InLoc: Indoor Visual Localization with Dense Matching and View Synthesis
Mircea Cimpoi
(CIIRC CTU Prague, Czech Republic)
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 significantly outperforms current state-of-the-art indoor
localization approaches on this new challenging data.