Recent Advances in Adaptive Sampling and Reconstruction for Monte Carlo Rendering

Monte Carlo integration is firmly established as the basis for most practical realistic image synthesis algorithms because of its flexibility and generality. However, the visual quality of rendered images often suffers from estimator variance, which appears as visually distracting noise. Adaptive sa...

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Vydáno v:	Computer graphics forum Ročník 34; číslo 2; s. 667 - 681
Hlavní autoři:	Zwicker, M., Jarosz, W., Lehtinen, J., Moon, B., Ramamoorthi, R., Rousselle, F., Sen, P., Soler, C., Yoon, S.-E.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Oxford Blackwell Publishing Ltd 01.05.2015 Wiley
Témata:	Adaptive sampling Algorithms Analysis Categories and Subject Descriptors (according to ACM CCS) Computer graphics Computer Science Computer simulation Graphics I.3.3 [Computer Graphics]: Picture/Image Generation- Display algorithms Image processing systems Monte Carlo methods Monte Carlo simulation Reconstruction Samples Sampling Statistical methods Studies
ISSN:	0167-7055, 1467-8659
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Abstract	Monte Carlo integration is firmly established as the basis for most practical realistic image synthesis algorithms because of its flexibility and generality. However, the visual quality of rendered images often suffers from estimator variance, which appears as visually distracting noise. Adaptive sampling and reconstruction algorithms reduce variance by controlling the sampling density and aggregating samples in a reconstruction step, possibly over large image regions. In this paper we survey recent advances in this area. We distinguish between “a priori” methods that analyze the light transport equations and derive sampling rates and reconstruction filters from this analysis, and “a posteriori” methods that apply statistical techniques to sets of samples to drive the adaptive sampling and reconstruction process. They typically estimate the errors of several reconstruction filters, and select the best filter locally to minimize error. We discuss advantages and disadvantages of recent state‐of‐the‐art techniques, and provide visual and quantitative comparisons. Some of these techniques are proving useful in real‐world applications, and we aim to provide an overview for practitioners and researchers to assess these approaches. In addition, we discuss directions for potential further improvements.
AbstractList	Monte Carlo integration is firmly established as the basis for most practical realistic image synthesis algorithms because of its flexibility and generality. But variance, which appears as visually distracting noise in rendered images, is a persistent challenge. Adaptive sampling and reconstruction algorithms reduce variance by controlling the sampling density and aggregating samples in a reconstruction step, possibly over large image regions. In this paper we survey recent advances in this area. We distinguish " a priori " methods that analyze the light transport equations and derive sampling rates and reconstruction filters from this analysis, and " a posteriori " methods that obtain error estimates, usually with statistical techniques, from sets of samples. They typically estimate the errors of several reconstruction filters, and select the best filter locally to minimize error. We discuss advantages and disadvantages of recent state-of-the-art techniques, and provide visual and quantitative comparisons. Some of these techniques are proving useful in real-world applications, and we aim to provide an overview for practitioners and researchers to assess these approaches. In addition, we discuss directions for potential further improvements. Categories and Subject Descriptors (according to ACM CCS): I.3.3 [Computer Graphics]: Picture/Image Generation— Display algorithms Monte Carlo methods are firmly established as the most practical methods for realistic image synthesis by numerically solving the rendering equation. Even simple Monte Carlo rendering algorithms, such as path tracing, come with a number of very desireable properties including unbiasedness, consistency, and applicability to most scene configurations that are relevant in practice. On the other hand, computation times to obtain visually satisfactory results without noticeable noise artifacts are often in the minutes and hours. Therefore , researchers have proposed a wide variety of noise or variance reduction strategies over the years, from different path sampling strategies (importance sampling, bidirectional techniques, Metropolis sampling) to statistical techniques (density estimation, control variates), or signal processing methods (frequency analysis, non-linear filtering), to name the most prominent ones. In this paper, we survey recent advances in adaptive sampling and reconstruction, which have proven very effective and are making Monte Carlo techniques more practical. Monte Carlo integration is firmly established as the basis for most practical realistic image synthesis algorithms because of its flexibility and generality. However, the visual quality of rendered images often suffers from estimator variance, which appears as visually distracting noise. Adaptive sampling and reconstruction algorithms reduce variance by controlling the sampling density and aggregating samples in a reconstruction step, possibly over large image regions. In this paper we survey recent advances in this area. We distinguish between “a priori” methods that analyze the light transport equations and derive sampling rates and reconstruction filters from this analysis, and “a posteriori” methods that apply statistical techniques to sets of samples to drive the adaptive sampling and reconstruction process. They typically estimate the errors of several reconstruction filters, and select the best filter locally to minimize error. We discuss advantages and disadvantages of recent state‐of‐the‐art techniques, and provide visual and quantitative comparisons. Some of these techniques are proving useful in real‐world applications, and we aim to provide an overview for practitioners and researchers to assess these approaches. In addition, we discuss directions for potential further improvements.
Author	Ramamoorthi, R. Rousselle, F. Zwicker, M. Moon, B. Soler, C. Jarosz, W. Yoon, S.-E. Lehtinen, J. Sen, P.
Author_xml	– sequence: 1 givenname: M. surname: Zwicker fullname: Zwicker, M. organization: University of Bern – sequence: 2 givenname: W. surname: Jarosz fullname: Jarosz, W. organization: Disney Research Zürich – sequence: 3 givenname: J. surname: Lehtinen fullname: Lehtinen, J. organization: Aalto University and NVIDIA – sequence: 4 givenname: B. surname: Moon fullname: Moon, B. organization: KAIST (Korea Advanced Institute of Science and Technology) – sequence: 5 givenname: R. surname: Ramamoorthi fullname: Ramamoorthi, R. organization: University of California, San Diego – sequence: 6 givenname: F. surname: Rousselle fullname: Rousselle, F. organization: Disney Research Zürich – sequence: 7 givenname: P. surname: Sen fullname: Sen, P. organization: University of California, Santa Barbara – sequence: 8 givenname: C. surname: Soler fullname: Soler, C. organization: INRIA Rhône-Alpes – sequence: 9 givenname: S.-E. surname: Yoon fullname: Yoon, S.-E. organization: KAIST (Korea Advanced Institute of Science and Technology)
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Snippet	Monte Carlo integration is firmly established as the basis for most practical realistic image synthesis algorithms because of its flexibility and generality....
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SubjectTerms	Adaptive sampling Algorithms Analysis Categories and Subject Descriptors (according to ACM CCS) Computer graphics Computer Science Computer simulation Graphics I.3.3 [Computer Graphics]: Picture/Image Generation- Display algorithms Image processing systems Monte Carlo methods Monte Carlo simulation Reconstruction Samples Sampling Statistical methods Studies
Title	Recent Advances in Adaptive Sampling and Reconstruction for Monte Carlo Rendering
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