  Relative camera pose estimation, i.e. estimating the translation and rotation
vectors using a pair of images taken in different locations, is an important
part of systems in augmented reality and robotics. In this paper, we present an
end-to-end relative camera pose estimation network using a siamese architecture
that is independent of camera parameters. The network is trained using the
Cambridge Landmarks data with four individual scene datasets and a dataset
combining the four scenes. To improve generalization, we propose a novel
two-stage training that alleviates the need of a hyperparameter to balance the
translation and rotation loss scale. The proposed method is compared with
one-stage training CNN-based methods such as RPNet and RCPNet and demonstrate
that the proposed model improves translation vector estimation by 16.11%,
28.88%, and 52.27% on the Kings College, Old Hospital, and St Marys Church
scenes, respectively. For proving texture invariance, we investigate the
generalization of the proposed method augmenting the datasets to different
scene styles, as ablation studies, using generative adversarial networks. Also,
we present a qualitative assessment of epipolar lines of our network
predictions and ground truth poses.
