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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2302.06287 |
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| _version_ | 1866909243819425792 |
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| author | Yan, Shen Cheng, Xiaoya Liu, Yuxiang Zhu, Juelin Wu, Rouwan Liu, Yu Zhang, Maojun |
| author_facet | Yan, Shen Cheng, Xiaoya Liu, Yuxiang Zhu, Juelin Wu, Rouwan Liu, Yu Zhang, Maojun |
| contents | Despite the significant progress in 6-DoF visual localization, researchers are mostly driven by ground-level benchmarks. Compared with aerial oblique photography, ground-level map collection lacks scalability and complete coverage. In this work, we propose to go beyond the traditional ground-level setting and exploit the cross-view localization from aerial to ground. We solve this problem by formulating camera pose estimation as an iterative render-and-compare pipeline and enhancing the robustness through augmenting seeds from noisy initial priors. As no public dataset exists for the studied problem, we collect a new dataset that provides a variety of cross-view images from smartphones and drones and develop a semi-automatic system to acquire ground-truth poses for query images. We benchmark our method as well as several state-of-the-art baselines and demonstrate that our method outperforms other approaches by a large margin. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2302_06287 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Render-and-Compare: Cross-View 6 DoF Localization from Noisy Prior Yan, Shen Cheng, Xiaoya Liu, Yuxiang Zhu, Juelin Wu, Rouwan Liu, Yu Zhang, Maojun Computer Vision and Pattern Recognition Despite the significant progress in 6-DoF visual localization, researchers are mostly driven by ground-level benchmarks. Compared with aerial oblique photography, ground-level map collection lacks scalability and complete coverage. In this work, we propose to go beyond the traditional ground-level setting and exploit the cross-view localization from aerial to ground. We solve this problem by formulating camera pose estimation as an iterative render-and-compare pipeline and enhancing the robustness through augmenting seeds from noisy initial priors. As no public dataset exists for the studied problem, we collect a new dataset that provides a variety of cross-view images from smartphones and drones and develop a semi-automatic system to acquire ground-truth poses for query images. We benchmark our method as well as several state-of-the-art baselines and demonstrate that our method outperforms other approaches by a large margin. |
| title | Render-and-Compare: Cross-View 6 DoF Localization from Noisy Prior |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2302.06287 |