Monte Carlo Path Tracing Today Path tracing Random walks and Markov chains Eye vs. light ray tracing Bidirectional ray tracing Next Irradiance caching Photon mapping University of Texas at Austin CS395T - Advanced Image Synthesis Spring 2007 Don Fussell

Light Path Should we show the directional form of this equation? University of Texas at Austin CS395T - Advanced Image Synthesis Spring 2007 Don Fussell

Light Transport Integrate over all paths Questions How to sample space of paths University of Texas at Austin CS395T - Advanced Image Synthesis Spring 2007 Don Fussell

Path Tracing

Penumbra: Trees vs. Paths Path tracing vs. recursive ray tracing using trees Use less samples for this Superimpose paths and trees over these images 4 eye rays per pixel 16 shadow rays per eye ray 64 eye rays per pixel 1 shadow ray per eye ray University of Texas at Austin CS395T - Advanced Image Synthesis Spring 2007 Don Fussell

Path Tracing: From Camera Step 1. Choose a camera ray r given the (x,y,u,v,t) sample weight = 1; Step 2. Find ray-surface intersection Step 3. if light return weight * Le(); else weight *= reflectance(r) Choose new ray r’ ~ BRDF pdf(r) Go to Step 2. Should illustrate this algorithm using the Cornell Box Confusion about reflectance The BRDF is not normalized to be a pdf The directional hemispherical reflectance is the correct normalization factor How to choose between Le and Lr University of Texas at Austin CS395T - Advanced Image Synthesis Spring 2007 Don Fussell

M. Fajardo Arnold Path Tracer Return to this when discussing the adjoint formulation University of Texas at Austin CS395T - Advanced Image Synthesis Spring 2007 Don Fussell

Cornell Box: Path Tracing Check that I am referring to these images in the correct way 10 rays per pixel 100 rays per pixel From Jensen, Realistic Image Synthesis Using Photon Maps University of Texas at Austin CS395T - Advanced Image Synthesis Spring 2007 Don Fussell

Path Tracing: Include Direct Lighting Step 1. Choose a camera ray r given the (x,y,u,v,t) sample weight = 1; Step 2. Find ray-surface intersection Step 3. weight += Lr(light sources) Choose new ray r’ ~ BRDF pdf(r) Go to Step 2. Should illustrate this algorithm using the Cornell Box Confusion about reflectance The BRDF is not normalized to be a pdf The directional hemispherical reflectance is the correct normalization factor How to choose between Le and Lr University of Texas at Austin CS395T - Advanced Image Synthesis Spring 2007 Don Fussell

Discrete Random Walk

Discrete Random Process States Creation Termination Transition Assign probabilities to each process University of Texas at Austin CS395T - Advanced Image Synthesis Spring 2007 Don Fussell

Discrete Random Process States Creation Termination Transition Equilibrium number of particles in each state University of Texas at Austin CS395T - Advanced Image Synthesis Spring 2007 Don Fussell

Discrete Random Walk Generate random particles from sources. States Generate random particles from sources. Undertake a discrete random walk. Count how many terminate in state i [von Neumann and Ulam; Forsythe and Leibler; 1950s] Creation Termination Transition University of Texas at Austin CS395T - Advanced Image Synthesis Spring 2007 Don Fussell

Monte Carlo Algorithm Define a random variable on the space of paths Probability: Estimator: Expectation: University of Texas at Austin CS395T - Advanced Image Synthesis Spring 2007 Don Fussell

Monte Carlo Algorithm Define a random variable on the space of paths Probability: Estimator: University of Texas at Austin CS395T - Advanced Image Synthesis Spring 2007 Don Fussell

Estimator Count the number of particles terminating in state j Do I have I,j reversed in Mi,j? University of Texas at Austin CS395T - Advanced Image Synthesis Spring 2007 Don Fussell

Equilibrium Distribution of States Total probability of being in states P Note that this is the solution of the equation Thus, the discrete random walk is an unbiased estimate of the equilibrium number of particles in each state University of Texas at Austin CS395T - Advanced Image Synthesis Spring 2007 Don Fussell

Light Ray Tracing

Backward ray tracing, Arvo 1986 Examples Backward ray tracing, Arvo 1986 University of Texas at Austin CS395T - Advanced Image Synthesis Spring 2007 Don Fussell

Path Tracing: From Lights Step 1. Choose a light ray Choose a light source according to the light source power distribution function. Choose a ray from the light source radiance (area) or intensity (point) distribution function w = 1; Step 2. Trace ray to find surface intersect Step 3. Interaction University of Texas at Austin CS395T - Advanced Image Synthesis Spring 2007 Don Fussell

Path Tracing: From Lights Step 1. Choose a light ray Step 2. Find ray-surface intersection Step 3. Interaction u = rand() if u < reflectance Choose new ray r ~ BRDF goto Step 2 else terminate on surface; deposit energy University of Texas at Austin CS395T - Advanced Image Synthesis Spring 2007 Don Fussell

Bidirectional Path Tracing

Bidirectional Ray Tracing Need to make notation clear K is the number of vertices on a path K-2 is the number of bounces on the path Should probably show an example with k=4, not k=3 University of Texas at Austin CS395T - Advanced Image Synthesis Spring 2007 Don Fussell

Path Pyramid From Veach and Guibas 25 samples per pixel Combined using the power heuristic Middle image in the k=4 row Rightmost image in the bottom row 5 light-path vertices 5 steps from the floor-lamp to the table Everyone really liked this picture Understand the phenomenology behind each subimage Is there a version of this picture that contains the weights for each pixel Review multiple importance sampling: need to keep the weights and values If there are multiple samples for each pixel, Kurt suggests premultiplying by the weights and keeping a running sum of the weights. From Veach and Guibas University of Texas at Austin CS395T - Advanced Image Synthesis Spring 2007 Don Fussell

Comparison Bidirectional path tracing Path tracing From Veach and Guibas University of Texas at Austin CS395T - Advanced Image Synthesis Spring 2007 Don Fussell

Generating All Paths University of Texas at Austin CS395T - Advanced Image Synthesis Spring 2007 Don Fussell

Adjoint Formulation

Symmetric Light Path All this adjoint stuff is confusing What are the key points Can always formulate adjoint version of the problem Rendering operators are self-adjoint Solution of the adjoint equation can be used as an importance function Clear explanation with examples of why you want to reverse sources and receivers University of Texas at Austin CS395T - Advanced Image Synthesis Spring 2007 Don Fussell

Symmetric Light Path University of Texas at Austin CS395T - Advanced Image Synthesis Spring 2007 Don Fussell

Symmetric Light Path University of Texas at Austin CS395T - Advanced Image Synthesis Spring 2007 Don Fussell

Three Consequences Forward estimate equal backward estimate - May use forward or backward ray tracing Adjoint solution - Importance sampling paths Solve for small subset of the answer University of Texas at Austin CS395T - Advanced Image Synthesis Spring 2007 Don Fussell

Example: Linear Equations Solve a linear system Solve for a single xi? Solve the adjoint equation Source Estimator More efficient than solving for all the unknowns [von Neumann and Ulam] University of Texas at Austin CS395T - Advanced Image Synthesis Spring 2007 Don Fussell