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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| Vydané v: | IEEE transactions on pattern analysis and machine intelligence Ročník 39; číslo 7; s. 1455 - 1461 |
|---|---|
| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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United States
IEEE
01.07.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 0162-8828, 1939-3539, 2160-9292, 1939-3539 |
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| Abstract | 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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| AbstractList | 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. 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.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. 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% outlier correspondences in city-scale models with several millions of 3D points. |
| Author | Enqvist, Olof Kahl, Fredrik Svarm, Linus Oskarsson, Magnus |
| Author_xml | – sequence: 1 givenname: Linus surname: Svarm fullname: Svarm, Linus email: linus@maths.lth.se organization: Centre for Math. Sci., Lund Univ., Lund, Sweden – sequence: 2 givenname: Olof surname: Enqvist fullname: Enqvist, Olof email: olof.enqvist@chalmers.se organization: Dept. of Signals & Syst., Chalmers Univ. of Technol., Gothenburg, Sweden – sequence: 3 givenname: Fredrik surname: Kahl fullname: Kahl, Fredrik email: fredrik@maths.lth.se organization: Centre for Math. Sci., Lund Univ., Lund, Sweden – sequence: 4 givenname: Magnus surname: Oskarsson fullname: Oskarsson, Magnus email: magnuso@maths.lth.se organization: Centre for Math. Sci., Lund Univ., Lund, Sweden |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/27514034$$D View this record in MEDLINE/PubMed https://research.chalmers.se/publication/247055$$DView record from Swedish Publication Index (Chalmers tekniska högskola) |
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| SubjectTerms | 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 |
| Title | City-Scale Localization for Cameras with Known Vertical Direction |
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