Robust Monte Carlo Methods for Light Transport Simulation PDF Download

Author: Eric Veach
Publisher:
ISBN:
Category : Computer algorithms
Languages : en
Pages : 444

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Author: Johannes Jendersie
Publisher:
ISBN:
Category :
Languages : en
Pages : 0

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Author: Anton S. Kaplanyan
Publisher:
ISBN:
Category :
Languages : en
Pages :

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Author: Malvin H. Kalos
Publisher: John Wiley & Sons
ISBN: 352740760X
Category : Science
Languages : en
Pages : 217

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Book Description
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.

Author: Toshiya Hachisuka
Publisher:
ISBN: 9781124881843
Category :
Languages : en
Pages : 194

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This dissertation introduces a new light transport simulation framework that significantly expands a class of scene configurations that we can handle. The main contribution is a novel density estimation method, called progressive density estimation, which addresses fundamental limitations of existing density estimation methods. The key feature of progressive density estimation is that the method does not need to store a full set of samples to guarantee convergence to the correct solution. Progressive density estimation led to a new light transport algorithm which can simulate many optical configurations that would be impractical to handle with any existing algorithm. In particular, the algorithm can efficiently simulate complex lighting fixtures from the filament/LED-level for the first time. This dissertation also extends this basic framework of progressive density estimation. We first introduce a practical error estimator for progressive density estimation. This method can estimate how much expected error exists for a given computed solution without needing any knowledge of the correct solution. Since we often need to estimate average illumination over a region that is unknown before computation in computer graphics, we developed stochastic progressive density estimation which provides a simple solution to this problem. This estimator extends progressive density estimation for computing average density over unknown region with provable convergence. In order to improve computational efficiency of the proposed framework, we applied an adaptive Markov chain Monte Carlo method to light transport simulation. With this adaptive algorithm, we can focus computation on only to the visible region. To our knowledge, this is the first application of adaptive Markov chain Monte Carlo methods in light transport simulation. We also propose a novel framework that achieves the adaptive combination of progressive density estimation and other approaches based on Monte Carlo integration. In order to develop this framework, we conducted theoretical analysis of a provably good combination of density estimation methods and Monte Carlo integration. For parallel computation of the proposed framework, we developed a new spatial hashing method. This new hashing algorithm is designed to work correctly regardless of the result of contentions in parallel processes as opposed to avoiding the contentions.

Author: Ronald Cools
Publisher: Springer
ISBN: 3319335073
Category : Mathematics
Languages : en
Pages : 624

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Book Description
This book presents the refereed proceedings of the Eleventh International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing that was held at the University of Leuven (Belgium) in April 2014. These biennial conferences are major events for Monte Carlo and quasi-Monte Carlo researchers. The proceedings include articles based on invited lectures as well as carefully selected contributed papers on all theoretical aspects and applications of Monte Carlo and quasi-Monte Carlo methods. Offering information on the latest developments in these very active areas, this book is an excellent reference resource for theoreticians and practitioners interested in solving high-dimensional computational problems, arising, in particular, in finance, statistics and computer graphics.

Author: Alexander Keller
Publisher: Springer Science & Business Media
ISBN: 3540744967
Category : Mathematics
Languages : en
Pages : 684

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Book Description
This book presents the refereed proceedings of the Seventh International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing, held in Ulm, Germany, in August 2006. The proceedings include carefully selected papers on many aspects of Monte Carlo and quasi-Monte Carlo methods and their applications. They also provide information on current research in these very active areas.

Author: Leland Lavele Carter
Publisher:
ISBN:
Category : Science
Languages : en
Pages : 132

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Author: Art B. Owen
Publisher: Springer
ISBN: 3319914367
Category : Computers
Languages : en
Pages : 476

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Book Description
This book presents the refereed proceedings of the Twelfth International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing that was held at Stanford University (California) in August 2016. These biennial conferences are major events for Monte Carlo and quasi-Monte Carlo researchers. The proceedings include articles based on invited lectures as well as carefully selected contributed papers on all theoretical aspects and applications of Monte Carlo and quasi-Monte Carlo methods. Offering information on the latest developments in these very active areas, this book is an excellent reference resource for theoreticians and practitioners interested in solving high-dimensional computational problems, arising in particular, in finance, statistics, computer graphics and the solution of PDEs.

Author: Wojciech Jarosz
Publisher:
ISBN:
Category :
Languages : en
Pages : 202

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In this dissertation we focus on developing accurate and efficient Monte Carlo methods for synthesizing images containing general participating media. Participating media such as clouds, smoke, and fog are ubiquitous in the world and are responsible for many important visual phenomena which are of interest to computer graphics as well as related fields. When present, the medium participates in lighting interactions by scattering or absorbing photons as they travel through the scene. Though these effects add atmosphere and considerable depth to rendered images they are computationally very expensive to simulate. Most practical solutions make simplifying assumptions about the medium in order to maintain efficiency. Unfortunately, accurate and efficient simulation of light transport in general scattering media is a challenging undertaking. In this dissertation, we address this problem by introducing two complementary techniques. We first turn to the irradiance caching method for surface illumination. Irradiance caching gains efficiency by computing an accurate representation of lighting only at a sparse set of locations and reusing these values through interpolation whenever possible. We derive the mathematical concepts that form the foundation of this approach and analyze its strengths and weaknesses. Drawing inspiration from this algorithm, we then introduce a novel volumetric radiance caching method for efficiently simulating global illumination within participating media. In developing the technique we also introduce efficient methods for evaluating the gradient of the lighting within participating media. Our gradient analysis has immediate applicability for improved interpolation quality in both surface and media-based caching methods. We also develop a novel photon mapping technique for participating media. We present a theoretical reformulation of volumetric photon mapping, which provides significant new insights. This reformulation makes it easier to qualify the error introduced by the radiance estimate but, more importantly, also allows us to develop more efficient rendering techniques. Conventional photon mapping accelerate the computation of lighting at any point in the scene by performing density estimation. In contrast, our reformulation accelerates the computation of accumulated lighting along the length of entire rays. This algorithmic improvement provides for significantly reduced render times and even the potential for real-time visualization of light transport in participating media.