Image-based localization has been widely used for autonomous vehicles, robotics, augmented reality, etc., and this is carried out by matching a query image taken from a cell phone or vehicle dashcam to a large scale of geo-tagged reference images, such as satellite/aerial images or Google Street Views. However, the problem remains challenging due to the inconsistency between the query images and the large-scale reference datasets regarding various light and weather conditions. To tackle this issue, this work proposes a novel view synthesis framework equipped with deep generative models, which can merge the unique features from the outdated reference dataset with features from the images containing seasonal changes. Our design features a unique scheme to ensure that the synthesized images contain the important features from both reference and patch images, covering seasonable features and minimizing the gap for the image-based localization tasks. The performance evaluation shows that the proposed framework can synthesize the views in various weather and lighting conditions.
© 2023 by the authors.
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) License.
Article states: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the data containing information that could compromise the privacy of research participants.
Original Publication Citation
Lee, U., Jiang, P., Wu, H., & Xin, C. (2023). View synthesis with scene recognition for cross-view image localization. Future Internet, 15(4), 1-13, Article 126. https://doi.org/10.3390/fi15040126
Lee, Uddom; Jiang, Peng; Wu, Hongyi; and Xin, Chunsheng, "View Synthesis With Scene Recognition for Cross-View Image Localization" (2023). Electrical & Computer Engineering Faculty Publications. 360.