  In this paper, we address the problem of estimating scale factors between
images. We formulate the scale estimation problem as a prediction of a
probability distribution over scale factors. We design a new architecture,
ScaleNet, that exploits dilated convolutions as well as self and
cross-correlation layers to predict the scale between images. We demonstrate
that rectifying images with estimated scales leads to significant performance
improvements for various tasks and methods. Specifically, we show how ScaleNet
can be combined with sparse local features and dense correspondence networks to
improve camera pose estimation, 3D reconstruction, or dense geometric matching
in different benchmarks and datasets. We provide an extensive evaluation on
several tasks and analyze the computational overhead of ScaleNet. The code,
evaluation protocols, and trained models are publicly available at
https://github.com/axelBarroso/ScaleNet.
