Long-Term Visual Localization Revisited

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F22%3A00343643" target="_blank" >RIV/68407700:21730/22:00343643 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1109/TPAMI.2020.3032010" target="_blank" >https://doi.org/10.1109/TPAMI.2020.3032010</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TPAMI.2020.3032010" target="_blank" >10.1109/TPAMI.2020.3032010</a>

Result language

angličtina

Original language name

Long-Term Visual Localization Revisited

Original language description

Visual localization enables autonomous vehicles to navigate in their surroundings and augmented reality applications to link virtual to real worlds. Practical visual localization approaches need to be robust to a wide variety of viewing conditions, including day-night changes, as well as weather and seasonal variations, while providing highly accurate six degree-of-freedom (6DOF) camera pose estimates. In this paper, we extend three publicly available datasets containing images captured under a wide variety of viewing conditions, but lacking camera pose information, with ground truth pose information, making evaluation of the impact of various factors on 6DOF camera pose estimation accuracy possible. We also discuss the performance of state-of-the-art localization approaches on these datasets. Additionally, we release around half of the poses for all conditions, and keep the remaining half private as a test set, in the hopes that this will stimulate research on long-term visual localization, learned local image features, and related research areas. Our datasets are available at visuallocalization.net, where we are also hosting a benchmarking server for automatic evaluation of results on the test set. The presented state-of-the-art results are to a large degree based on submissions to our server.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

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/EF15_003%2F0000468" target="_blank" >EF15_003/0000468: Intelligent Machine Perception</a><br>

Continuities

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

Publication year

2022

Confidentiality

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

Name of the periodical

IEEE Transactions on Pattern Analysis and Machine Intelligence

ISSN

0162-8828

e-ISSN

1939-3539

Volume of the periodical

44

Issue of the periodical within the volume

4

Country of publishing house

US - UNITED STATES

Number of pages

15

Pages from-to

2074-2088

UT code for WoS article

000764815300031

EID of the result in the Scopus database

2-s2.0-85125883764