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arXiv · 2302.06287

Render-and-Compare: Cross-View 6 DoF Localization from Noisy Prior

Shen Yan·Xiaoya Cheng·Yuxiang Liu·Juelin Zhu·Rouwan Wu·Yu Liu·Maojun Zhang
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Abstract

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.

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Shen Yan, Xiaoya Cheng, Yuxiang Liu, Juelin Zhu, Rouwan Wu, Yu Liu, Maojun Zhang. 2023-02-13. Render-and-Compare: Cross-View 6 DoF Localization from Noisy Prior. https://arxiv.org/abs/2302.06287

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