  Robust and accurate visual localization is a fundamental capability for
numerous applications, such as autonomous driving, mobile robotics, or
augmented reality. It remains, however, a challenging task, particularly for
large-scale environments and in presence of significant appearance changes.
State-of-the-art methods not only struggle with such scenarios, but are often
too resource intensive for certain real-time applications. In this paper we
propose HF-Net, a hierarchical localization approach based on a monolithic CNN
that simultaneously predicts local features and global descriptors for accurate
6-DoF localization. We exploit the coarse-to-fine localization paradigm: we
first perform a global retrieval to obtain location hypotheses and only later
match local features within those candidate places. This hierarchical approach
incurs significant runtime savings and makes our system suitable for real-time
operation. By leveraging learned descriptors, our method achieves remarkable
localization robustness across large variations of appearance and sets a new
state-of-the-art on two challenging benchmarks for large-scale localization.
