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20210728125145.0 |
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m eo d |
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cr cn |||m|||a |
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150620s2015 caua foab 000 0 eng d |
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|a 9781627057707
|q ebook
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|z 9781627057691
|q print
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|a 10.2200/S00649ED1V01Y201505CGR019
|2 doi
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|a (CaBNVSL)swl00405128
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|a (OCoLC)911246019
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|a 201505CGR019
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|a 5140259
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|a CaBNVSL
|b eng
|e rda
|c CaBNVSL
|d CaBNVSL
|d UtOrBLW
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4 |
|a TA1637
|b .M274 2015
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|a 621.367
|2 23
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| 100 |
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|a Marques, Ricardo,
|e author.
|0 http://id.loc.gov/authorities/names/no2015108144
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| 245 |
1 |
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|a Efficient quadrature rules for illumination integrals :
|b from quasi Monte Carlo to Bayesian Monte Carlo /
|c Ricardo Marques, Christian Bouville, Luís Paulo Santos, and Kadi Bouatouch.
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| 264 |
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1 |
|a San Rafael, California (1537 Fourth Street, San Rafael, CA 94901 USA) :
|b Morgan & Claypool,
|c 2015.
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| 300 |
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|a 1 online resource (x, 82 pages) :
|b illustrations.
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| 336 |
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 490 |
1 |
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|a Synthesis lectures on computer graphics and animation,
|x 1933-9003 ;
|v # 19
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| 504 |
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|a Includes bibliographical references (pages 75-79).
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|a 1. Introduction -- 1.1 The global illumination problem -- 1.2 Illumination integral evaluation -- 1.3 Motivation -- 1.4 Book overview --
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| 505 |
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|a 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 --
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| 505 |
8 |
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|a 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 --
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| 505 |
8 |
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|a A. Posterior distribution -- Bibliography -- Authors' biographies.
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| 506 |
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|a Abstract freely available; full-text restricted to subscribers or individual document purchasers.
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| 520 |
3 |
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|a Rendering photorealistic images is a costly process which can take up to several days in the case of high quality images. In most cases, the task of sampling the incident radiance function to evaluate the illumination integral is responsible for an important share of the computation time. Therefore, to reach acceptable rendering times, the illumination integral must be evaluated using a limited set of samples. Such a restriction raises the question of how to obtain the most accurate approximation possible with such a limited set of samples. One must thus ensure that sampling produces the highest amount of information possible by carefully placing and weighting the limited set of samples. Furthermore, the integral evaluation should take into account not only the information brought by sampling but also possible information available prior to sampling, such as the integrand smoothness. This idea of sparse information and the need to fully exploit the little information available is present throughout this book. The presented methods correspond to the state-of-the-art solutions in computer graphics, and take into account information which had so far been underexploited (or even neglected) by the previous approaches. The intended audiences are Ph.D. students and researchers in the field of realistic image synthesis or global illumination algorithms, or any person with a solid background in graphics and numerical techniques.
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| 530 |
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|a Also available in print.
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| 538 |
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|a Mode of access: World Wide Web.
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| 538 |
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|a System requirements: Adobe Acrobat Reader.
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| 588 |
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|a Title from PDF title page (viewed on June 20, 2015).
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| 650 |
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0 |
|a Image processing
|x Digital techniques.
|0 http://id.loc.gov/authorities/subjects/sh85064447
|
| 650 |
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0 |
|a Monte Carlo method.
|0 http://id.loc.gov/authorities/subjects/sh85087032
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| 650 |
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0 |
|a Spherical functions.
|0 http://id.loc.gov/authorities/subjects/sh85052349
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| 653 |
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|a Bayesian Monte Carlo
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| 653 |
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|a Monte Carlo methods
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| 653 |
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|a Quasi-Monte Carlo
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| 653 |
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|a global illumination
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| 653 |
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|a photorealistic rendering
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| 653 |
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|a raytracing
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| 653 |
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|a spherical Gaussian
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| 653 |
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|a spherical integration
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| 653 |
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|a spherical sampling
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| 655 |
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7 |
|a Electronic books.
|2 local
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| 700 |
1 |
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|a Bouatouch, K.
|q (Kadi),
|d 1950-
|e author.
|0 http://id.loc.gov/authorities/names/n91064615
|
| 700 |
1 |
|
|a Bouville, C.
|q (Christian),
|d 1949-
|e author.
|0 http://id.loc.gov/authorities/names/n91064619
|
| 700 |
1 |
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|a Santos, Luis Paulo,
|e author.
|0 http://id.loc.gov/authorities/names/nb2006026673
|
| 730 |
0 |
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|a Synthesis digital library of engineering and computer science.
|0 http://id.loc.gov/authorities/names/n2016188085
|
| 776 |
1 |
8 |
|i Print version:
|z 9781627057691
|
| 830 |
|
0 |
|a Synthesis lectures in computer graphics and animation ;
|v # 19.
|x 1933-9003.
|0 http://id.loc.gov/authorities/names/no2008027907
|
| 856 |
4 |
0 |
|u http://proxy.library.tamu.edu/login?url=https://dx.doi.org/10.2200/S00649ED1V01Y201505CGR019
|z Connect to the full text of this electronic book
|t 0
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| 999 |
f |
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|s 6d4e0208-ba7e-36b1-8ec5-6119fd6b2126
|i fd9d7df6-eaa1-334a-9175-e77d98ae84ff
|t 0
|
| 952 |
f |
f |
|a Texas A&M University
|b College Station
|c Electronic Resources
|d Available Online
|t 0
|e TA1637 .M274 2015
|h Library of Congress classification
|
| 998 |
f |
f |
|a TA1637 .M274 2015
|t 0
|l Available Online
|