Robust monte carlo methods for light transport simulation
Robust monte carlo methods for light transport simulation
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We propose an efficient final gathering technique using adaptive multiple importance sampling (AMIS) [Cornuet et al. 2009] for a scene containing a highly intense spot of light. AMIS is aimed at optimally recycling past simulations in an iterative importance sampling scheme. The difference to earlier adaptive importance sampling methods is that the past weighting functions are recomputed by multiple importance sampling [Veach 1997] at each iteration. In AMIS, the probability distribution function (PDF) at the tth iteration is parameterized by θt. The next parameter θt+1 is determined by estimating the optimal value from past samples (described in Section 2.2). The performance of AMIS depends on a sampling strategy, i.e. the choice of a PDF. This poster suggests a suitable PDF for final gathering. Our method increases error in some case, however, it is effective in the case of a scene contains a highly intense spot of light compared to the classic method.