A Large-Scale Homography Benchmark

Result code in 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>

Alternative codes found

RIV/68407700:21730/23:00367030

Result on the web

<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>

Result language

angličtina

Original language name

A Large-Scale Homography Benchmark

Original language description

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.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

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

Project

<a href="/en/project/EF16_019%2F0000765" target="_blank" >EF16_019/0000765: Research Center for Informatics</a><br>

Continuities

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

Publication year

2023

Confidentiality

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

Article name in the collection

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

Number of pages

11

Pages from-to

21360-21370

Publisher name

IEEE Computer Society

Place of publication

USA

Event location

Vancouver

Event date

Jun 18, 2023

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

001062531305067