Modeling anisotropic surface reflectance with example based microfacet synthesis
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Modeling Anisotropic Surface Reflectance with Example-Based Microfacet Synthesis. Jiaping Wang 1 , Shuang Zhao 2 , Xin Tong 1 John Snyder 3 , Baining Guo 1 Microsoft Research Asia 1 Shanghai Jiao Tong University 2 Microsoft Research 3. Surface Reflectance.

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Modeling anisotropic surface reflectance with example based microfacet synthesis l.jpg

Modeling Anisotropic Surface Reflectance with Example-Based Microfacet Synthesis

Jiaping Wang1, Shuang Zhao2, Xin Tong1John Snyder3, Baining Guo1

Microsoft Research Asia1Shanghai Jiao Tong University2Microsoft Research3


Surface reflectance l.jpg
Surface Reflectance

satin metal wood


Anisotropic surface reflectance l.jpg
Anisotropic Surface Reflectance

isotropic

anisotropic


Our goal l.jpg
Our Goal

modeling spatially-varying anisotropic reflectance


Surface reflectance in cg l.jpg
Surface Reflectance in CG

  • 4D BRDF ρ(o,i)

    • Bidirectional Reflectance Distribution Function

    • how much light reflected wrt in/out directions

o

i


Surface reflectance in cg6 l.jpg
Surface Reflectance in CG

  • 4D BRDF ρ(o,i)

    • Bidirectional Reflectance Distribution Function

    • how much light reflected wrt in/out directions

    • 6D Spatially-Varying BRDF: SVBRDFρ(x,o,i)

    • BRDF at each surface point x


Related work i l.jpg
Related Work I

  • parametric BRDF models

    • compact representation

    • easy acquisition and fitting

    • lack realistic details

groundtruth

parametric model [Ward 92]


Related work ii l.jpg
Related Work II

  • tabulated SVBRDF

    • realistic

    • large data set

    • difficult to capture

      • lengthy process

      • expensive hardware

      • image registration

light dome [Gu et al 06]


Related work ii9 l.jpg
Related Work II

  • tabulated SVBRDF

    • realistic

    • large data set

    • difficult to capture

      • lengthy process

      • expensive hardware

      • image registration

light dome [Gu et al 06]


Microfacet brdf model l.jpg
Microfacet BRDF Model

  • surface modeled by tiny mirror facets

[Cook & Torrance 82]


Microfacet brdf model11 l.jpg
Microfacet BRDF Model

  • surface modeled by tiny mirror facets

[Cook & Torrance 82]

fresnel term

normal distribution

shadow term


Microfacet brdf model12 l.jpg
Microfacet BRDF Model

  • based on Normal Distribution Function (NDF)

    • NDF D is 2D function of the half-way vector h

    • dominates surface appearance


Challenge partial domains l.jpg
Challenge: Partial Domains

  • samples from a single viewing direction i

    • cover only a sub-regionh Ωof NDF

    • How to obtain the full NDF?

?

partial NDF complete NDF

partial region


Solution exploit spatial redundancy l.jpg
Solution: Exploit Spatial Redundancy

  • find surface points with similar but differently rotatedNDFs

material sample partial NDF at each surface point


Example based microfacet synthesis l.jpg
Example-Based Microfacet Synthesis

partial NDFs from other surface points

Align

+

+

=

partial NDF

to complete

rotated partial NDFs

completed NDF


Comparison l.jpg
Comparison

ground truth

our model

isotropic Ward model

anisotropic Ward model


Overall pipeline l.jpg
Overall Pipeline

  • BRDF Slice Capture

  • Partial NDF Recovery

  • Microfacet Synthesis


Overall pipeline18 l.jpg
Overall Pipeline

  • BRDF Slice Capture

  • Partial NDF Recovery

  • Microfacet Synthesis


Device setup l.jpg
Device Setup

Camera-LED system, based on [Gardner et al 03]



Overall pipeline21 l.jpg
Overall Pipeline

  • BRDF Slice Capture

  • Partial NDF Recovery

  • Microfacet Synthesis


Ndf recovery l.jpg
NDF Recovery

  • invert the microfacet BRDF model

MeasuredBRDF

Unknown Unknown

NDF Shadow Term

,

[Ashikhmin et al 00]


Ndf recovery con t l.jpg
NDF Recovery (con’t)

  • iterative approach[Ngan et al 05]

    • solve for NDF, then shadow term

    • works for complete 4D BRDF data

[Ngan et al 05]

1.

,

[Ashikhmin et al 00]

2.


Partial ndf recovery l.jpg
Partial NDF Recovery

  • biased result on incomplete BRDF data

[Ngan et al. 05]

ground truth

NDF

shadow term

shadow term

NDF


Partial ndf recovery con t l.jpg
Partial NDF Recovery (con’t)

  • minimize the bias

    • isotropically constrain shadow term in each iteration

after constraint

before constraint


Recovered partial ndf l.jpg
Recovered Partial NDF

[Ngan et al. 05]

ground truth

our result


Overall pipeline27 l.jpg
Overall Pipeline

  • Capture BRDF slice

  • Partial NDF Recovery

  • Microfacet Synthesis


Microfacet synthesis l.jpg
Microfacet Synthesis

partial NDF

to complete

completed NDF

Merged partial NDFs


Microfacet synthesis con t l.jpg
Microfacet Synthesis (con’t)

  • straightforward implementation:For N NDFs at each surface point Match against (N-1)NDFs at other points In M rotation angles for alignment

  • number of rotations/comparisons:

    N 2*M ≈ 5×1011(N ≈ 640k, M ≈ 1k)


Synthesis acceleration l.jpg
Synthesis Acceleration

  • a straightforward implementation:For N NDFs in each surface point Match with (N -1)NDFs in other location In M rotation angles for alignment

  • times of spherical function rotation and comparison

    N 2* M ≈ 5×1011( N ≈ 640k )

  • Clustering [Matusik et al 03]

    • complete representative NDFs only (1% of full set)

N'2* M ≈ 5×107( N' ≈ 6.4k )


Synthesis acceleration31 l.jpg
Synthesis Acceleration

  • a straightforward implementation:For N NDFs in each surface point Match with (N -1)NDFs in other location In M rotation angles for alignment

  • times of spherical function rotation and comparison

    N 2* M ≈ 5×1011( N ≈ 640k)

  • Clustering[Matusik et al 03]

    • complete representative NDFs only (1% of full set)

  • Search Pruning

    • precompute all rotated candidates

    • prune via hierarchical searching

  • N'2* M ≈ 5×107( N' ≈ 6.4k)

    N'* log(N'* M) ≈ 5×105


    Performance summary l.jpg
    Performance Summary

    • 5-10 hours for BRDF slice acquisition in HDR

      • 1 Hour for acquisition in LDR

    • 2-4 hours for image processing

    • 2-3 hours for partial NDF recovery

    • 2-4 hours for accelerated microfacet synthesis

    On a PC with Intel CoreTM2 Quad 2.13GHz CPU and 4GB memory


    Model validation l.jpg
    Model Validation

    • full SVBRDF dataset [Lawrence et al. 06]

      • data from one view for modeling

      • data from other views for validation



    Limitations l.jpg
    Limitations

    • visual modeling, not physical accuracy

    • single-bounce microfacet model

      • retro-reflection not handled

    • spatial redundancy of rotated NDFs

      • easy fix by rotating the sample





    Conclusions l.jpg
    Conclusions

    • model surface reflectance via microfacet synthesis

      • general and compact representation

      • high resolution (spatial & angular), realistic result

      • easier acquisition:

        • single-view capture

        • cheap device

        • shorter capturing time


    Future work l.jpg
    Future Work

    • performance optimization

      • capturing and data processing

    • extension to non-flat objects

    • extension to multiple light bounce


    Acknowledgements l.jpg
    Acknowledgements

    • Le Ma for electronics of the LED array

    • Qiang Dai for capturing device setup

    • Steve Lin, Dong Xu for valuable discussions

    • Paul Debevec for HDR imagery

    • Anonymous reviewers for their helpful suggestions and comments



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