Abstract
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.
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Bekaert, P., Sbert, M., Willems, Y.D. (2000). Weighted Importance Sampling Techniques for Monte Carlo Radiosity. In: Péroche, B., Rushmeier, H. (eds) Rendering Techniques 2000. EGSR 2000. Eurographics. Springer, Vienna. https://doi.org/10.1007/978-3-7091-6303-0_4
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DOI: https://doi.org/10.1007/978-3-7091-6303-0_4
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