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Since ground truth pose labels are difficult to obtain, recent methods try to learn pose estimation networks using pixel-perfect synthetic data. However, this also introduces the problem of domain bias. In this paper, we first build a<jats:italic>Tuebingen Buildings<\/jats:italic>dataset of RGB images collected by a drone in urban scenes and create a 3D model for each scene. A large number of synthetic images are generated based on these 3D models. We take advantage of image style transfer and cycle-consistent adversarial training to predict the relative camera poses of image pairs based on training over synthetic environment data. We propose a relative camera pose estimation approach to solve the continuous localization problem for autonomous navigation of unmanned systems. Unlike those existing learning-based camera pose estimation methods that train and test in a single scene, our approach successfully estimates the relative camera poses of multiple city locations with a single trained model. We use the<jats:italic>Tuebingen Buildings<\/jats:italic>and the<jats:italic>Cambridge Landmarks<\/jats:italic>datasets to evaluate the performance of our approach in a single scene and across-scenes. For each dataset, we compare the performance between real images and synthetic images trained models. We also test our model in the indoor dataset<jats:italic>7Scenes<\/jats:italic>to demonstrate its generalization ability.<\/jats:p>","DOI":"10.1007\/s10846-021-01439-6","type":"journal-article","created":{"date-parts":[[2021,7,8]],"date-time":"2021-07-08T12:05:21Z","timestamp":1625745921000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Relative Camera Pose Estimation using Synthetic Data with Domain Adaptation via Cycle-Consistent Adversarial Networks"],"prefix":"10.1007","volume":"102","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9921-7095","authenticated-orcid":false,"given":"Chenhao","family":"Yang","sequence":"first","affiliation":[]},{"given":"Yuyi","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Andreas","family":"Zell","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,7,8]]},"reference":[{"key":"1439_CR1","doi-asserted-by":"crossref","unstructured":"Newcombe, R.A., Lovegrove, S.J., Davison, A.J.: DTAM: Dense tracking and mapping in real-time. 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