  We propose a new deep learning based approach for camera relocalization. Our
approach localizes a given query image by using a convolutional neural network
(CNN) for first retrieving similar database images and then predicting the
relative pose between the query and the database images, whose poses are known.
The camera location for the query image is obtained via triangulation from two
relative translation estimates using a RANSAC based approach. Each relative
pose estimate provides a hypothesis for the camera orientation and they are
fused in a second RANSAC scheme. The neural network is trained for relative
pose estimation in an end-to-end manner using training image pairs. In contrast
to previous work, our approach does not require scene-specific training of the
network, which improves scalability, and it can also be applied to scenes which
are not available during the training of the network. As another main
contribution, we release a challenging indoor localisation dataset covering 5
different scenes registered to a common coordinate frame. We evaluate our
approach using both our own dataset and the standard 7 Scenes benchmark. The
results show that the proposed approach generalizes well to previously unseen
scenes and compares favourably to other recent CNN-based methods.
