Weighted Importance Sampling Techniques for Monte Carlo Radiosity
This paper presents weighted importance sampling techniques for Monte Carlo form factor computation and for stochastic Jacobi radiosity system solution. Weighted importance sampling is a generalisation of importance sampling. The basic idea is to compute a-posteriori a correction factor to the importance sampling estimates, based on sample weights accumulated during sampling. With proper weights, the correction factor will compensate for statistical fluctuations and lead to a lower mean square error. Although weighted importance sampling is a simple extension to importance sampling, our experiments indicate that it can lead to a substantial reduction of the error at a very low additional computation and storage cost.
KeywordsForm Factor Mean Square Error Importance Sampling Local Line Spherical Triangle
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