Homework #3 Environment Lights

Slides:



Advertisements
Similar presentations
Insu Yu (Research Fellow) James Tompkin (Research Engineer & T.A.) ( me with questions)
Advertisements

Real-time Shading with Filtered Importance Sampling
Grey Level Enhancement Contrast stretching Linear mapping Non-linear mapping Efficient implementation of mapping algorithms Design of classes to support.
Retrospect Digital Image Synthesis Yu-Ting Wu. So far we have learned: Geometry and transforms Shapes Accelerators Color and radiometry Cameras Sampling.
Shu-Jheng Huang 1 / 25 Structured Importance Sampling of Environment Maps Agarwal, S., R. Ramamoorthi, S. Belongie, and H. W. Jensen.
Sampling Attila Gyulassy Image Synthesis. Overview Problem Statement Random Number Generators Quasi-Random Number Generation Uniform sampling of Disks,
Monte Carlo Integration Robert Lin April 20, 2004.
Advanced Computer Graphics (Spring 2005) COMS 4162, Lectures 18, 19: Monte Carlo Integration Ravi Ramamoorthi Acknowledgements.
Photon Tracing with Arbitrary Materials Patrick Yau.
Advanced Computer Graphics (Fall 2009) CS 294, Rendering Lecture 5: Monte Carlo Path Tracing Ravi Ramamoorthi
Rendering General BSDFs and BSSDFs Randy Rauwendaal.
Advanced Computer Graphics (Spring 2006) COMS 4162, Lecture 20: Monte Carlo Path Tracing Ravi Ramamoorthi Acknowledgements.
1 Monte Carlo Global Illumination Brandon Lloyd COMP 238 December 16, 2002.
Object recognition under varying illumination. Lighting changes objects appearance.
1 Stratified sampling This method involves reducing variance by forcing more order onto the random number stream used as input As the simplest example,
Computer Graphics Inf4/MSc Computer Graphics Lecture Notes #16 Image-Based Lighting.
Direct Illumination with Lazy Visibility Evaluation David Hart Philip Dutré Donald P. Greenberg Cornell University SIGGRAPH 99.
01/24/05© 2005 University of Wisconsin Last Time Raytracing and PBRT Structure Radiometric quantities.
01/28/05© 2005 University of Wisconsin Last Time Improving Monte Carlo Efficiency.
Image-Based Rendering from a Single Image Kim Sang Hoon Samuel Boivin – Andre Gagalowicz INRIA.
02/25/05© 2005 University of Wisconsin Last Time Meshing Volume Scattering Radiometry (Adsorption and Emission)
1 Lesson 3: Choosing from distributions Theory: LLN and Central Limit Theorem Theory: LLN and Central Limit Theorem Choosing from distributions Choosing.
-Global Illumination Techniques
Monte Carlo I Previous lecture Analytical illumination formula This lecture Numerical evaluation of illumination Review random variables and probability.
Capturing light Source: A. Efros.
Efficient Rendering of Local Subsurface Scattering Tom Mertens 1, Jan Kautz 2, Philippe Bekaert 1, Frank Van Reeth 1, Hans-Peter Seidel
Path Integral Formulation of Light Transport
Fourier Depth of Field Cyril Soler, Kartic Subr, Frédo Durand, Nicolas Holzschuch, François Sillion INRIA, UC Irvine, MIT CSAIL.
Photo-realistic Rendering and Global Illumination in Computer Graphics Spring 2012 Stochastic Path Tracing Algorithms K. H. Ko School of Mechatronics Gwangju.
1 Rendering translucent materials using SSS Implemented by João Pedro Jorge & Willem Frishert.
Rendering Synthetic Objects into Real Scenes: Bridging Traditional and Image-based Graphics with Global Illumination and High Dynamic Range Photography.
Optimally Combining Sampling Techniques for Monte Carlo Rendering Eric Veach and Leonidas J. Guibas Computer Science Department Stanford University SIGGRAPH.
Monte-Carlo Ray Tracing and
Pure Path Tracing: the Good and the Bad Path tracing concentrates on important paths only –Those that hit the eye –Those from bright emitters/reflectors.
02/12/03© 2003 University of Wisconsin Last Time Intro to Monte-Carlo methods Probability.
Photo-realistic Rendering and Global Illumination in Computer Graphics Spring 2012 Stochastic Path Tracing Algorithms K. H. Ko School of Mechatronics Gwangju.
Monte Carlo Path Tracing
University of Texas at Austin CS395T - Advanced Image Synthesis Spring 2007 Don Fussell Photon Mapping and Irradiance Caching.
01/26/05© 2005 University of Wisconsin Last Time Raytracing and PBRT Structure Radiometric quantities.
Global Illumination (3) Path Tracing. Overview Light Transport Notation Path Tracing Photon Mapping.
02/9/05© 2005 University of Wisconsin Last Time Lights Next assignment – Implement Kubelka-Munk as a BSDF.
PATH INTEGRAL FORMULATION OF LIGHT TRANSPORT Jaroslav Křivánek Charles University in Prague
Computer graphics III – Multiple Importance Sampling Jaroslav Křivánek, MFF UK
Computer graphics III – Monte Carlo integration II Jaroslav Křivánek, MFF UK
Interactive Rendering of Translucent Deformable Objects Tom Mertens 1, Jan Kautz 2, Philippe Bekaert 1, Hans-Peter Seidel 2, Frank Van Reeth
Computer graphics III – Path tracing Jaroslav Křivánek, MFF UK
Introduction to Monte Carlo Method
Monte Carlo Integration
Sampling and Reconstruction of Visual Appearance
Distributed Ray Tracing
Digital Image Synthesis Yung-Yu Chuang
Energy Preserving Non-linear Filters
Ray Tracing via Markov Chain Monte-Carlo Method
Metropolis light transport
© 2002 University of Wisconsin
© 2005 University of Wisconsin
Primary Sample Space Path Guiding
Path Tracing (some material from University of Wisconsin)
Monte Carol Integration
Distributed Ray Tracing
Efficient Importance Sampling Techniques for the Photon Map
Monte Carlo I Previous lecture Analytical illumination formula
Monte Carlo Path Tracing
Monte Carlo Rendering Central theme is sampling:
VQMC J. Planelles.
Fourier Transform of Boundaries
Lesson 4: Application to transport distributions
Distributed Ray Tracing
Monte Carlo Path Tracing and Caching Illumination
Photon Density Estimation using Multiple Importance Sampling
Presentation transcript:

Homework #3 Environment Lights Digital Image Synthesis Yu-Ting Wu

The brighter pixels (lights) have higher probability to be sampled Project description Pbrt uses run-time importance sampling to render scenes with environment lights The details will be given when talking about Monte Carlo integration In this homework, you are asked to provide an alternative approach The brighter pixels (lights) have higher probability to be sampled

Project description Represent the environment map with a set of point (directional) lights A light probe image is subdivided into 64 equal-energy regions. A point light is created for each region at its centroid.

Project description Implement the “MedianCut” algorithm in pbrt There are three functions you should modify: Constructor Do MedianCut algorithm to generate a set of lights with roughly equal energy Sample_L When pbrt asks a sample from environment light, uniformly select one from all lights and return its direction, intensity, and PDF IsDeltaLight Tell pbrt whether this light can be sampled or not? For any other member functions, you can leave them alone at this time

Importance sampling In rendering, one important question is how to determine the sample distributions Importance sampling: If the shape of sampling distribution is close to the shape of integrand, variance is reduced In our case Sample_L Sample_f

Multiple importance sampling Sample light or sample BRDF only cannot adapt to all configurations Multiple importance sampling: Generate one sample using Sample_L and another using Sample_f, then combine the sample results Better than either of the two strategies alone Sample Light Sample BRDF Multiple Importance Sampling

Multiple importance sampling Codes for estimating direct lighting in pbrt:

Multiple importance sampling

Multiple importance sampling IsDeltaLight In homework #3, since we transform the environment to a set of directional lights which cannot be sampled, we should return true to avoid “BRDF importance sampling” Sample_L Pbrt only asks one sample at a time. In homework #3, all lights have roughly equal energy. We can simply random choose one from n lights and return PDF with 1/n

FAQ The rendered images have different radiance scale to the reference ones Each pixel in the environment map represents an area on unit sphere Remember to scale the light intensity with the areas (solid angles) width  2PI w HeightPI h

Related papers Variance Minimization Light Probe Sampling K. Viriyothai and P. Debevec SIGGRAPH 2009 Poster Structured Importance Sampling of Environment Maps S. Agarwal, R. Ramamoorthi, S. Belongie, and H. W. Jensen SIGGRAPH 2002 Efficient Illumination by High Dynamic Range Images T. Kolling and A. Keller EGSR 2003 Fast Hierarchical Importance Sampling with Blue Noise Properties V. Ostromoukhov, C. Donohue, and P. –M. Hodoin SIGGRAPH 2004