TY - JOUR

T1 - Robust Structure From Motion Based on Relative Rotations and Tie Points

AU - Wang, Xin

AU - Rottensteiner, Franz

AU - Heipke, Christian

N1 - Funding information: Xin Wang would like to thank the China Scholarship Council for financially supporting his PhD study at Leibniz Universität, Hannover, Germany. Xin Wang would like to thank the China Scholarship Council for financially supporting his PhD study at Leibniz Univer-sität, Hannover, Germany.

PY - 2019/5/1

Y1 - 2019/5/1

N2 - In this article, we present two new approaches for image orientation with a focus on robustness, starting with relative orientations of available image pairs, an incremental and a global one, and compare their performance. For the incremental approach, we first choose a suitable initial image pair, and we then iteratively extend the image cluster by adding new images. The rotations of these newly added images are 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 that can be viewed in that new image, followed by a resection for refinement. Finally, we refine the orientation parameters of the images by a local bundle adjustment. We also present a global method that consists of two parts: global rotation averaging, followed by setting up a large linear equation system to solve for all image translation parameters simultaneously; a final bundle adjustment is carried out to refine the results. We compare these two methods by analyzing results on different benchmark sets, including ordered and unordered image data sets from the Internet and two other challenging data sets to demonstrate the performance of our two approaches. We conclude that while the incremental method typically yields results of higher accuracy and performs better on the challenging data sets, our global method runs significantly faster.

AB - In this article, we present two new approaches for image orientation with a focus on robustness, starting with relative orientations of available image pairs, an incremental and a global one, and compare their performance. For the incremental approach, we first choose a suitable initial image pair, and we then iteratively extend the image cluster by adding new images. The rotations of these newly added images are 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 that can be viewed in that new image, followed by a resection for refinement. Finally, we refine the orientation parameters of the images by a local bundle adjustment. We also present a global method that consists of two parts: global rotation averaging, followed by setting up a large linear equation system to solve for all image translation parameters simultaneously; a final bundle adjustment is carried out to refine the results. We compare these two methods by analyzing results on different benchmark sets, including ordered and unordered image data sets from the Internet and two other challenging data sets to demonstrate the performance of our two approaches. We conclude that while the incremental method typically yields results of higher accuracy and performs better on the challenging data sets, our global method runs significantly faster.

UR - http://www.scopus.com/inward/record.url?scp=85072814433&partnerID=8YFLogxK

U2 - 10.14358/PERS.85.5.347

DO - 10.14358/PERS.85.5.347

M3 - Article

AN - SCOPUS:85072814433

VL - 85

SP - 347

EP - 359

JO - Photogrammetric Engineering and Remote Sensing

JF - Photogrammetric Engineering and Remote Sensing

SN - 0099-1112

IS - 5

ER -