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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

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**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**satin metal wood**Anisotropic Surface Reflectance**isotropic anisotropic**Our Goal**modeling spatially-varying anisotropic reflectance**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 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**• parametric BRDF models • compact representation • easy acquisition and fitting • lack realistic details groundtruth parametric model [Ward 92]**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 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**• surface modeled by tiny mirror facets [Cook & Torrance 82]**Microfacet BRDF Model**• surface modeled by tiny mirror facets [Cook & Torrance 82] fresnel term normal distribution shadow term**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**• 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**• find surface points with similar but differently rotatedNDFs material sample partial NDF at each surface point**Example-Based Microfacet Synthesis**partial NDFs from other surface points Align + + = partial NDF to complete rotated partial NDFs completed NDF**Comparison**ground truth our model isotropic Ward model anisotropic Ward model**Overall Pipeline**• BRDF Slice Capture • Partial NDF Recovery • Microfacet Synthesis**Overall Pipeline**• BRDF Slice Capture • Partial NDF Recovery • Microfacet Synthesis**Device Setup**Camera-LED system, based on [Gardner et al 03]**Overall Pipeline**• BRDF Slice Capture • Partial NDF Recovery • Microfacet Synthesis**NDF Recovery**• invert the microfacet BRDF model MeasuredBRDF Unknown Unknown NDF Shadow Term , [Ashikhmin et al 00]**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**• biased result on incomplete BRDF data [Ngan et al. 05] ground truth NDF shadow term shadow term NDF**Partial NDF Recovery (con’t)**• minimize the bias • isotropically constrain shadow term in each iteration after constraint before constraint**Recovered Partial NDF**[Ngan et al. 05] ground truth our result**Overall Pipeline**• Capture BRDF slice • Partial NDF Recovery • Microfacet Synthesis**Microfacet Synthesis**partial NDF to complete completed NDF Merged partial NDFs**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**• 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 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**• 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**• full SVBRDF dataset [Lawrence et al. 06] • data from one view for modeling • data from other views for validation**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**• 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**• performance optimization • capturing and data processing • extension to non-flat objects • extension to multiple light bounce**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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