@inproceedings{c86c10b3-6b3a-4dda-abc8-37a7ab8402ad,
  abstract     = {{We present ON3R, an online-trained neural regressor addressing sparse-view structureless localization, where database images have limited visual overlap and no prebuilt 3D map. Given any sparse matches between a query and a K-tuple of posed database views, ON3R predicts 3D coordinates for matched query keypoints, supervised by database reprojection residuals and a monocular depth prior. Afterwards, the absolute pose of the query is estimated via P3P-RANSAC and refined with lightweight bundle adjustment. Across MegaDepth, Cambridge Landmarks, and a sparsified version of Aachen Day-Night, ON3R outperforms existing methods. ON3R is particularly effective when the data is extremely sparse – we focus on K ≤ 10 database images. The code is available athttps://github.com/ludvigdillen/ON3R.}},
  author       = {{Dillén, Ludvig and Oskarsson, Magnus and Larsson, Viktor}},
  booktitle    = {{Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}},
  keywords     = {{Pose estimation; localization}},
  language     = {{eng}},
  pages        = {{21794--21804}},
  publisher    = {{IEEE Computer Society}},
  title        = {{Sparse–View Localization via Online Neural 3D Regression}},
  url          = {{https://openaccess.thecvf.com/content/CVPR2026/papers/Dillen_Sparse-View_Localization_via_Online_Neural_3D_Regression_CVPR_2026_paper.pdf}},
  year         = {{2026}},
}