Details
| Original language | English |
|---|---|
| Pages (from-to) | 89-101 |
| Number of pages | 13 |
| Journal | ISPRS Journal of Photogrammetry and Remote Sensing |
| Volume | 127 |
| Early online date | 6 Feb 2017 |
| Publication status | Published - May 2017 |
Abstract
In this paper we present a novel rotation averaging scheme as part of our global image orientation model. This model is based on homologous points in overlapping images and is robust against outliers. It is applicable to various kinds of image data and provides accurate initializations for a subsequent bundle adjustment. The computation of global rotations is a combined optimization scheme: First, rotations are estimated in a convex relaxed semidefinite program. Rotations are required to be in the convex hull of the rotation group SO(3), which in most cases leads to correct rotations. Second, the estimation is improved in an iterative least squares optimization in the Lie algebra of SO(3). In order to deal with outliers in the relative rotations, we developed a sequential graph optimization algorithm that is able to detect and eliminate incorrect rotations. From the beginning, we propagate covariance information which allows for a weighting in the least squares estimation. We evaluate our approach using both synthetic and real image datasets. Compared to recent state-of-the-art rotation averaging and global image orientation algorithms, our proposed scheme reaches a high degree of robustness and accuracy. Moreover, it is also applicable to large Internet datasets, which shows its efficiency.
Keywords
- Convex optimization, Image orientation, Lie algebra, Pose estimation, Rotation averaging
ASJC Scopus subject areas
- Physics and Astronomy(all)
- Atomic and Molecular Physics, and Optics
- Engineering(all)
- Engineering (miscellaneous)
- Computer Science(all)
- Computer Science Applications
- Earth and Planetary Sciences(all)
- Computers in Earth Sciences
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In: ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 127, 05.2017, p. 89-101.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Global robust image rotation from combined weighted averaging
AU - Reich, Martin
AU - Yang, Michael Ying
AU - Heipke, Christian
N1 - Publisher Copyright: © 2017 Copyright: Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2017/5
Y1 - 2017/5
N2 - In this paper we present a novel rotation averaging scheme as part of our global image orientation model. This model is based on homologous points in overlapping images and is robust against outliers. It is applicable to various kinds of image data and provides accurate initializations for a subsequent bundle adjustment. The computation of global rotations is a combined optimization scheme: First, rotations are estimated in a convex relaxed semidefinite program. Rotations are required to be in the convex hull of the rotation group SO(3), which in most cases leads to correct rotations. Second, the estimation is improved in an iterative least squares optimization in the Lie algebra of SO(3). In order to deal with outliers in the relative rotations, we developed a sequential graph optimization algorithm that is able to detect and eliminate incorrect rotations. From the beginning, we propagate covariance information which allows for a weighting in the least squares estimation. We evaluate our approach using both synthetic and real image datasets. Compared to recent state-of-the-art rotation averaging and global image orientation algorithms, our proposed scheme reaches a high degree of robustness and accuracy. Moreover, it is also applicable to large Internet datasets, which shows its efficiency.
AB - In this paper we present a novel rotation averaging scheme as part of our global image orientation model. This model is based on homologous points in overlapping images and is robust against outliers. It is applicable to various kinds of image data and provides accurate initializations for a subsequent bundle adjustment. The computation of global rotations is a combined optimization scheme: First, rotations are estimated in a convex relaxed semidefinite program. Rotations are required to be in the convex hull of the rotation group SO(3), which in most cases leads to correct rotations. Second, the estimation is improved in an iterative least squares optimization in the Lie algebra of SO(3). In order to deal with outliers in the relative rotations, we developed a sequential graph optimization algorithm that is able to detect and eliminate incorrect rotations. From the beginning, we propagate covariance information which allows for a weighting in the least squares estimation. We evaluate our approach using both synthetic and real image datasets. Compared to recent state-of-the-art rotation averaging and global image orientation algorithms, our proposed scheme reaches a high degree of robustness and accuracy. Moreover, it is also applicable to large Internet datasets, which shows its efficiency.
KW - Convex optimization
KW - Image orientation
KW - Lie algebra
KW - Pose estimation
KW - Rotation averaging
UR - http://www.scopus.com/inward/record.url?scp=85011592170&partnerID=8YFLogxK
U2 - 10.1016/j.isprsjprs.2017.01.011
DO - 10.1016/j.isprsjprs.2017.01.011
M3 - Article
AN - SCOPUS:85011592170
VL - 127
SP - 89
EP - 101
JO - ISPRS Journal of Photogrammetry and Remote Sensing
JF - ISPRS Journal of Photogrammetry and Remote Sensing
SN - 0924-2716
ER -