Inverse global illumination recovering reflectance models of real scenes from photographs
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Inverse Global Illumination: Recovering Reflectance Models of Real Scenes from Photographs. Computer Science Division University of California at Berkeley. Yizhou Yu, Paul Debevec, Jitendra Malik & Tim Hawkins. Image-based Modeling and Rendering.

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Inverse Global Illumination: Recovering Reflectance Models of Real Scenes from Photographs

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Inverse global illumination recovering reflectance models of real scenes from photographs

Inverse Global Illumination:Recovering Reflectance Models of Real Scenes from Photographs

Computer Science Division

University of California at Berkeley

Yizhou Yu, Paul Debevec, Jitendra Malik & Tim Hawkins


Image based modeling and rendering

Image-based Modeling and Rendering

  • 1st Generation---- vary viewpoint but not lighting

    • Recover geometry ( explicit or implicit )

    • Acquire photographs

    • Facade, Plenoptic Modeling, View Morphing, Lumigraph, Layered Depth Images, (Light Field Rendering) etc.


Image based modeling and rendering1

Image-based Modeling and Rendering

  • Photographs arenot Reflectance Maps !

  • 2nd Generation---- vary viewpoint and lighting for non-diffuse scenes

    • Recover geometry

    • Recover reflectance properties

    • Render using light transport simulation

Illumination

Radiance

Reflectance


Previous work

Previous Work

  • BRDF Measurement in the Laboratory

    • [ Ward 92 ], [Dana, Ginneken, Nayar & Koenderink 97]

  • Isolated Objects under Direct Illumination

    • [ Sato, Wheeler & Ikeuchi 97 ]

  • Isolated Objects under General Illumination

    • [ Yu & Malik 98], [ Debevec 98]


The problem

The Problem

  • General case of multiple objects under mutual illumination has not been studied.


Global illumination

Global Illumination

Reflectance

Properties

Radiance

Images

Geometry

Illumination


Inverse global illumination

Inverse Global Illumination

Reflectance

Properties

Radiance

Images

Geometry

Illumination


Input radiance images

Input Radiance Images

[ Debevec & Malik 97]

http://www.cs.berkeley.edu/~debevec/HDR


In detail

In Detail ...


Geometry and camera positions

Geometry and Camera Positions


Light sources

Light Sources


Synthesized images

Synthesized Images

Original Lighting

Novel Lighting


Outline

Outline

  • Diffuse surfaces under mutual illumination

  • Non-diffuse surfaces under direct illumination

  • Non-diffuse surfaces under mutual illumination


Lambertian surfaces under mutual illumination

Source

Target

Lambertian Surfaces under Mutual Illumination

  • Bi, Bj, Ei measured

  • Form-factor Fij known

  • Solve for diffuse albedo


Parametric brdf model ward 92

Parametric BRDF Model [ Ward 92 ]

N

H

Isotropic Kernel

( 3 parameters)

Anisotropic Kernel

( 5 parameters)


Non diffuse surfaces under direct illumination

Non-diffuse Surfaces underDirect Illumination

N

P2

H

P1

P2

P1


Non diffuse surfaces under mutual illumination

Non-diffuse Surfaces under Mutual Illumination

  • LPiAj is not known. ( unlike diffuse case, where LPiAj = LCkAj )

Source

Aj

LPiAj

LCkAj

Pi

Target

LCvPi

Ck

Cv


Solution iteratively estimate specular component

Solution: iteratively estimate specular component.

  • Initialize

  • Repeat

    • Estimate BRDF parameters for each surface

    • Update and


Estimation of specular difference s

Estimation of Specular Difference S

  • Estimate specular component of by Monte Carlo ray-tracing using current guess of reflectance parameters.

  • Similarly for

  • Difference gives S

Aj

LPiAj

LPiAj

Pi

LCkAj

Ck

LCkAj

LCvPi

Cv


Recovering diffuse albedo maps

Recovering Diffuse Albedo Maps

  • Specular properties assumed uniform across each surface, but diffuse albedo allowed to vary.


Results

Results

  • A simulated cubical room


Results for the simulated case

Results for the Simulated Case

Diffuse Albedo

Specular Roughness


Results1

Results

  • A real conference room


Real vs synthetic for original lighting

Real vs. Synthetic for Original Lighting

Real

Synthetic


Diffuse albedo maps of identical posters in different positions

Diffuse Albedo Maps of Identical Posters in Different Positions

Poster A

Poster B

Poster C


Inverting color bleed

Inverting Color Bleed

Input Photograph

Output Albedo Map


Real vs synthetic for novel lighting

Real vs. Synthetic for Novel Lighting

Real

Synthetic


Video

Video


Acknowledgments

Acknowledgments

  • Thanks to David Culler and the Berkeley NOW project, Tal Garfinkel, Gregory Ward Larson, Carlo Sequin.

  • Supported by ONR BMDO, the California MICRO program, Philips Corporation, Interval Research Corporation and Microsoft Graduate Fellowship.


Conclusions

Conclusions

  • A digital camera can undertake all the data acquisition tasks involved.

  • Both specular and high resolution diffuse reflectance properties can be recovered from photographs.

  • Reflectance recovery can re-render non-diffuse real scenes under novel illumination as well as from novel viewpoints.


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