Exploiting uncertainty in random sample consensus

In this work, we present a technique for robust estimation, which by explicitly incorporating the inherent uncertainty of the estimation procedure, results in a more efficient robust estimation algorithm. In addition, we build on recent work in randomized model verification, and use this to characte...

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Bibliographic Details
Main Authors: Raguram, Rahul, Frahm, Jan-Michael, Pollefeys, Marc
Format: Conference Proceeding
Language:English
Subjects:
Application software
Computer science
Computer vision
Noise generators
Resumes
Robustness
Runtime
Sampling methods
Solid modeling
Uncertainty
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Summary:In this work, we present a technique for robust estimation, which by explicitly incorporating the inherent uncertainty of the estimation procedure, results in a more efficient robust estimation algorithm. In addition, we build on recent work in randomized model verification, and use this to characterize the `non-randomness' of a solution. The combination of these two strategies results in a robust estimation procedure that provides a significant speed-up over existing RANSAC techniques, while requiring no prior information to guide the sampling process. In particular, our algorithm requires, on average, 3-10 times fewer samples than standard RANSAC, which is in close agreement with theoretical predictions. The efficiency of the algorithm is demonstrated on a selection of geometric estimation problems.
ISSN:1550-5499
2380-7504
DOI:10.1109/ICCV.2009.5459456