Robust image orientation based on relative rotations and tie points

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Wang, X.; Rottensteiner, F.; Heipke, C.: Robust image orientation based on relative rotations and tie points. In: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences 4 (2018), Nr. 2, S. 295-302. DOI:

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Sum total of downloads: 129

In this paper we present a novel approach for image orientation by combining relative rotations and tie points. First, we choose an initial image pair with enough correspondences and large triangulation angle, and we then iteratively add clusters of new images. The rotation of these newly added images is estimated from relative rotations by single rotation averaging. In the next step, a linear equation system is set up for each new image to solve the translation parameters with triangulated tie points which can be viewed in that new image, followed by a resection for refinement. Finally, we optimize the cluster of reconstructed images by local bundle adjustment. We show results of our approach on different benchmark datasets. Furthermore, we orient several larger datasets incl. unordered image datasets to demonstrate the robustness and performance of our approach.
License of this version: CC BY 4.0 Unported
Document Type: article
Publishing status: publishedVersion
Issue Date: 2018
Appears in Collections:Fakultät für Bauingenieurwesen und Geodäsie

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pos. country downloads
total perc.
1 image of flag of Germany Germany 57 44.19%
2 image of flag of China China 16 12.40%
3 image of flag of Lithuania Lithuania 8 6.20%
4 image of flag of Italy Italy 7 5.43%
5 image of flag of Korea, Republic of Korea, Republic of 6 4.65%
6 image of flag of United States United States 5 3.88%
7 image of flag of France France 4 3.10%
8 image of flag of Australia Australia 4 3.10%
9 image of flag of United Kingdom United Kingdom 3 2.33%
10 image of flag of Japan Japan 2 1.55%
    other countries 17 13.18%

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