Modeling Anisotropic Surface Reflectance with Example-Based Microfacet Synthesis

1. 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

2. Surface Reflectance satin metal wood

3. Anisotropic Surface Reflectance isotropic anisotropic

4. Our Goal modeling spatially-varying anisotropic reflectance

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

6. 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

7. Related Work I • parametric BRDF models • compact representation • easy acquisition and fitting • lack realistic details groundtruth parametric model [Ward 92]

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

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

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

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

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

13. 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

14. Solution: Exploit Spatial Redundancy • find surface points with similar but differently rotatedNDFs material sample partial NDF at each surface point

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

16. Comparison ground truth our model isotropic Ward model anisotropic Ward model

17. Overall Pipeline • BRDF Slice Capture • Partial NDF Recovery • Microfacet Synthesis

18. Overall Pipeline • BRDF Slice Capture • Partial NDF Recovery • Microfacet Synthesis

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

20. Capturing Process

21. Overall Pipeline • BRDF Slice Capture • Partial NDF Recovery • Microfacet Synthesis

22. NDF Recovery • invert the microfacet BRDF model MeasuredBRDF Unknown Unknown NDF Shadow Term   , [Ashikhmin et al 00]

23. 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.

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

25. Partial NDF Recovery (con’t) • minimize the bias • isotropically constrain shadow term in each iteration after constraint before constraint

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

27. Overall Pipeline • Capture BRDF slice • Partial NDF Recovery • Microfacet Synthesis

28. Microfacet Synthesis partial NDF to complete completed NDF Merged partial NDFs

29. 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)

30. 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 )

31. 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

32. 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

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

34. Validation Result

35. 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

36. Rendering Result: Satin

37. Rendering Result: Wood

38. Rendering Result: Brushed Metal

39. 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

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

41. 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

42. Thank you!