  We propose a compact pipeline to unify all the steps of Visual Localization:
image retrieval, candidate re-ranking and initial pose estimation, and camera
pose refinement. Our key assumption is that the deep features used for these
individual tasks share common characteristics, so we should reuse them in all
the procedures of the pipeline. Our DRAN (Deep Retrieval and image Alignment
Network) is able to extract global descriptors for efficient image retrieval,
use intermediate hierarchical features to re-rank the retrieval list and
produce an initial pose guess, which is finally refined by means of a
feature-metric optimization based on learned deep multi-scale dense features.
DRAN is the first single network able to produce the features for the three
steps of visual localization. DRAN achieves competitive performance in terms of
robustness and accuracy under challenging conditions in public benchmarks,
outperforming other unified approaches and consuming lower computational and
memory cost than its counterparts using multiple networks. Code and models will
be publicly available at https://github.com/jmorlana/DRAN.
