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Efficient quadrature rules for illumination integrals : from quasi Monte Carlo to Bayesian Monte Carlo /

Bibliographic Details
Main Authors:	Marques, Ricardo (Author), Bouatouch, K. (Kadi), 1950- (Author), Bouville, C. (Christian), 1949- (Author), Santos, Luis Paulo (Author)
Format:	eBook
Language:	English
Published:	San Rafael, California (1537 Fourth Street, San Rafael, CA 94901 USA) : Morgan & Claypool, 2015.
Series:	Synthesis lectures in computer graphics and animation ; # 19.
Subjects:	Image processing > Digital techniques. Monte Carlo method. Spherical functions. Bayesian Monte Carlo Monte Carlo methods Quasi-Monte Carlo global illumination photorealistic rendering raytracing spherical Gaussian spherical integration spherical sampling Electronic books.
Online Access:	Connect to the full text of this electronic book

Table of Contents:

1. Introduction
1.1 The global illumination problem
1.2 Illumination integral evaluation
1.3 Motivation
1.4 Book overview
2. Spherical Fibonacci point sets for QMC estimates of illumination integrals
2.1 Introduction
2.2 Background
2.2.1 QMC on the unit square
2.2.2 QMC rules on the unit spherE
2.2.3 QMC point sets
2.2.4 Hemispherical projections
2.2.5 Summary
2.3 Spherical Fibonacci point sets
2.4 QMC for illumination integrals
2.5 Results
2.5.1 Experimental setup
2.5.2 Predicting the estimate error
2.5.3 Experimental estimate error
2.6 Conclusion
3. Bayesian Monte Carlo for global illumination
3.1 Introduction and motivation
3.2 Representing a function using a smooth model
3.2.1 Linear basis functions model
3.2.2 Bayesian regression
3.3 Bayesian Monte Carlo
3.3.1 BMC quadrature equations
3.3.2 Reducing the number of hyperparameters
3.3.3 Summary
3.4 Applying BMC to global illumination
3.4.1 Spherical Gaussians for fast quadrature computation
3.4.2 Prior GP: a global model with local adaptation
3.4.3 Optimal samples set for illumination integrals
3.4.4 From the hemisphere to the Gaussian lobe
3.4.5 Precomputations
3.4.6 The rendering algorithm
3.5 Results
3.5.1 Experimental environment
3.5.2 Hyperparameters learning
3.5.3 Comparison: BMC vs. QMC
3.5.4 Skipping the learning step
3.6 Conclusion
A. Posterior distribution
Bibliography
Authors' biographies.