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Frequency Domain Normal Map Filtering. Charles Han Bo Sun Ravi Ramamoorthi Eitan Grinspun Columbia University. Normal Mapping. (Blinn 78). Normal Mapping. (Blinn 78) Specify surface normals. Normal Mapping. ?. A Problem…. Multiple normals per pixel Undersampling Filtering needed.
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Frequency DomainNormal Map Filtering Charles Han Bo Sun Ravi Ramamoorthi Eitan Grinspun Columbia University
Normal Mapping • (Blinn 78)
Normal Mapping • (Blinn 78) • Specify surface normals
? A Problem… • Multiple normals per pixel • Undersampling • Filtering needed
Supersampling • Correct results • Too slow
MIP mapping • Pre-filter • Normals do not interpolate linearly • Blurring of details
Comparison supersampled MIP mapped
a single vector is not enough how do we represent multiple surface normals? Representation
no general solution Previous Work • Gaussian Distributions • (Olano and North 97) • (Schilling 97) • (Toksvig 05) • Mixture Models • (Fournier 92) • (Tan, et.al. 05) 3D Gaussian 2D covariance matrix 1D Gaussian mixture of Phong lobes mixture of 2D Gaussians
Our Contributions • Theoretical Framework • Normal Distribution Function (NDF) • Linear averaging for filtering • Convolution for rendering • Unifies previous works • New normal map representations • Spherical harmonics • von Mises-Fisher Distribution • Simple, efficient rendering algorithms
Normal Distribution Function (NDF) • Describes normals within region • Defined on the unit sphere • Integrates to one • Extended Gaussian Image (Horn 84)
Normal Distribution Function normal map NDF
Normal Distribution Function normal map NDF
Normal Distribution Function normal map NDF
Normal Distribution Function normal map NDF
NDF Filtering normal map
NDF Filtering normal map
NDF Filtering • NDF averaging is linear • Store NDFs in MIP map
normal, lights BRDF pixel value Rendering Radially symmetric BRDFs • Lambertian: • Blinn-Phong: • Torrance-Sparrow: • Factored: rendered image
samples Supersampling supersampled image Effective BRDF
samples NDF, Effective BRDF
Spherical Convolution • Form studied in lighting • (Basri and Jacobs 01) • (Ramamoorthi and Hanrahan 01) • Effective BRDF = convolution of NDF & BRDF
Spherical Convolution BRDF NDF Effective BRDF
NDF representations Previous Work • Gaussian Distributions • Olano and North (97) • Schilling (97) • Toksvig (05) • Mixture Models • Fournier (92) • Tan, et.al. (05) • Our Work 3D Gaussian 2D covariance matrix 1D Gaussian mixture of Phong lobes mixture of 2D Gaussians spherical harmonics von Mises-Fisher mixtures
Spherical Harmonics • Analogous to Fourier basis • Convolution formula:
BRDF Coefficients • Arbitrary BRDFs • Cheaply represented • Analytic: compute in shader • Measured: store on GPU • Easily changed at runtime
NDF Coefficients • Store in MIP mapped textures • Finest-level NDFs are delta functions, so: • Use standard linear filtering
Effective BRDF Coefficients • Product of NDF, BRDF coefficients • Proceed as usual
Limitations • Storage cost of NDF • One texture per coefficient • O( ) cost • Limited to low frequencies
more concentrated less concentrated von Mises-Fisher Distribution (vMF) • Spherical analogue to Gaussian • Desirable properties • Spherical domain • Distribution function • Radially symmetric
2 1 3 4 5 6 Mixtures of vMFs NDF number of vMFs
data model NDF vMF Mixture Expectation Maximization (EM) • From machine learning • Used in (Tan et.al. 05) • Fit model parameters to data EM
NDF rendered image Rendering • Convolution • Spherical harmonic coefficients • Analytic convolution formula • Extensions to EM • Aligned lobes (Tan et.al. 05) • Colored lobes
Conclusion • Summary • Theoretical Framework • New NDF representations • Practical rendering algorithms • Future directions • Offline rendering, PRT • Further applications for vMFs • Shadows, parallax, inter-reflections, etc.
Thanks! Tony Jebara, Aner Ben-Artzi, Peter Belhumeur, Pat Hanrahan, Shree Nayar, Evgueni Parilov, Makiko Yasui, Denis Zorin, and nVidia. http://www.cs.columbia.edu/cg/normalmap