interactive light transport with virtual point lights
TRANSCRIPT
1/59
PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Interactive Light Transport with Virtual PointLights
Benjamin Segovia1,2
1ENTPE: Ecole Nationale des Travaux Publics de l’Etat2LIRIS: Laboratoire d’InfoRmatique en Image et Systemes d’information
Benjamin Segovia Interactive Light Transport with Virtual Point Lights
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Presentation
1 Introduction
2 Formalizing the Problem
3 Sampling VPLs: Metropolis Instant Radiosity
4 Accumulating VPL contributions
5 Coherent Metropolis Light Transport
6 Conclusion
Benjamin Segovia Interactive Light Transport with Virtual Point Lights
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Summary
1 Introduction
2 Formalizing the Problem
3 Sampling VPLs: Metropolis Instant Radiosity
4 Accumulating VPL contributions
5 Coherent Metropolis Light Transport
6 Conclusion
Benjamin Segovia Interactive Light Transport with Virtual Point Lights
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Why a Ph.D. in computer graphics?
Movie / FX industry
Fast and robust renderingalgorithms;
Not necessary real-timebut speed is fundamental.
Figure: Poseidon, 2006, renderedwith Mental Ray
Benjamin Segovia Interactive Light Transport with Virtual Point Lights
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Why a Ph.D. in computer graphics?
Figure: Thee Dragon Room,rendered with yaCORT
Lighting design
Physically-based renderingtools;
Not necessary real-time.
Benjamin Segovia Interactive Light Transport with Virtual Point Lights
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Why a Ph.D. in computer graphics?
Video Games
The most realisticrendering with strictconstraints;
Real time (more than 30frames per second).
Figure: A Quake 3 scene,rendered with Qrender
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
What Does this Ph.D. Contain?
Common approach in science
1 Identify the physical problem→ Simulating light transport;
2 Propose an appropriate mathematical formalism→ The related physical quantities and the light transportequations;
3 Design algorithms to solve these equations
→ Computer science
Numerical schemesAlgorithms, codes . . .
The contribution of this Ph.D. thesis is mostly contained by the thirdpoint
→ Numerical schemes to solve the light transport equationsBenjamin Segovia Interactive Light Transport with Virtual Point Lights
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Overview of the Presentation
First, introduction of necessary concepts
Physics: Physics of light transport → quantities andequations;
Mathematics: Roots of Monte-Carlo and introduction of theappropriate formalism;
Computer Graphics: Most common algorithms used tocompute virtual pictures.
Benjamin Segovia Interactive Light Transport with Virtual Point Lights
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Overview of the Presentation
Then, presentation of the contributions
Two classes of contributions:
Coding Techniques: Once the set of VPLs has beencomputed, how can we accumulate their contributions ?→ we present two techniques using graphics hardware;
Sampling Techniques: How can we generate efficient sets ofVPLs?
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Summary
1 Introduction
2 Formalizing the Problem
3 Sampling VPLs: Metropolis Instant Radiosity
4 Accumulating VPL contributions
5 Coherent Metropolis Light Transport
6 Conclusion
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
The Rendering Equation - Three Point Form [Vea97]
Formalizes the behavior of materials and surfaces
L(x′→x′′) = Le(x′→x′′)+
∫
M
L(x→x′)fs(x→x′→x′′)G (x↔x′)dA(x)
where:
L is the equilibrium outgoing radiance function;
Le(x′→x′′) is the emitted radiance leaving x′ in the direction of x′′;
fs(x→x′→x′′) is the BSDF of the material;
M is the union of all the surfaces of the scene;
A is the Lebesgue (i.e. uniform) area measure on M;
G(x↔x′) is the geometric term between x and x′.
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
The Measurement Equation
Response of a given captor / sensor
Ij =
∫
M×M
W(j)e (x→x′)L(x→x′) G (x↔x′) dA(x) dA(x′)
where W (j)(x, ω) is the responsivity of sensor j .
Benjamin Segovia Interactive Light Transport with Virtual Point Lights
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Issues with Light Transport
High dimensional problem: light may bounce many times . . .
High frequency problem: many discontinuities (shadows forexample).
Benjamin Segovia Interactive Light Transport with Virtual Point Lights
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Issues with Light Transport
High dimensional problem: light may bounce many times . . .
High frequency problem: many discontinuities (shadows forexample).
Integrand has very poor properties →
Benjamin Segovia Interactive Light Transport with Virtual Point Lights
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Issues with Light Transport
High dimensional problem: light may bounce many times . . .
High frequency problem: many discontinuities (shadows forexample).
Integrand has very poor properties →
Use Monte-Carlo integration!
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Monte-Carlo Integration is Basically . . .
We want to integrate I =∫
Ω f (x)dµ(x)where
Ω is a given space;
µ is an associated measure;
f is a measurable function on (Ω, µ).
With sufficient properties ...
N random variables (Xn)n∈[1...N] with density p, then:
limN→∞
IN = limN→∞
1
N
N∑
n=1
f (Xn)
p(Xn)= I almost surely
Benjamin Segovia Interactive Light Transport with Virtual Point Lights
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
The Path Integral Formulation
Make the light transport problem an integration one
Inject the rendering eq. into the measurement eq. and expand it:
Ij =∑
∞
k=1
∫
Mk+1
[
Le(xk→xk−1)G (x0↔x1) W(j)e (x1→x0)
(∏k−1
i=1 fs(xi+1→xi→xi−1)G (xi↔xi+1))dA(x0) ... dA(xk)]
Benjamin Segovia Interactive Light Transport with Virtual Point Lights
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
The Path Integral Formulation
Make the light transport problem an integration one
Inject the rendering eq. into the measurement eq. and expand it:
Ij =∑
∞
k=1
∫
Mk+1
[
Le(xk→xk−1)G (x0↔x1) W(j)e (x1→x0)
(∏k−1
i=1 fs(xi+1→xi→xi−1)G (xi↔xi+1))dA(x0) ... dA(xk)]
The path integral formulation
Ij =
∫
Ωf (j)(x)dµ(x)
Ω is the set of all finite length paths, µ its natural measure andf (j) obtained with the expansion.
Benjamin Segovia Interactive Light Transport with Virtual Point Lights
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
A Short Pause Before the Remainder!
OK, a short summary!
Monte-Carlo rendering is:
Sample a path x with density p(x);
Evaluate f (j)(x)p(x) ;
Accumulate.
Benjamin Segovia Interactive Light Transport with Virtual Point Lights
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
A Short Pause Before the Remainder!
OK, a short summary!
Monte-Carlo rendering is:
Sample a path x with density p(x);
Evaluate f (j)(x)p(x) ;
Accumulate.
Most Monte-Carlo rendering methods → propose new ways togenerate paths x .
Basically, this Ph.D. presents new Monte-Carlo renderingtechniques.
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Short Overview of Path Integration
Core algorithm: path tracing [Kaj86]
We generate a light path backward from the camera for eachcamera sensor (i.e. for each pixel)
Benjamin Segovia Interactive Light Transport with Virtual Point Lights
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Short Overview of Path Integration
Core algorithm: path tracing [Kaj86]
We generate a light path backward from the camera for eachcamera sensor (i.e. for each pixel)
Many, many similar techniques
Bidirectional path tracing [VG94, LW93];
Light tracing [DLW93].
Benjamin Segovia Interactive Light Transport with Virtual Point Lights
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Path Tracing
Benjamin Segovia Interactive Light Transport with Virtual Point Lights
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Path Tracing
Benjamin Segovia Interactive Light Transport with Virtual Point Lights
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Path Tracing
Benjamin Segovia Interactive Light Transport with Virtual Point Lights
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Path Tracing
Benjamin Segovia Interactive Light Transport with Virtual Point Lights
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Path Tracing
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Problems with these ”Pure” Path Tracing methods
No computation coherency
Per-pixel computations are independent;
No factorization.
Benjamin Segovia Interactive Light Transport with Virtual Point Lights
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Problems with these ”Pure” Path Tracing methods
No computation coherency
Per-pixel computations are independent;
No factorization.
We must design efficient techniques
Most of them propose to use biased estimators:
Photon Maps [Jen01, Jen96, Jen97];
Radiance / Irradiance Caches [WRC88, War94, K05];
And . . .
Benjamin Segovia Interactive Light Transport with Virtual Point Lights
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Problems with these ”Pure” Path Tracing methods
No computation coherency
Per-pixel computations are independent;
No factorization.
Instant Radiosity [Kel97] → Replaces complete paths by”Virtual Point Lights”
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Instant Radiosity
Principles
Splits each path x = x0, x1, . . . , xn into three parts:
xc = x0, x1 is the camera sub-path;
xv is a geometric Virtual Point Light (VPL);
xs is the remainder of the path connected to a light source.
x0
xc
xv
xs
x1
Benjamin Segovia Interactive Light Transport with Virtual Point Lights
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Instant Radiosity
Principles
For all sensors (i.e. pixels), use the same (xv , xs) light paths;
Two-pass algorithm:
Propagation of light paths from the light sources (sampling);Accumulation of VPL contributions (gathering).
Do not forget: a VPL is a light path, not only a point!
x0
xc
xv
xs
x1
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
The Two Steps of Instant Radiosity
Particle Propagation
Benjamin Segovia Interactive Light Transport with Virtual Point Lights
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
The Two Steps of Instant Radiosity
Particle Propagation
Benjamin Segovia Interactive Light Transport with Virtual Point Lights
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
The Two Steps of Instant Radiosity
Particle Propagation
Benjamin Segovia Interactive Light Transport with Virtual Point Lights
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
The Two Steps of Instant Radiosity
Particle Propagation
Benjamin Segovia Interactive Light Transport with Virtual Point Lights
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
The Two Steps of Instant Radiosity
Particle Propagation
Benjamin Segovia Interactive Light Transport with Virtual Point Lights
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
The Two Steps of Instant Radiosity
Particle Propagation
Benjamin Segovia Interactive Light Transport with Virtual Point Lights
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
The Two Steps of Instant Radiosity
Incoming Radiance Field Integration
Benjamin Segovia Interactive Light Transport with Virtual Point Lights
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
The Two Steps of Instant Radiosity
Incoming Radiance Field Integration
Benjamin Segovia Interactive Light Transport with Virtual Point Lights
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
The Two Steps of Instant Radiosity
Incoming Radiance Field Integration
Benjamin Segovia Interactive Light Transport with Virtual Point Lights
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
The Two Steps of Instant Radiosity
Incoming Radiance Field Integration
Benjamin Segovia Interactive Light Transport with Virtual Point Lights
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
The Two Steps of Instant Radiosity
Incoming Radiance Field Integration
Benjamin Segovia Interactive Light Transport with Virtual Point Lights
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Advantages and Drawbacks of Instant Radiosity
Advantages
Simple → the incoming radiance field is replaced by a set ofpoints;
Fast → can be easily implemented with coherent ray tracingor rasterization.
Benjamin Segovia Interactive Light Transport with Virtual Point Lights
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Advantages and Drawbacks of Instant Radiosity
Advantages
Simple → the incoming radiance field is replaced by a set ofpoints;
Fast → can be easily implemented with coherent ray tracingor rasterization.
Drawbacks
Variance problems → how must the VPLs be located?
Does not handle all lighting phenomena → caustics . . .
Benjamin Segovia Interactive Light Transport with Virtual Point Lights
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Summary
1 Introduction
2 Formalizing the Problem
3 Sampling VPLs: Metropolis Instant Radiosity
4 Accumulating VPL contributions
5 Coherent Metropolis Light Transport
6 Conclusion
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Goal of Metropolis Instant Radiosity (MIR)
Properties of VPLs is fundamental
We must find VPLs which illuminate parts of the scene seen by thecamera!
Benjamin Segovia Interactive Light Transport with Virtual Point Lights
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Goal of Metropolis Instant Radiosity (MIR)
Solution: Combine the robutness of Metropolis LightTransport and the efficiency of Instant Radiosity
Principle of MIR
Use the path sequence of Metropolis Light Transport tosample VPLs (”MLT part”);
For each path, store the second point as a VPL;
Accumulate all VPL contributions (”IR part”).
With this sampler, all VPLs will bring the same amount ofpower to the camera
Benjamin Segovia Interactive Light Transport with Virtual Point Lights
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Metropolis Light Transport [VG97]
Principle (a short version)
Consider the whole camera integrand f (c);
Sample N paths with a density proportional to f (c);
Count for each pixel j , the number Nj of paths which get intoit;
With Nj , N, and∫
Ω f (c)(x)dµ(x), compute the per-pixel
histogram of f (c);
We have the intensity of each pixel!
Benjamin Segovia Interactive Light Transport with Virtual Point Lights
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Metropolis Light Transport [VG97]
Numerical schemes behind it
Compute∫
Ω f (c)(x)dµ(x)→ Use a standard bidirectional path tracer;
Sample N paths with a density proportional to f (c)
→ Use a Metropolis-Hastings algorithm.
Benjamin Segovia Interactive Light Transport with Virtual Point Lights
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Metropolis Light Transport [VG97]
Metropolis-Hastings
Goal: given function f , sequentially sample random variablesXi with a density proportional to f ;
Xi+1 and Xi are correlated by a mutation.
The density of Xi is not exactly f , but with good properties(”ergodicity”), we can use all Xi :as if their densities were fas if they were independent
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
MLT - Initial Path
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
MLT - Candidate
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
MLT - Candidate accepted → Count its contribution
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
MIR - Compute the power received by the camera
With a bidirectional path tracer (or any other technique)compute the power Pc =
∫
Ω f (c)(x)dµ(x) received by the camera.
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
MIR - Sample ”path VPLs”
The core idea of the method
MLT algorithm provides complete paths x0, x1, xv, x s;
Remove points x0 and x1 and consider (xv, x s) as a ”pathVPL”.
x0
xc
xv
xs
x1
Benjamin Segovia Interactive Light Transport with Virtual Point Lights
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
MIR - Sample ”path VPLs”
The core idea of the method
MLT algorithm provides complete paths x0, x1, xv, x s;
Remove points x0 and x1 and consider (xv, x s) as a ”pathVPL”.
x0
xc
xv
xs
x1
Path VPL
Benjamin Segovia Interactive Light Transport with Virtual Point Lights
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
MIR - Sample ”path VPLs”
The core idea of the method
MLT algorithm provides complete paths x0, x1, xv, x s;
Remove points x0 and x1 and consider (xv, x s) as a ”pathVPL”.
We do not know the outgoing radiance functions of the VPLs;
But, we can prove that these VPLs bring the sameamount of power to the camera
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
MIR - Cluster ”path VPLs” into ”geometric VPLs”
Cluster path VPLs with the same VPL location into one geometric VPL
When applying mutations, VPL locations may remain unchanged:
The candidate is rejected and the path is duplicated;
Only the sub-path xc = x0, x1 is mutated;
Only the sub-path x s is mutated.
Benjamin Segovia Interactive Light Transport with Virtual Point Lights
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
MIR - Cluster ”path VPLs” into ”geometric VPLs”
Cluster path VPLs with the same VPL location into one geometric VPL
When applying mutations, VPL locations may remain unchanged:
The candidate is rejected and the path is duplicated;
Only the sub-path xc = x0, x1 is mutated;
Only the sub-path x s is mutated.
x0
xc
xv
xs
x1
Before
Benjamin Segovia Interactive Light Transport with Virtual Point Lights
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
MIR - Cluster ”path VPLs” into ”geometric VPLs”
Cluster path VPLs with the same VPL location into one geometric VPL
When applying mutations, VPL locations may remain unchanged:
The candidate is rejected and the path is duplicated;
Only the sub-path xc = x0, x1 is mutated;
Only the sub-path x s is mutated.
x0
xc
xv
xs
x1
Mutation
Benjamin Segovia Interactive Light Transport with Virtual Point Lights
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
MIR - Cluster ”path VPLs” into ”geometric VPLs”
Cluster path VPLs with the same VPL location into one geometric VPL
When applying mutations, VPL locations may remain unchanged:
The candidate is rejected and the path is duplicated;
Only the sub-path xc = x0, x1 is mutated;
Only the sub-path x s is mutated.
x0
xc
xv
xs
x1
After
Benjamin Segovia Interactive Light Transport with Virtual Point Lights
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
MIR - Cluster ”path VPLs” into ”geometric VPLs”
Cluster path VPLs with the same VPL location into one geometric VPL
When applying mutations, VPL locations may remain unchanged:
The candidate is rejected and the path is duplicated;
Only the sub-path xc = x0, x1 is mutated;
Only the sub-path x s is mutated.
x0
xc
xv
x1
2 path VPLs into 1 geometric VPL
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
MIR - Accumulate the VPL contributions
Set of m geometric VPLs xvi
They bring a fixed amount of power to the camera equal to
Pi = ki · Pc/n;
n is the total number of path VPLs;
ki is the number of path VPLs connected to xvi.
Benjamin Segovia Interactive Light Transport with Virtual Point Lights
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
MIR - Accumulate the VPL contributions
Set of m geometric VPLs xvi
They bring a fixed amount of power to the camera equal to
Pi = ki · Pc/n;
n is the total number of path VPLs;
ki is the number of path VPLs connected to xvi.
We do not know the ”VPL power”
Suppose that the power of the VPL is equal to 1;
Compute the intensity of every pixel;
Evaluate the total power P ′i ;
Scale all pixel intensities by Pi/P′i .
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
MIR - Decrease the VPL correlation
Classical Issue with Metropolis-Hastings
Example: If a VPL is on a wall, there is a high probability that thenext one will be on the wall too.
Benjamin Segovia Interactive Light Transport with Virtual Point Lights
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
MIR - Decrease the VPL correlation
Classical Issue with Metropolis-Hastings
Example: If a VPL is on a wall, there is a high probability that thenext one will be on the wall too.
Increase Variance!
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
MIR - Decrease the VPL correlation
Classical Issue with Metropolis-Hastings
Example: If a VPL is on a wall, there is a high probability that thenext one will be on the wall too.
Increase Variance!
Replace Metropolis-Hastings by Multiple-Try Metropolis-Hastings
Idea (simplified explanation): generate many candidates atonce and try to keep the best one;
Does not really change the conception of the algorithm;
Details in the Ph.D. thesis.
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Results - MH vs MTMH - Same Computation Times
MH MTMH
Figure: Exploration of left/right contributions (256 VPLs)
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Results and Comparisons
Different tests were made
Test scenes
With directly-lit scenes;
With many light sources;
With difficult visibility layouts.
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Results and Comparisons
Different tests were made
Other algorithms
Standard Instant Radiosity [Kel97];
Power Sampling Technique [WBS03];
Bidirectional Instant Radiosity.
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Results - Simple Scenes - 256 VPLs - Same ComputationTimes
Reference (standard)
STD - 0.02% BIR - 0.007%
Power Sampling - 0.008% MIR - 0.009%
Figure: Tests with Shirley’s Scene 10.Benjamin Segovia Interactive Light Transport with Virtual Point Lights
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Results - Difficult Visibility - 1024 VPLs - SameComputation Times
Standard / Power Sampling Bidirectional MIR
Figure: Indirect illumination stress tests.
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Results - Difficult Visibility - 1024 VPLs - SameComputation Times
Standard / Power Sampling Bidirectional MIR
Figure: Indirect illumination stress tests.
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Advantages
Thanks to MLT → Robust and fast sampling strategies;
Thanks to Instant Radiosity → Fast and efficient gatheringtechniques:→ We can use IGI;→ We can use rasterization techniques . . .
Non-intrusive algorithm → Can be used in any pre-existingrenderer already using VPLs and Path Tracing.
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Drawbacks and Future Work
Does not handle flickering problems during animations
Nothing is made to ensure temporal coherency→ if one sample changes, the whole sequence is modified;
Solution: Reuse the previous samples with a sequentialsampler (see [GDH06]).
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Drawbacks and Future Work
And glossy and specular reflections ?!
What happens if a part of the scene is seen through a highlyglossy or a specular reflection ?
Solution - Already implemented in yaCORT:→ Instead, find the second diffuse surface to deposit the VPLwith probability P ;→ While gathering, compute a camera sub-path which has alength with the same probability.
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Drawbacks and Future Work
And Caustics ?!
Does not easily handle caustics.
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Summary
1 Introduction
2 Formalizing the Problem
3 Sampling VPLs: Metropolis Instant Radiosity
4 Accumulating VPL contributions
5 Coherent Metropolis Light Transport
6 Conclusion
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Non-interleaved Deferred Shading of Interleaved SamplePatterns
Goal
We want to accumulate the contributions of a VPL set.
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Non-interleaved Deferred Shading of Interleaved SamplePatterns
Goal
We want to accumulate the contributions of a VPL set.
Issues
Many light sources == Large fillrate;
Many light sources == Many rasterization steps.
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Non-interleaved Deferred Shading of Interleaved SamplePatterns
Goal
We want to accumulate the contributions of a VPL set.
Issues
Many light sources == Large fillrate;
Many light sources == Many rasterization steps.
Strategy: combine two techniques
Deferred Shading → geometry rasterized once;
Interleaved Sampling → decreases fill rate and maintains goodimage quality.
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Deferred Shading [DWS+88, ST90]
Principles
The per-pixel geometric information is stored in a GeometricBuffer (G-buffer) (Normals, positions and colors)
The G-buffer is then read to perform any lightingcomputation.
It greatly simplifies the rendering pipeline and it alsoprevents the geometry from being reprojected each time a
shading pass is performed.
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Interleaved Sampling [KH01]
Instead of evaluating all VPL contributions for all pixels, weuse separate subsets of VPLs for every neighbor pixel.
(a) Standard Sampling (b) Interleaved Sampling
Already used in ray tracing → ”Instant Global Illumination”Benjamin Segovia Interactive Light Transport with Virtual Point Lights
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Interleaved Sampling [KH01]
Instead of evaluating all VPL contributions for all pixels, weuse separate subsets of VPLs for every neighbor pixel.
Already used in ray tracing → ”Instant Global Illumination”Benjamin Segovia Interactive Light Transport with Virtual Point Lights
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Overview of the Algorithm
Creation Splitting Shading Gathering
Discontinuity Filtering Blending
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Overview of the Algorithm
Creation Splitting Shading Gathering
Discontinuity Filtering Blending
Conservative extension of deferred shading: all algorithms usingdeferred shading may also be used with our system.
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Core of the Algorithm: G-buffer Splitting
Principle
G-buffer G split into n × m smaller tiled sub-buffers Gi ,j
Texel (x , y) from G goes to texel (x/n, y/m) of sub-bufferGi , j with i = x mod n and j = y mod m.
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Core of the Algorithm: G-buffer Splitting
Principle
G-buffer G split into n × m smaller tiled sub-buffers Gi ,j
Texel (x , y) from G goes to texel (x/n, y/m) of sub-bufferGi , j with i = x mod n and j = y mod m.
Fast Solution - Two-pass splitting
Small blocks are split;
Split blocks are translated.
Results: fast
A 1024 × 1024 192 bit G-buffer is split in 7 ms on a 6800GT;
20 ms with a one-pass splitting.
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Core of the Algorithm: Filtering
Coherency of the pixels
Discontinuity buffer;
Box blur on continuous zones of the screen.
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Core of the Algorithm: Filtering
Coherency of the pixels
Discontinuity buffer;
Box blur on continuous zones of the screen.
+X
P
+X
PFigure: Box Blur
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Core of the Algorithm: Filtering
Coherency of the pixels
Discontinuity buffer;
Box blur on continuous zones of the screen.
Interleaved Sub-sampling
Figure: Quality
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Results - 500 sources - 1024× 768 - IS 8 × 6
Fully interactive applications
No visibility for secondary light sources;
Fast!
69 f/s 36 f/s (×31) 64 f/s 29 f/s (×25)
58 f/s 29 f/s (×26) 57 f/s 29 f/s (×30)Benjamin Segovia Interactive Light Transport with Virtual Point Lights
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Results - Physically Based Rendering - 1280 × 1024 - IS2 × 2
Physically based rendering
Visibility for secondary light sources
0.7 s - 14 f/s (×3.4) 4.0 s - 2.5 f/s (×1.5)
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Conclusion
To sum up . . .
Generic extension of deferred shading;
Trade-off between quality and speed.
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Summary
1 Introduction
2 Formalizing the Problem
3 Sampling VPLs: Metropolis Instant Radiosity
4 Accumulating VPL contributions
5 Coherent Metropolis Light Transport
6 Conclusion
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Issues with Virtual Point Lights
VPL based techniques
Fast;
Simple;
Elegant.
But:
Do not handle all lighting phenomena;
Use the same VPL family for all pixels.
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Issues with Virtual Point Lights
VPL based techniques
Fast;
Simple;
Elegant.
But:
Do not handle all lighting phenomena;
Use the same VPL family for all pixels.
Alternative approach
Instead of making Instant Radiosity more robust, make MetropolisLight Transport more coherent and faster.
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Advantages and Drawbacks of Metropolis Light Transport
Advantages
Conceptually super simple;
Very robust → it samples the density we want;
Handles all kinds of lighting phenomena.
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Advantages and Drawbacks of Metropolis Light Transport
Advantages
Conceptually super simple;
Very robust → it samples the density we want;
Handles all kinds of lighting phenomena.
Drawbacks
Pretty difficult to implement;
Slow! → does not use efficient techniques like rasterization orcoherent ray tracing.
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Coherent Metropolis Light Transport
Core idea
Replace standard MCMC mutations by Multiple-try ones.Goal: Generating many paths at the same time.
Uses SIMD computations;
Factorizes cache accesses!Fundamental for any commercial renderer
x
D
D
E
ED diffuse
eye
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Split MLT in Three Steps
Step 1: Exploration of the sample space with MCMC mutations
Standard Metropolis Light Transport
timeBenjamin Segovia Interactive Light Transport with Virtual Point Lights
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Split MLT in Three Steps
Step 1: Exploration of the sample space with MCMC mutations
Provides a set of n path samples (x i )i∈[1...n] with density
f (c)/||f (c)|| (Squares)
timeBenjamin Segovia Interactive Light Transport with Virtual Point Lights
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Split MLT in Three Steps
Step 2: Fast exploration of the lens sub-space
Lens sub-space: ES∗DS∗(L|D)Sub-paths from the camera which intersect two diffuse surfaces
timeBenjamin Segovia Interactive Light Transport with Virtual Point Lights
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Split MLT in Three Steps
Step 2: Fast exploration of the lens sub-space
Use multiple-try mutations. At each step, two families:
”Candidates” x∗1 . . . x∗
p (Disks);
”Competitors” x∗∗1 . . . x∗∗
p (Triangles).
timeBenjamin Segovia Interactive Light Transport with Virtual Point Lights
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Split MLT in Three Steps
Step 3: Accumulate all sample contributions
Each family: use of the ”expected value”: accumulate x∗ accordingto Rg and x∗∗ according to 1 − Rg ;
Each element: accumulate each element x proportionally to f (c)(x)
time
Camera
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Implementation - Mutation strategies
Exploration of the entire space: standard MLT with bidirectionalmutations only;
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Implementation - Mutation strategies
Exploration of the lens sub-space: use of lens and caustics pertuba-tions.
Jittering;
Gaussiandistributions aroundthe initial samples;
”Packetize” therays → use SIMDinstructions.
S
D
D
E L
a) b)
c)Benjamin Segovia Interactive Light Transport with Virtual Point Lights
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Implementation - Mutation strategies
Exploration of the lens sub-space: use of lens and caustics pertuba-tions.
With pictures . . .
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Results
Quality equivalent to the quality obtained with MLT but . . .
As MLT, some parameters have to be carefully tuned:
Lengths of MTMH sub-sequences;
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Results
Quality equivalent to the quality obtained with MLT but . . .
As MLT, some parameters have to be carefully tuned:
Lengths of MTMH sub-sequences;
σ and the number of MTMH candidates.
(a) σ = 16 pixels (b) σ = 32 pixels (c) σ = 64 pixels
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Results
Quality equivalent to the quality obtained with MLT but . . .
As MLT, some parameters have to be carefully tuned:
Lengths of MTMH sub-sequences;
σ and the number of MTMH candidates.
Overall performance with SIMD
Speed-up from 1.5 to 2.3.
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Results - Cache Simulation
Affinity with caches is fundamental
Distribution ray tracing in complex scenes [CLF+03, Chr06];
Multi-resolution geometry caching;
On-the-fly tesselation;
Memory systems with difficult layouts:Cell processors (PS3, Blade Center)Xenon (XBOX 360)LarabeePC cluster . . .
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Results - Cache Simulation
Theater Three Dragons4 Tri 16 Tri 128 Tri 4 Tri 16 Tri 128 Tri
MLT 53% 61% 60% 54% 63% 70%CMLT 86% 92% 98% 81% 95% 99%
IR 87% 93% 99% 82% 95% 99%
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
CMLT - Conclusion and Remarks
Faster than MLT
Simple extension of MLT → reorganization of thecomputations;
Not real time (and not even interactive) but may be a goodalternative.
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
CMLT - Conclusion and Remarks
Faster than MLT
Simple extension of MLT → reorganization of thecomputations;
Not real time (and not even interactive) but may be a goodalternative.
But . . .
As MIR, does not handle flickering problems during animation.It is a major problem with ”importance driven” methods.
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Summary
1 Introduction
2 Formalizing the Problem
3 Sampling VPLs: Metropolis Instant Radiosity
4 Accumulating VPL contributions
5 Coherent Metropolis Light Transport
6 Conclusion
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Conclusion
During three years . . .
Many implementations: GPU, coherent ray tracing;
Many numerical schemes.
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Conclusion
But . . . no ”ultimate” renderer was found!
However . . . some personal points of view
For absolute realism and large interactivity:
Monte-Carlo + Brute Force + Carefully DesignedImplementation is the only solution
No compression, no PRT, no expensive pre-computation;
Rasterization vs ray tracing → Geometric efficiency vs lightingsimulation efficiency?
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Merci!
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PresentationIntroduction
Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Per Christensen.Ray Tracing for the Movie ”Cars”.In IEEE Symposium on Interactive Ray Tracing, pages 1–6,2006.
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Philip Dutre, Eric Lafortune, and Yves Willems.Monte Carlo Light Tracing with Direct Computation of PixelIntensities.
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Formalizing the ProblemSampling VPLs: Metropolis Instant Radiosity
Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
In Compugraphics ’93 (Proceedings of Third InternationalConference on Computational Graphics and VisualizationTechniques), 1993.
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Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
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Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
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Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
Jaroslav Krivanek.Radiance Caching for Global Illumination Computation onGlossy Surfaces.PhD thesis, Universite de Rennes 1 and Czech TechnicalUniversity in Prague, 2005.
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Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
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Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
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Accumulating VPL contributionsCoherent Metropolis Light Transport
Conclusion
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Benjamin Segovia Interactive Light Transport with Virtual Point Lights