City-Scale Localization for Cameras with Known Vertical Direction
Author
Summary, in English
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.
Department/s
- Mathematics (Faculty of Engineering)
- Mathematical Imaging Group
- ELLIIT: the Linköping-Lund initiative on IT and mobile communication
Publishing year
2017-07-01
Language
English
Pages
1455-1461
Publication/Series
IEEE Transactions on Pattern Analysis and Machine Intelligence
Volume
39
Issue
7
Document type
Journal article
Publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
Topic
- Mathematics
- Computer Vision and Robotics (Autonomous Systems)
Keywords
- Localization,
- Camera Pose
- Position Retrieval
Status
Published
Research group
- Mathematical Imaging Group
ISBN/ISSN/Other
- ISSN: 0162-8828