Corpus ID: 8855490

Comparing nite and biased in nite path length shootingrandom walk estimators for

  title={Comparing nite and biased in nite path length shootingrandom walk estimators for},
  author={radiosityMateu Sbert and Alex BrusiyAbstract},
In this paper we compare the best shooting random walk estimator with expected nite path length and the estimator resulting of biasing the innnite one. Heuristic formulae for the Mean Square Error of both estimators are given, and based on them a formula for the relative eeciency of both estimators is presented. The results are contrasted with diierent tests. The formulae for the MSE are also useful to know a priori the number of paths (or particles) needed to obtain a given error. 


Variance of two in nite path length random walk radiosity estimators
The error in an unbiased Monte Carlo method is characterized by the variance. By knowing the variance of different Monte Carlo estimators for Radiosity (and also their cost) we should be able toExpand
Error and Complexity of Random Walk Monte Carlo Radiosity
  • M. Sbert
  • Computer Science
  • IEEE Trans. Vis. Comput. Graph.
  • 1997
The author shows that the shooting method exhibits a lower complexity than the gathering one, and under some constraints, it has a linear complexity, improvement over a previous result that pointed to an O(n log n) complexity. Expand
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  • R. Rubinstein
  • Computer Science, Mathematics
  • Wiley series in probability and mathematical statistics
  • 1981
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Importance-driven Monte Carlo Light Tracing
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  • P. Shirley
  • Computer Science, Mathematics
  • Comput. Graph.
  • 1992
A proof is given that the expected number of rays required to produce a statistical radiosity solution below a specified variance for N zones is O(N), which is well below a predefined threshold. Expand
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