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 proposed method, which is an
extension of HSCNet, allows us to train compact models which scale robustly to
large environments. It sets a new state-of-the-art for single-image
localization
on the 7-Scenes, 12-Scenes, Cambridge Landmarks datasets, and the combined
indoor scenes.

Authors: Shuzhe Wang, Zakaria Laskar, Iaroslav Melekhov, Xiaotian Li, Yi Zhao,
Giorgos Tolias & Juho Kannala