View Synthesis with Scene Recognition for Cross-View Image Localization

Uddom Lee, Peng Jiang, Hongyi Wu and Chunsheng Xin ()
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Uddom Lee: School of Cybersecurity, Old Dominion University, Norfolk, VA 23529, USA
Peng Jiang: School of Cybersecurity, Old Dominion University, Norfolk, VA 23529, USA
Hongyi Wu: Department of ECE, University of Arizona, Tucson, AZ 85721, USA
Chunsheng Xin: School of Cybersecurity, Old Dominion University, Norfolk, VA 23529, USA

Future Internet, 2023, vol. 15, issue 4, 1-13

Abstract: 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a novel view synthesis framework equipped with deep generative models, which can merge the unique features from the outdated reference dataset with features from the images containing seasonal changes.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.

Keywords: image-based localization; deep learning; generative neural networks; style transfer (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2023
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