Abstract
We address the problem of determining correspondences
between two images in agreement with a geometric model
such as an affine or thin-plate spline transformation, and
estimating its parameters. The contributions of this work
are three-fold. First, we propose a convolutional neural network architecture for geometric matching. The architecture
is based on three main components that mimic the standard
steps of feature extraction, matching and simultaneous inlier detection and model parameter estimation, while being
trainable end-to-end. Second, we demonstrate that the network parameters can be trained from synthetically generated imagery without the need for manual annotation and
that our matching layer significantly increases generalization capabilities to never seen before images. Finally, we
show that the same model can perform both instance-level
and category-level matching giving state-of-the-art results
on the challenging Proposal Flow dataset.