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Sparse–View Localization via Online Neural 3D Regression

Dillén, Ludvig LU orcid ; Oskarsson, Magnus LU orcid and Larsson, Viktor LU orcid (2026) p.21794-21804
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... (More)
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. (Less)
Please use this url to cite or link to this publication:
author
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organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Pose estimation, localization
host publication
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
pages
11 pages
publisher
IEEE Computer Society
language
English
LU publication?
yes
id
c86c10b3-6b3a-4dda-abc8-37a7ab8402ad
alternative location
https://openaccess.thecvf.com/content/CVPR2026/papers/Dillen_Sparse-View_Localization_via_Online_Neural_3D_Regression_CVPR_2026_paper.pdf
date added to LUP
2026-06-26 12:36:05
date last changed
2026-09-23 13:19:01
@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}},
}