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Unbiased sampling techniques for image synthesis

Kirk, David and Arvo, James (1991) Unbiased sampling techniques for image synthesis. ACM SIGGRAPH Computer Graphics, 25 (4). pp. 153-156. ISSN 0097-8930. doi:10.1145/127719.122735.

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We examine a class of adaptive sampling techniques employed in image synthesis and show that those commonly used for efficient anti-aliasing are statistically biased. This bias is dependent upon the image function being sampled as well as the strategy for determining the number of samples to use. It is most prominent in areas of high contrast and is attributable to early stages of sampling systematically favoring one extreme or the other. If the expected outcome of the entire adaptive sampling algorithm is considered, we find that the bias of the early decisions is still present in the final estimator. We propose an alternative strategy for performing adaptive sampling that is unbiased but potentially more costly. We conclude that it may not always be practical to mitigate this source of bias, but as a source of error it should be considered when high accuracy and image fidelity are a central concern.

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Additional Information:© 1991 ACM. Much of this research was performed while the authors were employed at Apollo Computer and Hewlett-Packard. The authors also wish to thank the anonymous reviewers for their thoughtful and detailed comments.
Subject Keywords:Algorithms, Graphics, Adaptive Sampling, Anti-aliasing, Monte Carlo, Statistical Bias
Issue or Number:4
Classification Code:I.3.7— [Computer Graphics]: Three-Dimensional Graphics and Realism; 1.3.3 —[Computer Graphics]: Picture/Image Generation;
Record Number:CaltechAUTHORS:20161116-153206206
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Official Citation:David Kirk and James Arvo. 1991. Unbiased sampling techniques for image synthesis. SIGGRAPH Comput. Graph. 25, 4 (July 1991), 153-156. DOI=
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:72073
Deposited On:17 Nov 2016 00:50
Last Modified:11 Nov 2021 04:55

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