RePoseD: Efficient Relative Pose Estimation With Known Depth Information

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/68407700:21730/25:00388366

Výsledek na webu

<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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Jazyk výsledku

angličtina

Název v původním jazyce

RePoseD: Efficient Relative Pose Estimation With Known Depth Information

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

RePoseD: Efficient Relative Pose Estimation With Known Depth Information

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

ICCV2025: Proceedings of the International Conference on Computer Vision

ISBN

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ISSN

1550-5499

e-ISSN

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Počet stran výsledku

11

Strana od-do

14876-14886

Název nakladatele

IEEE Communications Society

Místo vydání

Anchorage

Místo konání akce

Honolulu

Datum konání akce

19. 10. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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