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Unbiased and consistent rendering using biased estimators
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AI
DOI：10.1145/3528223.3530160.png)
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Abstract
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We introduce a general framework for transforming biased estimators into unbiased and consistent estimators for the same quantity. We show how several existing unbiased and consistent estimation strategies in rendering are special cases of this framework, and are part of a broader debiasing principle. We provide a recipe for constructing estimators using our generalized framework and demonstrate its applicability by developing novel unbiased forms of transmittance estimation, photon mapping, and finite differences.
Keywords:
Monte Carlo
infinite series
Taylor series
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9.5
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4.8K
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