  Image based localization is one of the important problems in computer vision
due to its wide applicability in robotics, augmented reality, and autonomous
systems. There is a rich set of methods described in the literature how to
geometrically register a 2D image w.r.t.\ a 3D model. Recently, methods based
on deep (and convolutional) feedforward networks (CNNs) became popular for pose
regression. However, these CNN-based methods are still less accurate than
geometry based methods despite being fast and memory efficient. In this work we
design a deep neural network architecture based on sparse feature descriptors
to estimate the absolute pose of an image. Our choice of using sparse feature
descriptors has two major advantages: first, our network is significantly
smaller than the CNNs proposed in the literature for this task---thereby making
our approach more efficient and scalable. Second---and more importantly---,
usage of sparse features allows to augment the training data with synthetic
viewpoints, which leads to substantial improvements in the generalization
performance to unseen poses. Thus, our proposed method aims to combine the best
of the two worlds---feature-based localization and CNN-based pose
regression--to achieve state-of-the-art performance in the absolute pose
estimation. A detailed analysis of the proposed architecture and a rigorous
evaluation on the existing datasets are provided to support our method.
