixel brightness value is a nonlinear function of radiance.
Debevec & Malik[Siggraph’97] give a method to recover this nonlinear mapping.
Radiance
Radiance
Intensity
Saturation

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