A Large-Scale Homography Benchmark

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F23%3A00367030" target="_blank" >RIV/68407700:21230/23:00367030 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21730/23:00367030

Výsledek na webu

<a href="https://doi.org/10.1109/CVPR52729.2023.02046" target="_blank" >https://doi.org/10.1109/CVPR52729.2023.02046</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/CVPR52729.2023.02046" target="_blank" >10.1109/CVPR52729.2023.02046</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A Large-Scale Homography Benchmark

Popis výsledku v původním jazyce

We present a large-scale dataset of Planes in 3D, Pi3D, of roughly 1000 planes observed in 10 000 images from the 1DSfM dataset, and HEB, a large-scale homography estimation benchmark leveraging Pi3D. The applications of the Pi3D dataset are diverse, e.g. training or evaluating monocular depth, surface normal estimation and image matching algorithms. The HEB dataset consists of 226 260 homographies and includes roughly 4M correspondences. The homographies link images that often undergo significant viewpoint and illumination changes. As applications of HEB, we perform a rigorous evaluation of a wide range of robust estimators and deep learning-based correspondence filtering methods, establishing the current state-of- the-art in robust homography estimation. We also evalu- ate the uncertainty of the SIFT orientations and scales w.r.t. the ground truth coming from the underlying homographies and provide codes for comparing uncertainty of custom de- tectors. The dataset is available at https://github.com/danini/homography-benchmark.

Název v anglickém jazyce

A Large-Scale Homography Benchmark

Popis výsledku anglicky

We present a large-scale dataset of Planes in 3D, Pi3D, of roughly 1000 planes observed in 10 000 images from the 1DSfM dataset, and HEB, a large-scale homography estimation benchmark leveraging Pi3D. The applications of the Pi3D dataset are diverse, e.g. training or evaluating monocular depth, surface normal estimation and image matching algorithms. The HEB dataset consists of 226 260 homographies and includes roughly 4M correspondences. The homographies link images that often undergo significant viewpoint and illumination changes. As applications of HEB, we perform a rigorous evaluation of a wide range of robust estimators and deep learning-based correspondence filtering methods, establishing the current state-of- the-art in robust homography estimation. We also evalu- ate the uncertainty of the SIFT orientations and scales w.r.t. the ground truth coming from the underlying homographies and provide codes for comparing uncertainty of custom de- tectors. The dataset is available at https://github.com/danini/homography-benchmark.

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

<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

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

ISBN

979-8-3503-0129-8

ISSN

1063-6919

e-ISSN

2575-7075

Počet stran výsledku

11

Strana od-do

21360-21370

Název nakladatele

IEEE Computer Society

Místo vydání

USA

Místo konání akce

Vancouver

Datum konání akce

18. 6. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001062531305067