Inverse global illumination recovering reflectance models of real scenes from photographs
Download
1 / 30

Inverse Global Illumination: Recovering Reflectance Models of Real Scenes from Photographs - PowerPoint PPT Presentation


  • 83 Views
  • Uploaded on

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.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about ' Inverse Global Illumination: Recovering Reflectance Models of Real Scenes from Photographs' - libby


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
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





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



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




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.


ad