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Accurate fundamental matrix estimation from computed correspondences is hard to achieve depending on the constraints on computational time and available data (i.e. correspondences and quality scores). Several algorithms exist for this task, like the 8-points, the 7-points algorithm  or robust methods such as RANSAC , MSAC  or LMedS . Robust methods are capable of discriminating correspondence outliers, thus, obtaining better results. Additionally, some variations of the previous methods have been proposed. For instance PROSAC  is an improvement of RANSAC which takes into account additional information of the quality of the matches to largely reduce the computational cost of the fundamental matrix estimation process. This work proposes a new robust method for fundamental matrix estimation that combines the benefits of PROSAC and LMedS algorithms, namely improved quality, reduced computational time and less parameters to adjust
CitationIrurueta, A.; Morros, J. Promeds: an adaptive robust fundamental matrix estimation approach. A: 3DTV Conference. "Proceedings of 3DTV Conference 2012". Zurich: 2012.
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