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We propose a novel fine-grained cross-view localization method that estimates the 3 Degrees of Freedom pose of a ground-level image in an aerial image of the surroundings by matching fine-grained features between the two images.
A generalized solution of the orthogonal procrustes problem
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Yujiao Shi, Fei Wu, Akhil Perincherry, Ankit Vora, and Hongdong Li · 2023
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View consistent purification for accurate cross-view localization
Shan Wang, Yanhao Zhang, Akhil Perincherry, Ankit Vora, and Hongdong Li · 2023
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Convolutional cross-view pose estimation
Zimin Xia, Olaf Booij, and Julian FP Kooij · 2023
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Axel Barroso-Laguna, Sowmya Munukutla, Victor Adrian Prisacariu, and Eric Brachmann · 2024
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Statewide visual geolocalization in the wild
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Beyond cross-view image retrieval: Highly accurate vehicle localization using satellite image
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Google maps static api, 2024
Google · 2024
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Weakly-supervised camera localization by ground-to-satellite image registration
Yujiao Shi, Hongdong Li, Akhil Perincherry, and Ankit Vora · 2024
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Learning dense flow field for highly-accurate cross-view camera localization
Zhenbo Song, Xianghui Ze, Jianfeng Lu, and Yujiao Shi · 2024
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Adapting fine-grained cross-view localization to areas without fine ground truth
Zimin Xia, Yujiao Shi, Hongdong Li, and Julian FP Kooij · 2024
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Data issues in high-definition maps furniture–a survey
Andi Zang, Runsheng Xu, Goce Trajcevski, and Fan Zhou · 2024
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