  Visual localization in large and complex indoor scenes, dominated by weakly
textured rooms and repeating geometric patterns, is a challenging problem with
high practical relevance for applications such as Augmented Reality and
robotics. To handle the ambiguities arising in this scenario, a common strategy
is, first, to generate multiple estimates for the camera pose from which a
given query image was taken. The pose with the largest geometric consistency
with the query image, e.g., in the form of an inlier count, is then selected in
a second stage. While a significant amount of research has concentrated on the
first stage, there is considerably less work on the second stage. In this
paper, we thus focus on pose verification. We show that combining different
modalities, namely appearance, geometry, and semantics, considerably boosts
pose verification and consequently pose accuracy. We develop multiple
hand-crafted as well as a trainable approach to join into the
geometric-semantic verification and show significant improvements over
state-of-the-art on a very challenging indoor dataset.

Computer Vision and Pattern Recognition