NONSAC is a general, estimator-agnostic framework that improves scalability and robustness for geometric model estimation on very large noisy datasets by sampling non-minimal subsets and scoring candidate hypotheses.
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Non-Minimal Sampling and Consensus for Prohibitively Large Datasets
NONSAC is a general, estimator-agnostic framework that improves scalability and robustness for geometric model estimation on very large noisy datasets by sampling non-minimal subsets and scoring candidate hypotheses.