Download or read book Sequential Monte Carlo Methods for Physically Based Rendering written by Shao Hua Fan and published by . This book was released on 2006 with total page 164 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Sequential Monte Carlo Methods in Practice written by Arnaud Doucet and published by Springer Science & Business Media. This book was released on 2013-03-09 with total page 590 pages. Available in PDF, EPUB and Kindle. Book excerpt: Monte Carlo methods are revolutionizing the on-line analysis of data in many fileds. They have made it possible to solve numerically many complex, non-standard problems that were previously intractable. This book presents the first comprehensive treatment of these techniques.
Download or read book Physically Based Rendering fourth edition written by Matt Pharr and published by MIT Press. This book was released on 2023-05-30 with total page 1274 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive update of the leading-edge computer graphics textbook that sets the standard for physically-based rendering in the industry and the field, with new material on GPU ray tracing. Photorealistic computer graphics are ubiquitous in today’s world, widely used in movies and video games as well as product design and architecture. Physically-based approaches to rendering, where an accurate modeling of the physics of light scattering is at the heart of image synthesis, offer both visual realism and predictability. Now in a comprehensively updated new edition, this best-selling computer graphics textbook sets the standard for physically-based rendering in the industry and the field. Physically Based Rendering describes both the mathematical theory behind a modern photorealistic rendering system as well as its practical implementation. A method known as literate programming combines human-readable documentation and source code into a single reference that is specifically designed to aid comprehension. The book’s leading-edge algorithms, software, and ideas—including new material on GPU ray tracing—equip the reader to design and employ a full-featured rendering system capable of creating stunning imagery. This essential text represents the future of real-time graphics. Detailed and rigorous but accessible approach guides readers all the way from theory to practical software implementation Fourth edition features new chapter on GPU ray tracing essential for game developers The premier reference for professionals learning about and working in the field Won its authors a 2014 Academy Award for Scientific and Technical Achievement Includes a companion site complete with source code
Download or read book Physically Based Rendering of Synthetic Objects in Real Environments written by Joel Kronander and published by Linköping University Electronic Press. This book was released on 2015-11-10 with total page 157 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis presents methods for photorealistic rendering of virtual objects so that they can be seamlessly composited into images of the real world. To generate predictable and consistent results, we study physically based methods, which simulate how light propagates in a mathematical model of the augmented scene. This computationally challenging problem demands both efficient and accurate simulation of the light transport in the scene, as well as detailed modeling of the geometries, illumination conditions, and material properties. In this thesis, we discuss and formulate the challenges inherent in these steps and present several methods to make the process more efficient. In particular, the material contained in this thesis addresses four closely related areas: HDR imaging, IBL, reflectance modeling, and efficient rendering. The thesis presents a new, statistically motivated algorithm for HDR reconstruction from raw camera data combining demosaicing, denoising, and HDR fusion in a single processing operation. The thesis also presents practical and robust methods for rendering with spatially and temporally varying illumination conditions captured using omnidirectional HDR video. Furthermore, two new parametric BRDF models are proposed for surfaces exhibiting wide angle gloss. Finally, the thesis also presents a physically based light transport algorithm based on Markov Chain Monte Carlo methods that allows approximations to be used in place of exact quantities, while still converging to the exact result. As illustrated in the thesis, the proposed algorithm enables efficient rendering of scenes with glossy transfer and heterogenous participating media.
Download or read book Fast Sequential Monte Carlo Methods for Counting and Optimization written by Reuven Y. Rubinstein and published by John Wiley & Sons. This book was released on 2013-11-13 with total page 177 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive account of the theory and application of Monte Carlo methods Based on years of research in efficient Monte Carlo methods for estimation of rare-event probabilities, counting problems, and combinatorial optimization, Fast Sequential Monte Carlo Methods for Counting and Optimization is a complete illustration of fast sequential Monte Carlo techniques. The book provides an accessible overview of current work in the field of Monte Carlo methods, specifically sequential Monte Carlo techniques, for solving abstract counting and optimization problems. Written by authorities in the field, the book places emphasis on cross-entropy, minimum cross-entropy, splitting, and stochastic enumeration. Focusing on the concepts and application of Monte Carlo techniques, Fast Sequential Monte Carlo Methods for Counting and Optimization includes: Detailed algorithms needed to practice solving real-world problems Numerous examples with Monte Carlo method produced solutions within the 1-2% limit of relative error A new generic sequential importance sampling algorithm alongside extensive numerical results An appendix focused on review material to provide additional background information Fast Sequential Monte Carlo Methods for Counting and Optimization is an excellent resource for engineers, computer scientists, mathematicians, statisticians, and readers interested in efficient simulation techniques. The book is also useful for upper-undergraduate and graduate-level courses on Monte Carlo methods.
Download or read book Robust Monte Carlo Methods for Light Transport Simulation written by Eric Veach and published by . This book was released on 1998 with total page 444 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Physically Based Rendering written by Matt Pharr and published by Morgan Kaufmann. This book was released on 2016-09-30 with total page 1270 pages. Available in PDF, EPUB and Kindle. Book excerpt: Physically Based Rendering: From Theory to Implementation, Third Edition, describes both the mathematical theory behind a modern photorealistic rendering system and its practical implementation. Through a method known as 'literate programming', the authors combine human-readable documentation and source code into a single reference that is specifically designed to aid comprehension. The result is a stunning achievement in graphics education. Through the ideas and software in this book, users will learn to design and employ a fully-featured rendering system for creating stunning imagery. This completely updated and revised edition includes new coverage on ray-tracing hair and curves primitives, numerical precision issues with ray tracing, LBVHs, realistic camera models, the measurement equation, and much more. It is a must-have, full color resource on physically-based rendering. Presents up-to-date revisions of the seminal reference on rendering, including new sections on bidirectional path tracing, numerical robustness issues in ray tracing, realistic camera models, and subsurface scattering Provides the source code for a complete rendering system allowing readers to get up and running fast Includes a unique indexing feature, literate programming, that lists the locations of each function, variable, and method on the page where they are first described Serves as an essential resource on physically-based rendering
Download or read book Physically Based Rendering written by Matt Pharr and published by Morgan Kaufmann. This book was released on 2010-06-28 with total page 1201 pages. Available in PDF, EPUB and Kindle. Book excerpt: This updated edition describes both the mathematical theory behind a modern photorealistic rendering system as well as its practical implementation. Through the ideas and software in this book, designers will learn to design and employ a full-featured rendering system for creating stunning imagery. Includes a companion site complete with source code for the rendering system described in the book, with support for Windows, OS X, and Linux.
Download or read book Monte Carlo Methods written by Malvin H. Kalos and published by John Wiley & Sons. This book was released on 2008-10-20 with total page 217 pages. Available in PDF, EPUB and Kindle. Book excerpt: This introduction to Monte Carlo methods seeks to identify and study the unifying elements that underlie their effective application. Initial chapters provide a short treatment of the probability and statistics needed as background, enabling those without experience in Monte Carlo techniques to apply these ideas to their research. The book focuses on two basic themes: The first is the importance of random walks as they occur both in natural stochastic systems and in their relationship to integral and differential equations. The second theme is that of variance reduction in general and importance sampling in particular as a technique for efficient use of the methods. Random walks are introduced with an elementary example in which the modeling of radiation transport arises directly from a schematic probabilistic description of the interaction of radiation with matter. Building on this example, the relationship between random walks and integral equations is outlined. The applicability of these ideas to other problems is shown by a clear and elementary introduction to the solution of the Schrodinger equation by random walks. The text includes sample problems that readers can solve by themselves to illustrate the content of each chapter. This is the second, completely revised and extended edition of the successful monograph, which brings the treatment up to date and incorporates the many advances in Monte Carlo techniques and their applications, while retaining the original elementary but general approach.
Download or read book Monte Carlo Methods written by Adrian Barbu and published by Springer Nature. This book was released on 2020-02-24 with total page 433 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book seeks to bridge the gap between statistics and computer science. It provides an overview of Monte Carlo methods, including Sequential Monte Carlo, Markov Chain Monte Carlo, Metropolis-Hastings, Gibbs Sampler, Cluster Sampling, Data Driven MCMC, Stochastic Gradient descent, Langevin Monte Carlo, Hamiltonian Monte Carlo, and energy landscape mapping. Due to its comprehensive nature, the book is suitable for developing and teaching graduate courses on Monte Carlo methods. To facilitate learning, each chapter includes several representative application examples from various fields. The book pursues two main goals: (1) It introduces researchers to applying Monte Carlo methods to broader problems in areas such as Computer Vision, Computer Graphics, Machine Learning, Robotics, Artificial Intelligence, etc.; and (2) it makes it easier for scientists and engineers working in these areas to employ Monte Carlo methods to enhance their research.
Download or read book Advances in Visual Computing written by George Bebis and published by Springer Science & Business Media. This book was released on 2008-11-13 with total page 1216 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two volume set LNCS 5358 and LNCS 5359 constitutes the refereed proceedings of the 4th International Symposium on Visual Computing, ISVC 2008, held in Las Vegas, NV, USA, in December 2008. The 102 revised full papers and 70 poster papers presented together with 56 full and 8 poster papers of 8 special tracks were carefully reviewed and selected from more than 340 submissions. The papers are organized in topical sections on computer graphics, visualization, shape/recognition, video analysis and event recognition, virtual reality, reconstruction, motion, face/gesture, and computer vision applications. The 8 additional special tracks address issues such as object recognition, real-time vision algorithm implementation and application, computational bioimaging and visualization, discrete and computational geometry, soft computing in image processing and computer vision, visualization and simulation on immersive display devices, analysis and visualization of biomedical visual data, as well as image analysis for remote sensing data.
Download or read book Physically Based Rendering fourth edition written by Matt Pharr and published by MIT Press. This book was released on 2023-03-28 with total page 1274 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive update of the leading-edge computer graphics textbook that sets the standard for physically-based rendering in the industry and the field, with new material on GPU ray tracing. Photorealistic computer graphics are ubiquitous in today’s world, widely used in movies and video games as well as product design and architecture. Physically-based approaches to rendering, where an accurate modeling of the physics of light scattering is at the heart of image synthesis, offer both visual realism and predictability. Now in a comprehensively updated new edition, this best-selling computer graphics textbook sets the standard for physically-based rendering in the industry and the field. Physically Based Rendering describes both the mathematical theory behind a modern photorealistic rendering system as well as its practical implementation. A method known as literate programming combines human-readable documentation and source code into a single reference that is specifically designed to aid comprehension. The book’s leading-edge algorithms, software, and ideas—including new material on GPU ray tracing—equip the reader to design and employ a full-featured rendering system capable of creating stunning imagery. This essential text represents the future of real-time graphics. Detailed and rigorous but accessible approach guides readers all the way from theory to practical software implementation Fourth edition features new chapter on GPU ray tracing essential for game developers The premier reference for professionals learning about and working in the field Won its authors a 2014 Academy Award for Scientific and Technical Achievement Includes a companion site complete with source code
Download or read book Monte Carlo Methods in Statistical Physics written by Kurt Binder and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 425 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the seven years since this volume first appeared. there has been an enormous expansion of the range of problems to which Monte Carlo computer simulation methods have been applied. This fact has already led to the addition of a companion volume ("Applications of the Monte Carlo Method in Statistical Physics", Topics in Current Physics. Vol . 36), edited in 1984, to this book. But the field continues to develop further; rapid progress is being made with respect to the implementation of Monte Carlo algorithms, the construction of special-purpose computers dedicated to exe cute Monte Carlo programs, and new methods to analyze the "data" generated by these programs. Brief descriptions of these and other developments, together with numerous addi tional references, are included in a new chapter , "Recent Trends in Monte Carlo Simulations" , which has been written for this second edition. Typographical correc tions have been made and fuller references given where appropriate, but otherwise the layout and contents of the other chapters are left unchanged. Thus this book, together with its companion volume mentioned above, gives a fairly complete and up to-date review of the field. It is hoped that the reduced price of this paperback edition will make it accessible to a wide range of scientists and students in the fields to which it is relevant: theoretical phYSics and physical chemistry , con densed-matter physics and materials science, computational physics and applied mathematics, etc.
Download or read book Anti Aliased Low Discrepancy Samplers for Monte Carlo Estimators in Physically Based Rendering written by Hélène Perrier and published by . This book was released on 2018 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: When you display a 3D object on a computer screen, we transform this 3D scene into a 2D image, which is a set of organized colored pixels. We call Rendering all the process that aims at finding the correct color to give those pixels. This is done by integrating all the light rays coming for every directions that the object's surface reflects back to the pixel, the whole being ponderated by a visibility function. Unfortunately, a computer can not compute an integrand. We therefore have two possibilities to solve this issue: We find an analytical expression to remove the integrand (statistic based strategy). Numerically approximate the equation by taking random samples in the integration domain and approximating the integrand value using Monte Carlo methods. Here we focused on numerical integration and sampling theory. Sampling is a fundamental part of numerical integration. A good sampler should generate points that cover the domain uniformly to prevent bias in the integration and, when used in Computer Graphics, the point set should not present any visible structure, otherwise this structure will appear as artifacts in the resulting image. Furthermore, a stochastic sampler should minimize the variance in integration to converge to a correct approximation using as few samples as possible. There exists many different samplers that we will regroup into two families: Blue Noise samplers, that have a low integration variance while generating unstructured point sets. The issue with those samplers is that they are often slow to generate a pointset. Low Discrepancy samplers, that minimize the variance in integration and are able to generate and enrich a point set very quickly. However, they present a lot of structural artifacts when used in Rendering. Our work aimed at developing hybriod samplers, that are both Blue Noise and Low Discrepancy.
Download or read book Quality Assessment and Variance Reduction in Monte Carlo Rendering Algorithms written by Joss Whittle and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Sequential Monte Carlo Methods for Nonlinear Discrete time Filtering written by Marcelo G. S. Bruno and published by Morgan & Claypool Publishers. This book was released on 2013 with total page 101 pages. Available in PDF, EPUB and Kindle. Book excerpt: In these notes, we introduce particle filtering as a recursive importance sampling method that approximates the minimum-mean-square-error (MMSE) estimate of a sequence of hidden state vectors in scenarios where the joint probability distribution of the states and the observations is non-Gaussian and, therefore, closed-form analytical expressions for the MMSE estimate are generally unavailable. We begin the notes with a review of Bayesian approaches to static (i.e., time-invariant) parameter estimation. In the sequel, we describe the solution to the problem of sequential state estimation in linear, Gaussian dynamic models, which corresponds to the well-known Kalman (or Kalman-Bucy) filter. Finally, we move to the general nonlinear, non-Gaussian stochastic filtering problem and present particle filtering as a sequential Monte Carlo approach to solve that problem in a statistically optimal way. We review several techniques to improve the performance of particle filters, including importance function optimization, particle resampling, Markov Chain Monte Carlo move steps, auxiliary particle filtering, and regularized particle filtering. We also discuss Rao-Blackwellized particle filtering as a technique that is particularly well-suited for many relevant applications such as fault detection and inertial navigation. Finally, we conclude the notes with a discussion on the emerging topic of distributed particle filtering using multiple processors located at remote nodes in a sensor network. Throughout the notes, we often assume a more general framework than in most introductory textbooks by allowing either the observation model or the hidden state dynamic model to include unknown parameters. In a fully Bayesian fashion, we treat those unknown parameters also as random variables. Using suitable dynamic conjugate priors, that approach can be applied then to perform joint state and parameter estimation.
Download or read book Monte Carlo Methods in Statistical Physics written by M. E. J. Newman and published by Clarendon Press. This book was released on 1999-02-11 with total page 490 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an introduction to Monte Carlo simulations in classical statistical physics and is aimed both at students beginning work in the field and at more experienced researchers who wish to learn more about Monte Carlo methods. The material covered includes methods for both equilibrium and out of equilibrium systems, and common algorithms like the Metropolis and heat-bath algorithms are discussed in detail, as well as more sophisticated ones such as continuous time Monte Carlo, cluster algorithms, multigrid methods, entropic sampling and simulated tempering. Data analysis techniques are also explained starting with straightforward measurement and error-estimation techniques and progressing to topics such as the single and multiple histogram methods and finite size scaling. The last few chapters of the book are devoted to implementation issues, including discussions of such topics as lattice representations, efficient implementation of data structures, multispin coding, parallelization of Monte Carlo algorithms, and random number generation. At the end of the book the authors give a number of example programmes demonstrating the applications of these techniques to a variety of well-known models.
