Global multipath Monte Carlo algorithms for radiosity

Sbert, Mateu; Pueyo, Xavier; Neumann, Lázló; Pergathofer, Werner

doi:10.1007/bf01782104

Cited by 26 publications

(30 citation statements)

References 5 publications

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“…It is a finite-element method that uses Monte Carlo estimates of the diffuse light transport [60]. Global lines are lines that are cast independently from surface positions, with a uniform density all over the scene.…”

Section: Multi-frame Lighting Methodsmentioning

confidence: 99%

State of the Art in Global Illumination for Interactive Applications and High‐quality Animations

Damez

Dmitriev

2003

Computer Graphics Forum

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Global illumination algorithms are regarded as computationally intensive. This cost is a practical problem when producing animations or when interactions with complex models are required. Several algorithms have been proposed to address this issue. Roughly, two families of methods can be distinguished. The first one aims at providing interactive feedback for lighting design applications. The second one gives higher priority to the quality of results, and therefore relies on offline computations. Recently, impressive advances have been made in both categories. In this report, we present a survey and classification of the most up-to-date of these methods.

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Section: Multi-frame Lighting Methodsmentioning

confidence: 99%

State of the Art in Global Illumination for Interactive Applications and High‐quality Animations

Damez

Dmitriev

2003

Computer Graphics Forum

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“…If the directions ωÀ is sampled from a uniform distribution, then according to equation (26) the expectation value of the application of this operator is…”

Section: Ray-bundle Based Transfermentioning

confidence: 99%

Stochastic Iteration for Non‐diffuse Global Illumination

Szirmay‐Kalos

Benedek

1999

Computer Graphics Forum

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This paper presents a single‐pass, view‐dependent method to solve the rendering equation, using a stochastic iterational scheme where the transport operator is selected randomly in each iteration. The requirements of convergence are given for the general case. To demonstrate the basic idea, a very simple,continuous random transport operator is examined, which gives back the light tracing algorithm incorporating Russian roulette. Then, a new mixed continuous and finite‐element based iteration method is proposed, which uses ray‐bundles to transfer the radiance in a single random direction. The resulting algorithm is fast, it provides initial results in seconds and accurate solutions in minutes and does not suffer from the error accumulation problem and the high memory demand of other finite‐element and hierarchical approaches.

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“…A particle exits from this source according to the Form-Factors probability (simulated selecting a random exit point and a random direction [15]), and goes to patch j with probability F sj . Then it survives or dies according to the probabilities (R j , 1 -R j ).…”

Section: Estimatormentioning

confidence: 99%

“…It must also be remarked that apart from random walk Monte Carlo radiosity (or rather, discrete random walk Monte Carlo radiosity) other Monte Carlo radiosity methods exist, such as the Stochastic Radiosity method [11], global Monte Carlo methods [15], and others. Path-tracing [9], and even distributed ray-tracing [4], [19], [20], can be considered as the limiting case of gathering random walk for the nondiscrete case.…”

Section: Introductionmentioning

confidence: 99%

Error and complexity of random walk Monte Carlo radiosity

Sbert

1997

IEEE Trans. Visual. Comput. Graphics

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Abstract-In this paper, we study the error and complexity of the discrete random walk Monte Carlo technique for Radiosity, both the shooting and gathering methods. We show that the shooting method exhibits a lower complexity than the gathering one, and under some constraints, it has a linear complexity. This is an improvement over a previous result that pointed to an 2(n log n) complexity. We give and compare three unbiased estimators for each method, and obtain closed forms and bounds for their variances. We also bound the expected value of the Mean Square Error (MSE). Some of the results obtained are also shown to be valid for the nondiscrete gathering case. We also give bounds for the variances and MSE for the infinite path length estimators; these bounds might be useful in the study of the biased estimators resulting of cutting off the infinite path.

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Global multipath Monte Carlo algorithms for radiosity

Cited by 26 publications

References 5 publications

State of the Art in Global Illumination for Interactive Applications and High‐quality Animations

State of the Art in Global Illumination for Interactive Applications and High‐quality Animations

Stochastic Iteration for Non‐diffuse Global Illumination

Error and complexity of random walk Monte Carlo radiosity

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