Exploiting uncertainty in random sample consensus

Rahul Raguram, Jan-Michael Frahm, Marc Pollefeys
2009 2009 IEEE 12th International Conference on Computer Vision  
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
more » ... 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.
doi:10.1109/iccv.2009.5459456 dblp:conf/iccv/RaguramFP09 fatcat:wgrllq5fyzbmxac7gwmvj3wqi4