Approximate Reflectance Profiles for Efficient Subsurface ... · Approximate Reflectance Profiles...
Transcript of Approximate Reflectance Profiles for Efficient Subsurface ... · Approximate Reflectance Profiles...
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Approximate Reflectance Profiles for Efficient Subsurface Scattering
Per Christensen
Pixar Animation Studios
SIGGRAPH 2015, Los Angeles
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Goal: subsurface scattering, fast+simple
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Overview
• Simple subsurface scattering model
• New parameterization allows comparison with physically-based models
• Matches Monte Carlo references very well -- better than physically-based models
• Useful for ray-traced (and point-based) subsurface scattering
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Advantages
• Faster evaluation, simpler code
• Built-in single-scattering term
• No need for numerical inversion of user-friendly parameters (surface albedo and scattering length) to physical parameters (volume scattering and absorption coeffs)
• Bonus: simple cdf for importance sampling
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Inspiration: Schlick’s Fresnel approx.
• Physics: Fresnel reflection formula -- reflection is average of parallel and perpendicular polarized: R(theta) = (R + R ) / 2p s
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Inspiration: Schlick’s Fresnel approx.
• Physics: Fresnel reflection formula
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Inspiration: Schlick’s Fresnel approx.
• [Schlick94]: Simple approximation as polynomial
• No visual difference
• We want similar simple approximation for subsurface scattering!
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Outline of talk
• Subsurface scattering
• Physically-based subsurface scattering models
• Burley’s approximate model
• My reparameterization
• Results
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Monte Carlo simulation
• Most general method: brute-force Monte Carlo
• But: very slow!
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BSSRDF
• Function that describes how light enters an object, bounces around, then leaves: BSSRDF (bidirectional surface scattering reflectance distribution function) S
• Often simplified as:
reflectance profile
Fresnel transmission terms
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Reflectance profiles: reference
• Brute-force Monte Carlo simulation
• Reflectance profile R(r); A = surface albedo
linear y axis log y axis
0.001
0.01
0.1
1 2 3 4 5 6 7 8
r R(r)
r
Reflectance profiles (Monte Carlo)A = 0.90A = 0.80A = 0.70A = 0.60A = 0.50A = 0.40A = 0.30A = 0.20A = 0.10
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0 1 2 3 4 5
r R(r)
r
Reflectance profiles (Monte Carlo)A = 0.90A = 0.80A = 0.70A = 0.60A = 0.50A = 0.40A = 0.30A = 0.20A = 0.10
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Physically-based reflectance profiles
• Dipole diffusion [Jensen01,02]–simple, fast, widely used; but: blurry “waxy” look
• Better dipole diffusion [d’Eon12]
• Directional dipole diffusion [Frisvad14]–can handle oblique incident angles
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Physically-based reflectance profiles
• Formulas:
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Physically-based reflectance profiles
• Formulas:
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Physically-based reflectance profiles
• Formulas:
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Physically-based reflectance profiles
• Formulas:
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Physically-based reflectance profiles
• Formulas:
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Physically-based reflectance profiles
• Formulas:
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Physically-based reflectance profiles
• Quantized diffusion [d’Eon11]–Improved diffusion theory
–Extended source term (instead of just two points)
–Sharper edges -- not “waxy” looking
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Physically-based reflectance profiles
• More formulas:
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Physically-based reflectance profiles
• More formulas:
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Physically-based reflectance profiles
• More formulas:
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Physically-based reflectance profiles
• More formulas:
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Physically-based reflectance profiles
• More formulas:
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Physically-based reflectance profiles
• More formulas:
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Physically-based reflectance profiles
• Photon beam diffusion [Habel13]–As accurate as quantized diffusion, but faster
–Accurate single-scattering model
–Can handle oblique incident angles
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Physically-based reflectance profiles
• Some other formulas:
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Physically-based reflectance profiles
• Some other formulas:
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Physically-based reflectance profiles
• Some other formulas:
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Physically-based reflectance profiles
• Some other formulas:
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Approximate reflectance profiles
• Forget physics ... just approximate curves!
• Standard approach: sum of Gaussians
0.001
0.01
0.1
1 2 3 4 5 6 7 8
r R(r)
r
Monte Carlo references (searchlight)A = 0.90A = 0.80A = 0.70A = 0.60A = 0.50A = 0.40A = 0.30A = 0.20A = 0.10
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Approximate reflectance profiles
• Burley: curves look more like exponentials
• Sum of two exponentials (divided by distance r) is remarkably good approximation
0.001
0.01
0.1
1 2 3 4 5 6 7 8
r R(r)
r
Monte Carlo references (searchlight)A = 0.90A = 0.80A = 0.70A = 0.60A = 0.50A = 0.40A = 0.30A = 0.20A = 0.10
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Approximate reflectance profiles
• Normalized diffusion model [Burley]:
• Multiply by A = surface albedo
• d controls width and height of curve ... but what is d ??–artistic control of subsurface “softness”
–what is connection between d and physical params?
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Translation from physical param to d
• Our usual way of expressing scattering distance is mfp or dmfp:–mean free path in volume
–diffuse mean free path on surface
• Let’s find a “translation” s between mfp and d:–d = mfp / s (s depends on A)
• With a translation we can compare normalized diffusion with physically-based diffusion models
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Translation from physical param to d
• To determine s it is sufficient to consider only curves for mfp=1 since the shape of reflectance profile curve for given A is independent of mfp
0.001
0.01
0.1
1 2 3 4 5 6 7 8
r R(r)
r
A = 0.5, mfp = 1reflectance profile
0.0001
0.001
0.01
10 20 30 40 50 60 70 80
r R(r)
r
A = 0.5, mfp = 10reflectance profile
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Translation from mfp to d
• For mfp=1 :
• Find s that minimizes difference between R(r) and Monte Carlo reference for same A
• For example: with optimal s for A = 0.2, 0.5, 0.8 ... :
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Comparisons: surface albedo 0.2
0.001
0.01
0.1
1 2 3 4 5 6 7 8
r R(r)
r
A = 0.2MC reference
our approx.Gauss2 approx.
dipole diffusion + 1scatbetter dipole + 1scat
beam diffusion + 1scat
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Comparisons: surface albedo 0.5
0.001
0.01
0.1
1 2 3 4 5 6 7 8
r R(r)
r
A = 0.5MC reference
our approx.Gauss2 approx.
dipole diffusion + 1scatbetter dipole + 1scat
beam diffusion + 1scat
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Comparisons: surface albedo 0.8
0.001
0.01
0.1
1 2 3 4 5 6 7 8
r R(r)
r
A = 0.8MC reference
our approx.Gauss2 approx.
dipole diffusion + 1scatbetter dipole + 1scat
beam diffusion + 1scat
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Comparisons: summary
• Normalized diffusion is closer to the MC reference points than dipole, better dipole, beam diffusion (w/ single scattering)
• Normalized diffusion (two exponentials) is a better approximation than two Gaussians
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Translation from mfp to d
• Find s that minimizes difference between R(r) and Monte Carlo reference for all A in 0.01, 0.02, ... , 0.99
• Gives data points; fit simple polynomial
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Translation from mfp to d
• Data points and fitted curve:
0
1
2
3
4
5
6
0 0.2 0.4 0.6 0.8 1
s
A
data pointsapprox.
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Translation from mfp to d
• Error wrt. MC references is ~5.5%
• Small error compared to appoximations and assumptions built into MC references: semi-infinite homogeneous volume, flat surface, ...
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Diffuse surface transmission
searchlight configurationmilk, juice, oily skin, ...
diffuse transmissiondry skin, make-up, ...
vs.
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Diffuse surface transmission
0.001
0.01
0.1
1 2 3 4 5 6 7 8
r R(r)
r
Monte Carlo references (diffuse)A = 0.90A = 0.80A = 0.70A = 0.60A = 0.50A = 0.40A = 0.30A = 0.20A = 0.10
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Translation from mfp to d (diffuse)
• Data points and fitted curve:
0
1
2
3
4
5
0 0.2 0.4 0.6 0.8 1
s
A
data pointsapprox.
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Translation from mfp to d (diffuse)
• Error wrt. MC references is only ~3.9%
• In practical use: not much visual difference between searchlight approx and diffuse-transmission approx -- even though built on very different assumptions
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Translation from dmfp to d
• Back to searchlight configuration
• Change parameterization of scattering distance: diffuse mean free path on surface (instead of mean free path in volume)
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Translation from dmfp to d
• Data points and fitted curve:
0
5
10
15
20
25
30
35
40
0 0.2 0.4 0.6 0.8 1
s
A
data pointsapprox.
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Translation from dmfp to d
• Error wrt. MC references is ~7.7%
• In practical use: dmfp might be more intuitive than mfp; hence standard parameter of our previous scattering models
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Translation summary
• 3 ways to determine d in Burley’s normalized diffusion formula:–mfp to d for searchlight configuration
–mfp to d for diffuse transmission
–dmfp to d for searchlight configuration
• 3 simple polynomials for s = s(A)
• Pick the one you like!
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Practical detail: importance sampling
• Importance sampling of distance r between light entry and exit points: need cdf(r)
• For physically-based BSSRDFs the cdf has to be computed with numerical integration: slow
• Burley’s normalized diffusion has simple cdf:
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Discussion
• Much simpler than physically-based diffusion (e.g. quantized diffusion or beam diffusion)
• Many times faster*
• *footnote: only a bit faster if careful table-based optimizations of physically-based
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Result: comparison w/ beam diffusion
beam diffusion + 1scatter our approx
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Result: comparison w/ beam diffusion
beam diffusion + 1scatter our approx
(Head data: Infinite Realities)
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Results
marble fruits plastic
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Result: still life
subsurface scattering
image credit: Dylan Sisson
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Conclusion
• Reparameterization of Burley’s normalized diffusion approximation gives plug-in replacement of physically-based diffusion formulas -- same parameters
• Simpler, faster
• Error wrt. MC references is only a few percent
• More accurate than physically-based models
• One of the sss models built into RenderMan
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Future work
• Oblique angles of incidence; non-symmetric scattering–maybe just s that depends on polar and relative
azimuthal angle of incident illumination?
• Anisotropic scattering?
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More information
• Burley, “Extending Disney’s physically based BRDF with integrated subsurface scattering”, Physically Based Shading Course
• Technical report: Christensen & Burley, graphics.pixar.com/library/ApproxBSSRDF
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Acknowledgments
• HUGE thanks: Brent Burley
• Colleagues in RenderMan team
• Christophe Hery and Ryusuke Villemin (Pixar)
• Wojciech Jarosz and Ralf Habel (Disney)
• Images: Dylan Sisson, ...
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Thank you!
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