  This paper presents a convolutional neural network based approach for
estimating the relative pose between two cameras. The proposed network takes
RGB images from both cameras as input and directly produces the relative
rotation and translation as output. The system is trained in an end-to-end
manner utilising transfer learning from a large scale classification dataset.
The introduced approach is compared with widely used local feature based
methods (SURF, ORB) and the results indicate a clear improvement over the
baseline. In addition, a variant of the proposed architecture containing a
spatial pyramid pooling (SPP) layer is evaluated and shown to further improve
the performance.
