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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

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Surface Reflectance satin metal wood

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Anisotropic Surface Reflectance anisotropicisotropic

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Our Goal modeling spatially-varying anisotropic reflectance

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Surface Reflectance in CG 4D BRDF ρ(o,i) – Bidirectional Reflectance Distribution Function – how much light reflected wrt in/out directions o i

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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: SVBRDF ρ(x,o,i) – BRDF at each surface point x

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Related Work I parametric BRDF models – compact representation – easy acquisition and fitting – lack realistic details ground truth parametric model [Ward 92]

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Related Work II tabulated SVBRDF – realistic – large data set – difficult to capture lengthy process expensive hardware image registration light dome [Gu et al 06]

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Related Work II tabulated SVBRDF – realistic – large data set – difficult to capture lengthy process expensive hardware image registration light dome [Gu et al 06]

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Microfacet BRDF Model [Cook & Torrance 82] surface modeled by tiny mirror facets

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Microfacet BRDF Model surface modeled by tiny mirror facets shadow term fresnel term normal distribution [Cook & Torrance 82]

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Microfacet BRDF Model based on Normal Distribution Function (NDF) – NDF D is 2D function of the half-way vector h – dominates surface appearance

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Challenge: Partial Domains samples from a single viewing direction i – cover only a sub-region h Ω of NDF – How to obtain the full NDF? partial region ? partial NDF complete NDF

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Solution: Exploit Spatial Redundancy similar but differently rotated NDFs find surface points with similar but differently rotated NDFs material sample partial NDF at each surface point

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Example-Based Microfacet Synthesis Align + + = partial NDF to complete completed NDF rotated partial NDFs partial NDFs from other surface points

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ComparisonComparison isotropic Ward model anisotropic Ward model ground truth our model

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Overall Pipeline BRDF Slice Capture Partial NDF Recovery Microfacet Synthesis

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Overall Pipeline BRDF Slice Capture Partial NDF Recovery Microfacet Synthesis

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

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Capturing Process

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Overall Pipeline BRDF Slice Capture Partial NDF Recovery Microfacet Synthesis

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NDF Recovery invert the microfacet BRDF model [Ashikhmin et al 00], Unknown NDF Shadow Term Measured BRDF

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NDF Recovery (cont) Ngan et al 05 iterative approach [Ngan et al 05] – solve for NDF, then shadow term – works for complete 4D BRDF data [Ashikhmin et al 00], Ngan et al 05 [Ngan et al 05]

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Partial NDF Recovery biased result on incomplete BRDF data ground truth NDF shadow term [Ngan et al. 05] NDF shadow term

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Partial NDF Recovery (cont) minimize the bias – isotropically constrain shadow term in each iteration before constraint after constraint

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Recovered Partial NDF ground truth [Ngan et al. 05] our result

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Overall Pipeline Capture BRDF slice Partial NDF Recovery Microfacet Synthesis

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partial NDF to complete Merged partial NDFs completed NDF

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Microfacet Synthesis (cont) 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 5×10 11 (N 640k, M 1k )

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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 5×10 11 ( N 640k ) Synthesis Acceleration Clustering [Matusik et al 03] – complete representative NDFs only (1% of full set) N' 2 * M 5×10 7 ( N' 6.4k )

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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 5×10 11 ( N 640k ) Synthesis Acceleration 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 5×10 7 ( N' 6.4k ) N'* log(N'* M) 5×10 5

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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 Core TM 2 Quad 2.13GHz CPU and 4GB memory

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Model Validation full SVBRDF dataset [Lawrence et al. 06] – data from one view for modeling – data from other views for validation

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Validation Result

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LimitationsLimitations visual modeling, not physical accuracy single-bounce microfacet model – retro-reflection not handled spatial redundancy of rotated NDFs – easy fix by rotating the sample

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Rendering Result: Satin

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Rendering Result: Wood

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Rendering Result: Brushed Metal

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ConclusionsConclusions 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

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Future Work performance optimization – capturing and data processing extension to non-flat objects extension to multiple light bounce

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AcknowledgementsAcknowledgements 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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Thank you!

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