106609 20210902121837.0 doi 10.1109/CVPR42600.2020.00270 sideral 121225 ART-2020-121225 eng Warburg, F. Mapillary street-level sequences: A dataset for lifelong place recognition 2020 Access copy available to the general public Unrestricted Lifelong place recognition is an essential and challenging task in computer vision with vast applications in robust localization and efficient large-scale 3D reconstruction. Progress is currently hindered by a lack of large, diverse, publicly available datasets. We contribute with Mapillary Street-Level Sequences (SLS), a large dataset for urban and suburban place recognition from image sequences. It contains more than 1.6 million images curated from the Mapillary collaborative mapping platform. The dataset is orders of magnitude larger than current data sources, and is designed to reflect the diversities of true lifelong learning. It features images from 30 major cities across six continents, hundreds of distinct cameras, and substantially different viewpoints and capture times, spanning all seasons over a nine year period. All images are geo-located with GPS and compass, and feature high-level attributes such as road type. We propose a set of benchmark tasks designed to push state-of-the-art performance and provide baseline studies. We show that current state-of-the-art methods still have a long way to go, and that the lack of diversity in existing datasets have prevented generalization to new environments. The dataset and benchmarks are available for academic research. info:eu-repo/grantAgreement/ES/DGA-FSE/T45-17R info:eu-repo/grantAgreement/EC/H2020/757360/EU/Measuring with no tape/NoTape This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 757360-NoTape info:eu-repo/grantAgreement/ES/MCIU-AEI-FEDER/PGC2018-096367-B-I00 info:eu-repo/semantics/openAccess All rights reserved http://www.europeana.eu/rights/rr-f/ 4.658 2020 Software 2020 Computer Vision and Pattern Recognition 2020 info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion Hauberg, S. López-Antequera, M. Gargallo, P. Kuang, Y. Civera, J. Universidad de Zaragoza (orcid)0000-0003-1368-1151 5007 520 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Ingen.Sistemas y Automát. (2020), 2623-2632 Proc.- IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition 1063-6919 4367058 http://zaguan.unizar.es/record/106609/files/texto_completo.pdf Postprint 2638144 http://zaguan.unizar.es/record/106609/files/texto_completo.jpg?subformat=icon icon Postprint oai:zaguan.unizar.es:106609 articulos driver 2021-09-02-10:21:11 ARTICLE