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Fast Relative Pose Estimation using Relative Depth

Astermark, Jonathan LU ; Ding, Yaqing LU ; Larsson, Viktor LU and Heyden, Anders LU orcid (2024) 11th International Conference on 3D Vision, 3DV 2024 In Proceedings - 2024 International Conference on 3D Vision, 3DV 2024 p.873-881
Abstract

In this paper, we revisit the problem of estimating the relative pose from a sparse set of point-correspondences. For each point-correspondence we also estimate the relative depth, i.e. the relative distance to the scene point in the two images. This yields an additional constraint, allowing us to use fewer matches in RANSAC to generate the pose candidates. In the paper we propose two novel minimal solvers: one for general motion and one for the case of known vertical direction. To obtain the relative depth estimates, we explore using scale estimates obtained from a keypoint detector as well as a neural network that directly predicts the relative depth for a pair of patches. We show in experiments that while our estimates are more noisy... (More)

In this paper, we revisit the problem of estimating the relative pose from a sparse set of point-correspondences. For each point-correspondence we also estimate the relative depth, i.e. the relative distance to the scene point in the two images. This yields an additional constraint, allowing us to use fewer matches in RANSAC to generate the pose candidates. In the paper we propose two novel minimal solvers: one for general motion and one for the case of known vertical direction. To obtain the relative depth estimates, we explore using scale estimates obtained from a keypoint detector as well as a neural network that directly predicts the relative depth for a pair of patches. We show in experiments that while our estimates are more noisy compared to the purely point-based solvers, the smaller sample size leads to a significantly reduced runtime in settings with high outlier ratios.

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author
; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
epipolar geometry, minimal solver, RANSAC, relative depth, Relative pose, SIFT
host publication
Proceedings - 2024 International Conference on 3D Vision, 3DV 2024
series title
Proceedings - 2024 International Conference on 3D Vision, 3DV 2024
pages
9 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
11th International Conference on 3D Vision, 3DV 2024
conference location
Davos, Switzerland
conference dates
2024-03-18 - 2024-03-21
external identifiers
  • scopus:85196743754
ISBN
9798350362459
DOI
10.1109/3DV62453.2024.00053
language
English
LU publication?
yes
id
dba2f06b-76f5-43d2-89c2-b7fa7f39a590
date added to LUP
2024-08-29 15:41:59
date last changed
2025-03-14 10:53:24
@inproceedings{dba2f06b-76f5-43d2-89c2-b7fa7f39a590,
  abstract     = {{<p>In this paper, we revisit the problem of estimating the relative pose from a sparse set of point-correspondences. For each point-correspondence we also estimate the relative depth, i.e. the relative distance to the scene point in the two images. This yields an additional constraint, allowing us to use fewer matches in RANSAC to generate the pose candidates. In the paper we propose two novel minimal solvers: one for general motion and one for the case of known vertical direction. To obtain the relative depth estimates, we explore using scale estimates obtained from a keypoint detector as well as a neural network that directly predicts the relative depth for a pair of patches. We show in experiments that while our estimates are more noisy compared to the purely point-based solvers, the smaller sample size leads to a significantly reduced runtime in settings with high outlier ratios.</p>}},
  author       = {{Astermark, Jonathan and Ding, Yaqing and Larsson, Viktor and Heyden, Anders}},
  booktitle    = {{Proceedings - 2024 International Conference on 3D Vision, 3DV 2024}},
  isbn         = {{9798350362459}},
  keywords     = {{epipolar geometry; minimal solver; RANSAC; relative depth; Relative pose; SIFT}},
  language     = {{eng}},
  pages        = {{873--881}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  series       = {{Proceedings - 2024 International Conference on 3D Vision, 3DV 2024}},
  title        = {{Fast Relative Pose Estimation using Relative Depth}},
  url          = {{http://dx.doi.org/10.1109/3DV62453.2024.00053}},
  doi          = {{10.1109/3DV62453.2024.00053}},
  year         = {{2024}},
}