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Noisy One-Point Homographies are Surprisingly Good

Ding, Yaqing LU ; Astermark, Jonathan LU ; Oskarsson, Magnus LU orcid and Larsson, Viktor LU (2024) 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024 In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition p.5125-5134
Abstract

Two-view homography estimation is a classic and fundamental problem in computer vision. While conceptually simple, the problem quickly becomes challenging when multiple planes are visible in the image pair. Even with correct matches, each individual plane (homography) might have a very low number of inliers when comparing to the set of all correspondences. In practice, this requires a large number of RANSAC iterations to generate a good model hypothesis. The current state-of-the-art methods therefore seek to reduce the sample size, from four point correspondences originally, by including additional information such as keypoint orientation/angles or local affine information. In this work, we continue in this direction and propose a novel... (More)

Two-view homography estimation is a classic and fundamental problem in computer vision. While conceptually simple, the problem quickly becomes challenging when multiple planes are visible in the image pair. Even with correct matches, each individual plane (homography) might have a very low number of inliers when comparing to the set of all correspondences. In practice, this requires a large number of RANSAC iterations to generate a good model hypothesis. The current state-of-the-art methods therefore seek to reduce the sample size, from four point correspondences originally, by including additional information such as keypoint orientation/angles or local affine information. In this work, we continue in this direction and propose a novel one-point solver that leverages different approximate constraints derived from the same auxiliary information. In experiments we obtain state-of-the-art results, with execution time speed-ups, on large benchmark datasets and show that it is more beneficial for the solver to be sample efficient compared to generating more accurate homographies.

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Please use this url to cite or link to this publication:
author
; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Homography, Relative pose, SIFT
host publication
Proceedings - 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024
series title
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
pages
10 pages
publisher
IEEE Computer Society
conference name
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024
conference location
Seattle, United States
conference dates
2024-06-16 - 2024-06-22
external identifiers
  • scopus:85207303475
ISSN
1063-6919
ISBN
9798350353006
DOI
10.1109/CVPR52733.2024.00490
language
English
LU publication?
yes
additional info
Publisher Copyright: © 2024 IEEE.
id
e49eff51-91f2-4a9f-b0ae-cae6e5d24733
date added to LUP
2024-12-17 11:48:26
date last changed
2025-04-04 15:07:11
@inproceedings{e49eff51-91f2-4a9f-b0ae-cae6e5d24733,
  abstract     = {{<p>Two-view homography estimation is a classic and fundamental problem in computer vision. While conceptually simple, the problem quickly becomes challenging when multiple planes are visible in the image pair. Even with correct matches, each individual plane (homography) might have a very low number of inliers when comparing to the set of all correspondences. In practice, this requires a large number of RANSAC iterations to generate a good model hypothesis. The current state-of-the-art methods therefore seek to reduce the sample size, from four point correspondences originally, by including additional information such as keypoint orientation/angles or local affine information. In this work, we continue in this direction and propose a novel one-point solver that leverages different approximate constraints derived from the same auxiliary information. In experiments we obtain state-of-the-art results, with execution time speed-ups, on large benchmark datasets and show that it is more beneficial for the solver to be sample efficient compared to generating more accurate homographies.</p>}},
  author       = {{Ding, Yaqing and Astermark, Jonathan and Oskarsson, Magnus and Larsson, Viktor}},
  booktitle    = {{Proceedings - 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024}},
  isbn         = {{9798350353006}},
  issn         = {{1063-6919}},
  keywords     = {{Homography; Relative pose; SIFT}},
  language     = {{eng}},
  pages        = {{5125--5134}},
  publisher    = {{IEEE Computer Society}},
  series       = {{Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition}},
  title        = {{Noisy One-Point Homographies are Surprisingly Good}},
  url          = {{http://dx.doi.org/10.1109/CVPR52733.2024.00490}},
  doi          = {{10.1109/CVPR52733.2024.00490}},
  year         = {{2024}},
}