Abstract We present SWIGS, a Swift and effificient Guided Sampling method for robust model estimation from image feature correspondences. Our method leverages the accuracy of our new confifidence measure (MR-Rayleigh), which assigns a correctness-confifidence to a putative correspondence in an online fashion. MR-Rayleigh is inspired by Meta-Recognition (MR), an algorithm that aims to predict when a classififier’s outcome is correct. We demonstrate that by using a Rayleigh distribution, the prediction accuracy of MR can be improved considerably. Our experiments show that MR-Rayleigh tends to predict better than the often-used Lowe’s ratio, Brown’s ratio, and the standard MR under a range of imaging conditions. Furthermore, our homography estimation experiment demonstrates that SWIGS performs similarly or better than other guided sampling methods while requiring fewer iterations, leading to fast and accurate model estimates