RePoseD: Efficient Relative Pose Estimation With Known Depth Information
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F25%3A00388366" target="_blank" >RIV/68407700:21230/25:00388366 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21730/25:00388366
Result on the web
<a href="https://openaccess.thecvf.com/content/ICCV2025/papers/Ding_RePoseD_Efficient_Relative_Pose_Estimation_With_Known_Depth_Information_ICCV_2025_paper.pdf" target="_blank" >https://openaccess.thecvf.com/content/ICCV2025/papers/Ding_RePoseD_Efficient_Relative_Pose_Estimation_With_Known_Depth_Information_ICCV_2025_paper.pdf</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
RePoseD: Efficient Relative Pose Estimation With Known Depth Information
Original language description
Recent advances in monocular depth estimation methods (MDEs) and their improved accuracy open new possibilities for their applications. In this paper, we investigate how monocular depth estimates can be used for relative pose estimation. In particular, we are interested in answering the question whether using MDEs improves results over traditional point-based methods. We propose a novel framework for estimating the relative pose of two cameras from point correspondences with associated monocular depths. Since depth predictions are typically defined up to an unknown scale or even both unknown scale and shift parameters, our solvers jointly estimate the scale or both the scale and shift parameters along with the relative pose. We derive efficient solvers considering different types of depths for three camera configurations: (1) two calibrated cameras, (2) two cameras with an unknown shared focal length, and (3) two cameras with unknown different focal lengths. Our new solvers outperform stateof-the-art depth-aware solvers in terms of speed and accuracy. In extensive real experiments on multiple datasets and with various MDEs, we discuss which depth-aware solvers are preferable in which situation. The code is available at https://github.com/kocurvik/mdrp.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
ICCV2025: Proceedings of the International Conference on Computer Vision
ISBN
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ISSN
1550-5499
e-ISSN
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Number of pages
11
Pages from-to
14876-14886
Publisher name
IEEE Communications Society
Place of publication
Anchorage
Event location
Honolulu
Event date
Oct 19, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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