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Establishing the correspondence between two images is an important researchdirection of computer vision. When estimating the relationship between twoimages, it is often disturbed by outliers. In this paper, we propose aconvolutional neural network that can filter the noise of outliers. It canoutput the probability that the pair of feature points is an inlier and regressthe essential matrix representing the relative pose of the camera. The outliersare mainly caused by the noise introduced by the previous processing. Theoutliers rejection can be treated as a problem of noise elimination, and thesoft threshold function has a very good effect on noise reduction. Therefore,we designed an adaptive denoising module based on soft threshold function toremove noise components in the outliers, to reduce the probability that theoutlier is predicted to be an inlier. Experimental results on the YFCC100Mdataset show that our method exceeds the state-of-the-art in relative poseestimation.

FN-Net:Remove the Outliers by Filtering the Noise