InLoc: Indoor Visual Localization with Dense Matching and View Synthesis

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: (...

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Veröffentlicht in:2018 IEEE CVF Conference on Computer Vision and Pattern Recognition S. 7199 - 7209
Hauptverfasser: Taira, Hajime, Okutomi, Masatoshi, Sattler, Torsten, Cimpoi, Mircea, Pollefeys, Marc, Sivic, Josef, Pajdla, Tomas, Torii, Akihiko
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.06.2018
Schlagworte:
Buildings
Cameras
Distributed databases
Life Sciences
Lighting
Three-dimensional displays
Visualization
scale
ISBN:9781538664209, 1538664208
ISSN:1063-6919
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Zusammenfassung: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 texture less 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.
ISBN:9781538664209
1538664208
ISSN:1063-6919
DOI:10.1109/CVPR.2018.00752