  Visual localization is critical to many applications in computer vision and
robotics. To address single-image RGB localization, state-of-the-art
feature-based methods match local descriptors between a query image and a
pre-built 3D model. Recently, deep neural networks have been exploited to
regress the mapping between raw pixels and 3D coordinates in the scene, and
thus the matching is implicitly performed by the forward pass through the
network. However, in a large and ambiguous environment, learning such a
regression task directly can be difficult for a single network. In this work,
we present a new hierarchical scene coordinate network to predict pixel scene
coordinates in a coarse-to-fine manner from a single RGB image. The network
consists of a series of output layers, each of them conditioned on the previous
ones. The final output layer predicts the 3D coordinates and the others produce
progressively finer discrete location labels. The proposed method outperforms
the baseline regression-only network and allows us to train compact models
which scale robustly to large environments. It sets a new state-of-the-art for
single-image RGB localization performance on the 7-Scenes, 12-Scenes, Cambridge
Landmarks datasets, and three combined scenes. Moreover, for large-scale
outdoor localization on the Aachen Day-Night dataset, we present a hybrid
approach which outperforms existing scene coordinate regression methods, and
reduces significantly the performance gap w.r.t. explicit feature matching
methods.
