Abstract
We seek to predict the 6 degree-of-freedom (6DoF) pose of
a query photograph with respect to a large indoor 3D map.
The contributions of this work are three-fold. First, we
develop a new large-scale visual localization method targeted for indoor environments. The method proceeds along
three steps: (i) efficient retrieval of candidate poses that ensures scalability to large-scale environments, (ii) pose estimation using dense matching rather than local features
to deal with textureless indoor scenes, and (iii) pose verification by virtual view synthesis to cope with significant
changes in viewpoint, scene layout, and occluders. Second, we collect a new dataset with reference 6DoF poses
for large-scale indoor localization. Query photographs are
captured by mobile phones at a different time than the reference 3D map, thus presenting a realistic indoor localization
scenario. Third, we demonstrate that our method signifi-
cantly outperforms current state-of-the-art indoor localization approaches on this new challenging data