@inproceedings{7978aa9d-21d8-44d6-b304-adf39982b356,
  abstract     = {{In this paper, we present a pipeline for visual localization using 2D-3D matching, yielding high localization accuracy comparable with hierarchical methods based on 2D-2D matching. This is accomplished through an active exploration strategy, which adaptively explores multiple parts of the scene to find the region that generates the most inlier matches, followed by refining the pose with a pose-to-pixel uncertainty re-matching scheme. To justify our design choices, we conduct extensive ablations demonstrating clear improvements over related approaches. Our method achieves competitive accuracy on the large-scale benchmarks Aachen Day & Night, RobotCar, CMU Seasons, and Gangnam Station, while maintaining a low runtime. The code can be found at https://github.com/jojjo99/active_exploration.}},
  author       = {{Lidholm, Johanna and Dillén, Ludvig and Kukelova, Zuzana and Sattler, Torsten and Larsson, Viktor}},
  booktitle    = {{Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Findings}},
  keywords     = {{Localization; 2D-3D matching; Image Retrieval}},
  language     = {{eng}},
  pages        = {{338--347}},
  publisher    = {{IEEE Computer Society}},
  title        = {{Active Exploration for Sparse Visual Localization}},
  url          = {{https://openaccess.thecvf.com/content/CVPR2026F/papers/Lidholm_Active_Exploration_for_Sparse_Visual_Localization_CVPRF_2026_paper.pdf}},
  year         = {{2026}},
}