  Establishing the correspondence between two images is an important research
direction of computer vision. When estimating the relationship between two
images, it is often disturbed by outliers. In this paper, we propose a
convolutional neural network that can filter the noise of outliers. It can
output the probability that the pair of feature points is an inlier and regress
the essential matrix representing the relative pose of the camera. The outliers
are mainly caused by the noise introduced by the previous processing. The
outliers rejection can be treated as a problem of noise elimination, and the
soft threshold function has a very good effect on noise reduction. Therefore,
we designed an adaptive denoising module based on soft threshold function to
remove noise components in the outliers, to reduce the probability that the
outlier is predicted to be an inlier. Experimental results on the YFCC100M
dataset show that our method exceeds the state-of-the-art in relative pose
estimation.
