Recovering BRDF Models for Architectural Scenes

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 - Static scene configuration - Fixed lighting condition

Image-based Modeling and Rendering • Vary lighting • Recover reflectance properties for multiple objects in a mutual illumination environment 5:00am 6:00am 7:00am 10:00am

The Problem • 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

Global Illumination Reflectance Properties Light Transport Images Geometry Light Sources

Inverse Global Illumination Reflectance Properties Images Geometry Light Sources

Input Images Every surface should be covered by at least one photograph A specular highlight should be captured for every specular surface

Camera Radiance Response Curve • Pixel brightness value is a nonlinear function of radiance. • Debevec & Malik[Siggraph’97] gives a method to recover this nonlinear mapping. Intensity Saturation Radiance

In Detail ...

Recovered Geometry and Camera Pose

Light Sources Spherical light sources are easier to model Light source intensity can be calibrated from dynamic range images

Synthesized Images Original Lighting Novel Lighting

A Comparison Hand-crafted Recovered

Outline • Diffuse surfaces under mutual illumination • Non-diffuse surfaces under direct illumination • Non-diffuse 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 ] N H Isotropic Kernel ( 3 parameters) Anisotropic Kernel ( 5 parameters)

Non-diffuse Surfaces underDirect Illumination N P2 H P1 P2 P1

Non-diffuse Surfaces under Mutual Illumination • Problem: LPiAj is not known. ( unlike diffuse case, where LPiAj = LCkAj ) • Solution: iterative estimation Source Aj LPiAj LCkAj Pi Target LCvPi Ck Cv

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 • Specular properties assumed uniform across each surface, but diffuse albedo allowed to vary. • Subtract specular component • Recover pointwise diffuse albedo

Results • A simulated cubical room

Results for the Simulated Case Diffuse Albedo Specular Roughness

Results • A real conference room

Real vs. Synthetic for Original Lighting Real Synthetic

Diffuse Albedo Maps of Identical Posters in Different Positions Poster A Poster B Poster C

Inverting Color Bleed Input Photograph Output Albedo Map

Real vs. Synthetic for Novel Lighting Real Synthetic

Modeling 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.

A Recovered Sky Radiance Model

Coarse-grain Environment Radiance Maps • Partition the lower hemisphere into small regions • Project pixels into regions and obtain the average radiance

Comparison with Real Photographs Synthetic Real

Inverse 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

Recovering BRDF Models for Architectural Scenes