Details
| Original language | English |
|---|---|
| Pages (from-to) | 233-249 |
| Number of pages | 17 |
| Journal | PFG - Journal of Photogrammetry, Remote Sensing and Geoinformation Science |
| Volume | 89 |
| Issue number | 3 |
| Early online date | 23 Jun 2021 |
| Publication status | Published - Jun 2021 |
Abstract
The quality of the parameters of relative orientation (ROs) of a stereoscopic pair of images is crucial for the quality of results in structure from motion (SfM). In this paper we focus on improving the robustness and accuracy of SfM by detecting and eliminating incorrect ROs, especially due to repetitive structure, that typically result in incorrectly estimated RO results and thus degrade 3D reconstruction. ROs are represented by a view graph. We develop a novel variant of cycle consistency inference, called sequential cycle consistency inference or SCCI, to infer wrong edges by analyzing the geometric consistency of cycles in the view graph. Our method consists essentially of a two-stage process, an initialization step based on the union of various orthogonal minimum spanning trees (MST) of the graph, followed by an expansion step which incrementally adds edges to this graph. We show by means of experimental studies that our method reaches better robustness and accuracy for data sets containing repetitive structure, compared to state-of-the-art algorithms of RO outlier detection.
Keywords
- Outlier detection, Relative orientations (ROs), Repetitive structure, Sequential cycle consistency inference, Structure from motion (SfM)
ASJC Scopus subject areas
- Social Sciences(all)
- Geography, Planning and Development
- Physics and Astronomy(all)
- Instrumentation
- Earth and Planetary Sciences(all)
- Earth and Planetary Sciences (miscellaneous)
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In: PFG - Journal of Photogrammetry, Remote Sensing and Geoinformation Science, Vol. 89, No. 3, 06.2021, p. 233-249.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Sequential Cycle Consistency Inference for Eliminating Incorrect Relative Orientations in Structure from Motion
AU - Xiao, Teng
AU - Wang, Xin
AU - Deng, Fei
AU - Heipke, Christian
N1 - Funding Information: We would like to thank Mario Michelini and Helmut Mayer, Universität der Bundeswehr München, for making the church data set available to us. The author Xin Wang would like to thank the China Scholarship Council (No. 201606270207) for financially supporting his Ph.D. study at Leibniz Universität Hannover, Germany. Funding Information: This work was supported in part by the Open Fund of the Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Land and Resources, China, KF-2018–03-025.
PY - 2021/6
Y1 - 2021/6
N2 - The quality of the parameters of relative orientation (ROs) of a stereoscopic pair of images is crucial for the quality of results in structure from motion (SfM). In this paper we focus on improving the robustness and accuracy of SfM by detecting and eliminating incorrect ROs, especially due to repetitive structure, that typically result in incorrectly estimated RO results and thus degrade 3D reconstruction. ROs are represented by a view graph. We develop a novel variant of cycle consistency inference, called sequential cycle consistency inference or SCCI, to infer wrong edges by analyzing the geometric consistency of cycles in the view graph. Our method consists essentially of a two-stage process, an initialization step based on the union of various orthogonal minimum spanning trees (MST) of the graph, followed by an expansion step which incrementally adds edges to this graph. We show by means of experimental studies that our method reaches better robustness and accuracy for data sets containing repetitive structure, compared to state-of-the-art algorithms of RO outlier detection.
AB - The quality of the parameters of relative orientation (ROs) of a stereoscopic pair of images is crucial for the quality of results in structure from motion (SfM). In this paper we focus on improving the robustness and accuracy of SfM by detecting and eliminating incorrect ROs, especially due to repetitive structure, that typically result in incorrectly estimated RO results and thus degrade 3D reconstruction. ROs are represented by a view graph. We develop a novel variant of cycle consistency inference, called sequential cycle consistency inference or SCCI, to infer wrong edges by analyzing the geometric consistency of cycles in the view graph. Our method consists essentially of a two-stage process, an initialization step based on the union of various orthogonal minimum spanning trees (MST) of the graph, followed by an expansion step which incrementally adds edges to this graph. We show by means of experimental studies that our method reaches better robustness and accuracy for data sets containing repetitive structure, compared to state-of-the-art algorithms of RO outlier detection.
KW - Outlier detection
KW - Relative orientations (ROs)
KW - Repetitive structure
KW - Sequential cycle consistency inference
KW - Structure from motion (SfM)
UR - http://www.scopus.com/inward/record.url?scp=85108634219&partnerID=8YFLogxK
U2 - 10.1007/s41064-021-00152-1
DO - 10.1007/s41064-021-00152-1
M3 - Article
AN - SCOPUS:85108634219
VL - 89
SP - 233
EP - 249
JO - PFG - Journal of Photogrammetry, Remote Sensing and Geoinformation Science
JF - PFG - Journal of Photogrammetry, Remote Sensing and Geoinformation Science
SN - 2512-2789
IS - 3
ER -