Render-and-Compare: Cross-View 6 DoF Localization from Noisy Prior
This addresses the problem of scalable and complete visual localization for applications like mapping and navigation, though it is incremental as it builds on existing render-and-compare methods.
The paper tackles cross-view 6-DoF localization from aerial to ground images by proposing an iterative render-and-compare pipeline with robustness enhancements from noisy priors, and it outperforms state-of-the-art baselines by a large margin on a newly collected dataset.
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.