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- W2983134350 endingPage "1307" @default.
- W2983134350 startingPage "1293" @default.
- W2983134350 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 spaces. The method proceeds along three steps: (i) efficient retrieval of candidate poses that scales to large-scale environments, (ii) pose estimation using dense matching rather than sparse local features to deal with weakly textured indoor scenes, and (iii) pose verification by virtual view synthesis that is robust to significant changes in viewpoint, scene layout, and occlusion. Second, we release 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. Code and data are publicly available." @default.
- W2983134350 created "2019-11-22" @default.
- W2983134350 creator A5011683384 @default.
- W2983134350 creator A5011925809 @default.
- W2983134350 creator A5021908609 @default.
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- W2983134350 creator A5032196371 @default.
- W2983134350 creator A5043222506 @default.
- W2983134350 creator A5063957408 @default.
- W2983134350 date "2021-04-01" @default.
- W2983134350 modified "2023-10-01" @default.
- W2983134350 title "InLoc: Indoor Visual Localization with Dense Matching and View Synthesis" @default.
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