Homography-based Egomotion Estimation Using Gravity and SIFT Features

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F21%3A00345989" target="_blank" >RIV/68407700:21230/21:00345989 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1007/978-3-030-69525-5_17" target="_blank" >https://doi.org/10.1007/978-3-030-69525-5_17</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-69525-5_17" target="_blank" >10.1007/978-3-030-69525-5_17</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Homography-based Egomotion Estimation Using Gravity and SIFT Features

Popis výsledku v původním jazyce

Camera systems used, eg, in cars, UAVs, smartphones, and tablets, are typically equipped with IMUs (inertial measurement units) that can measure the gravity vector. Using the information from an IMU, the y-axes of cameras can be aligned with the gravity, reducing their relative orientation to a single DOF (degree of freedom). In this paper, we use the gravity information to derive extremely efficient minimal solvers for homography-based egomotion estimation from orientation-and scale-covariant features. We use the fact that orientation-and scale-covariant features, such as SIFT or ORB, provide additional constraints on the homography. Based on the prior knowledge about the target plane (horizontal/vertical/general plane, wrt the gravity direction) and using the SIFT/ORB constraints, we derive new minimal solvers that require fewer correspondences than traditional approaches and, thus, speed up the robust estimation procedure significantly. The proposed solvers are compared with the state-of-the-art point-based solvers on both synthetic data and real images, showing comparable accuracy and significant improvement in terms of speed. The implementation of our solvers is available at https://github. com/yaqding/relativepose-sift-gravity.

Název v anglickém jazyce

Homography-based Egomotion Estimation Using Gravity and SIFT Features

Popis výsledku anglicky

Camera systems used, eg, in cars, UAVs, smartphones, and tablets, are typically equipped with IMUs (inertial measurement units) that can measure the gravity vector. Using the information from an IMU, the y-axes of cameras can be aligned with the gravity, reducing their relative orientation to a single DOF (degree of freedom). In this paper, we use the gravity information to derive extremely efficient minimal solvers for homography-based egomotion estimation from orientation-and scale-covariant features. We use the fact that orientation-and scale-covariant features, such as SIFT or ORB, provide additional constraints on the homography. Based on the prior knowledge about the target plane (horizontal/vertical/general plane, wrt the gravity direction) and using the SIFT/ORB constraints, we derive new minimal solvers that require fewer correspondences than traditional approaches and, thus, speed up the robust estimation procedure significantly. The proposed solvers are compared with the state-of-the-art point-based solvers on both synthetic data and real images, showing comparable accuracy and significant improvement in terms of speed. The implementation of our solvers is available at https://github. com/yaqding/relativepose-sift-gravity.

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

<a href="/cs/project/EF16_019%2F0000765" target="_blank" >EF16_019/0000765: Výzkumné centrum informatiky</a><br>

Návaznosti

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

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název statě ve sborníku

ACCV 2020: Proceedings of the 14th Asian Conference on Computer Vision, Part I

ISBN

978-3-030-69524-8

ISSN

0302-9743

e-ISSN

1611-3349

Počet stran výsledku

17

Strana od-do

278-294

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Kyoto

Datum konání akce

30. 11. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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