ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume V-2-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2020, 95–104, 2020
https://doi.org/10.5194/isprs-annals-V-2-2020-95-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2020, 95–104, 2020
https://doi.org/10.5194/isprs-annals-V-2-2020-95-2020

  03 Aug 2020

03 Aug 2020

A HYBRID GLOBAL IMAGE ORIENTATION METHOD FOR SIMULTANEOUSLY ESTIMATING GLOBAL ROTATIONS AND GLOBAL TRANSLATIONS

X. Wang1, T. Xiao2, and Y. Kasten3 X. Wang et al.
  • 1Institute of Photogrammetry and GeoInformation, Leibniz Universität Hannover, Germany
  • 2School of Geodesy and Geomatics, Wuhan University, Wuhan, P.R. China
  • 3Weizmann Institute of Science, Israel

Keywords: image orientation, global structure from motion (SfM), global rotations estimation, global translation estimation

Abstract. In recent years, the determination of global image orientation, i.e. global SfM, has gained a lot of attentions from researchers, mainly due to its time efficiency. Most of the global methods take relative rotations and translations as input for a two-step strategy comprised of global rotation averaging and global translation averaging. This paper by contrast presents a hybrid approach that aims to solve global rotations and translations simultaneously, but hierarchically. We first extract an optimal minimum cover connected image triplet set (OMCTS) which includes all available images with a minimum number of triplets, all of them with the three related relative orientations being compatible to each other. For non-collinear triplets in the OMCTS, we introduce some basic characterizations of the corresponding essential matrices and solve for the image pose parameters by averaging the constrained essential matrices. For the collinear triplets, on the other hand, the image pose parameters are estimated by relative orientation using the depth of object points from individual local spatial intersection. Finally, all image orientations are estimated in a common coordinate frame by traversing every solved triplet using a similarity transformation. We show results of our method on different benchmarks and demonstrate the performance and capability of the proposed approach by comparing with other global SfM methods.