  Scene coordinate regression has become an essential part of current camera
re-localization methods. Different versions, such as regression forests and
deep learning methods, have been successfully applied to estimate the
corresponding camera pose given a single input image. In this work, we propose
to regress the scene coordinates pixel-wise for a given RGB image by using deep
learning. Compared to the recent methods, which usually employ RANSAC to obtain
a robust pose estimate from the established point correspondences, we propose
to regress confidences of these correspondences, which allows us to immediately
discard erroneous predictions and improve the initial pose estimates. Finally,
the resulting confidences can be used to score initial pose hypothesis and aid
in pose refinement, offering a generalized solution to solve this task.

Computer Vision and Pattern Recognition