City-Scale Localization for Cameras with Known Vertical Direction

We consider the problem of localizing a novel image in a large 3D model, given that the gravitational vector is known. In principle, this is just an instance of camera pose estimation, but the scale of the problem introduces some interesting challenges. Most importantly, it makes the correspondence...

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Vydáno v:IEEE transactions on pattern analysis and machine intelligence Ročník 39; číslo 7; s. 1455 - 1461
Hlavní autoři: Svarm, Linus, Enqvist, Olof, Kahl, Fredrik, Oskarsson, Magnus
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.07.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Approximation
camera pose
Cameras
Computational modeling
Computer and Information Sciences
Computer graphics and computer vision
Data- och informationsvetenskap (Datateknik)
Datorgrafik och datorseende
Gravitation
Localization
Matematik
Mathematical Sciences
Natural Sciences
Naturvetenskap
Outliers (statistics)
Pose estimation
Position (location)
position retrieval
Robustness
Scale models
Searching
Sensors
Solid modeling
Solvers
Three dimensional models
Three-dimensional displays
ISSN:0162-8828, 1939-3539, 2160-9292, 1939-3539
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Popis
Shrnutí:We consider the problem of localizing a novel image in a large 3D model, given that the gravitational vector is known. In principle, this is just an instance of camera pose estimation, but the scale of the problem introduces some interesting challenges. Most importantly, it makes the correspondence problem very difficult so there will often be a significant number of outliers to handle. To tackle this problem, we use recent theoretical as well as technical advances. Many modern cameras and phones have gravitational sensors that allow us to reduce the search space. Further, there are new techniques to efficiently and reliably deal with extreme rates of outliers. We extend these methods to camera pose estimation by using accurate approximations and fast polynomial solvers. Experimental results are given demonstrating that it is possible to reliably estimate the camera pose despite cases with more than 99 percent outlier correspondences in city-scale models with several millions of 3D points.
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ISSN:0162-8828
1939-3539
2160-9292
1939-3539
DOI:10.1109/TPAMI.2016.2598331