Reference Pose Generation for Long-term Visual Localization via Learned Features and View Synthesis

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F21%3A00347016" target="_blank" >RIV/68407700:21730/21:00347016 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1007/s11263-020-01399-8" target="_blank" >https://doi.org/10.1007/s11263-020-01399-8</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s11263-020-01399-8" target="_blank" >10.1007/s11263-020-01399-8</a>

Result language

angličtina

Original language name

Reference Pose Generation for Long-term Visual Localization via Learned Features and View Synthesis

Original language description

Visual Localization is one of the key enabling technologies for autonomous driving and augmented reality. High quality datasets with accurate 6 Degree-of-Freedom (DoF) reference poses are the foundation for benchmarking and improving existing methods. Traditionally, reference poses have been obtained via Structure-from-Motion (SfM). However, SfM itself relies on local features which are prone to fail when images were taken under different conditions, e.g., day/night changes. At the same time, manually annotating feature correspondences is not scalable and potentially inaccurate. In this work, we propose a semi-automated approach to generate reference poses based on feature matching between renderings of a 3D model and real images via learned features. Given an initial pose estimate, our approach iteratively refines the pose based on feature matches against a rendering of the model from the current pose estimate. We significantly improve the nighttime reference poses of the popular Aachen Day–Night dataset, showing that state-of-the-art visual localization methods perform better (up to 47%) than predicted by the original reference poses. We extend the dataset with new nighttime test images, provide uncertainty estimates for our new reference poses, and introduce a new evaluation criterion. We will make our reference poses and our framework publicly available upon publication.

Czech name

—

Czech description

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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

2021

Confidentiality

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

Name of the periodical

International Journal of Computer Vision

ISSN

0920-5691

e-ISSN

1573-1405

Volume of the periodical

129

Issue of the periodical within the volume

4

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

24

Pages from-to

821-844

UT code for WoS article

000601485200002

EID of the result in the Scopus database

2-s2.0-85099510872