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008			220601s2015 sz | o |||| 0|eng d
020			_a9783031795671
024	7		_a10.1007/978-3-031-79567-1 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTA1637 _b2015 EB
100	1		_aMarques, Ricardo _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9688373
245	1	0	_aEfficient Quadrature Rules for Illumination Integrals : _bFrom Quasi Monte Carlo to Bayesian Monte Carlo _cby Ricardo Marques, Christian Bouville, Luís Paulo Santos, Kadi Bouatouch
250			_a1st edition 2015
264		1	_aCham _bSpringer International Publishing _c2015
300			_a1 recurso en línea (X, 82 páginas)
336			_aTexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Computer Graphics and Animation _x1933-9003
505	0		_aIntroduction -- Spherical Fibonacci Point Sets for QMC Estimates of Illumination Integrals -- Bayesian Monte Carlo for Global Illumination -- Bibliography -- Authors' Biographies.
520			_aRendering 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.
988			_aSynthesis Collection of Technology_2015
650		7	_2embne _9413188 _aProceso digital de imágenes
650		7	_2embne _9681471 _aMétodo de Monte Carlo
700	1		_aBouville, C. _q(Christian) _d1949- _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9688374
700	1		_aSantos, Luis Paulo _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9688375
700	1		_aBouatouch, K. _q(Kadi) _d1950- _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9688376
776	0	8	_iPrinted edition: _z9783031795664
776	0	8	_iPrinted edition: _z9783031795688
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-79567-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b05/2023 _dz _eb _zSI