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ICCV
2009
IEEE

Exploiting uncertainty in random sample consensus

8 years 5 months ago
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 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.
Rahul Raguram, Jan-Michael Frahm, Marc Pollefeys
Added 18 Feb 2011
Updated 18 Feb 2011
Type Journal
Year 2009
Where ICCV
Authors Rahul Raguram, Jan-Michael Frahm, Marc Pollefeys
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