Fast Relative Pose Estimation using Relative Depth

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F24%3A00380521" target="_blank" >RIV/68407700:21230/24:00380521 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1109/3DV62453.2024.00053" target="_blank" >https://doi.org/10.1109/3DV62453.2024.00053</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/3DV62453.2024.00053" target="_blank" >10.1109/3DV62453.2024.00053</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Fast Relative Pose Estimation using Relative Depth

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Fast Relative Pose Estimation using Relative Depth

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název statě ve sborníku

3DV2024: Proceedings of the 2024 International Conference in 3D Vision

ISBN

979-8-3503-6246-6

ISSN

2378-3826

e-ISSN

2475-7888

Počet stran výsledku

9

Strana od-do

873-881

Název nakladatele

IEEE Computer Society

Místo vydání

Los Alamitos

Místo konání akce

Davos

Datum konání akce

18. 3. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001250581700071