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We seek to predict the 6 degree-of-freedom (6DoF) pose of a query photographwith respect to a large indoor 3D map. The contributions of this work arethree-fold. First, we develop a new large-scale visual localization methodtargeted for indoor environments. The method proceeds along three steps: (i)efficient retrieval of candidate poses that ensures scalability to large-scaleenvironments, (ii) pose estimation using dense matching rather than localfeatures to deal with textureless indoor scenes, and (iii) pose verification byvirtual view synthesis to cope with significant changes in viewpoint, scenelayout, and occluders. Second, we collect a new dataset with reference 6DoFposes for large-scale indoor localization. Query photographs are captured bymobile phones at a different time than the reference 3D map, thus presenting arealistic indoor localization scenario. Third, we demonstrate that our methodsignificantly outperforms current state-of-the-art indoor localizationapproaches on this new challenging data.

InLoc: Indoor Visual Localization with Dense Matching and View Synthesis