132130 20260113073156.0 doi 10.1109/ICCV51070.2023.00483 sideral 137283 ART-2024-137283 eng Mur-Labadia, Lorenzo Universidad de Zaragoza Multi-label affordance mapping from egocentric vision 2024 Access copy available to the general public Unrestricted Accurate affordance detection and segmentation with pixel precision is an important piece in many complex systems based on interactions, such as robots and assitive devices. We present a new approach to affordance perception which enables accurate multi-label segmentation. Our approach can be used to automatically extract grounded affordances from first person videos of interactions using a 3D map of the environment providing pixel level precision for the affordance location. We use this method to build the largest and most complete dataset on affordances based on the EPIC-Kitchen dataset, EPIC-Aff, which provides interaction-grounded, multi-label, metric and spatial affordance annotations. Then, we propose a new approach to affordance segmentation based on multi-label detection which enables multiple affordances to co-exists in the same space, for example if they are associated with the same object. We present several strategies of multi-label detection using several segmentation architectures. The experimental results highlight the importance of the multi-label detection. Finally, we show how our metric representation can be exploited for build a map of interaction hotspots in spatial action-centric zones and use that representation to perform a task-oriented navigation. info:eu-repo/grantAgreement/ES/DGA/T45-23R info:eu-repo/grantAgreement/ES/MICINN-AEI/PID2021-125209OB-I00 info:eu-repo/grantAgreement/EUR/MICINN/TED2021-129410B-I00 info:eu-repo/grantAgreement/EUR/MICINN/TED2021-131150B-I00 info:eu-repo/semantics/openAccess All rights reserved http://www.europeana.eu/rights/rr-f/ 3.544 2024 Software 2024 Computer Vision and Pattern Recognition 2024 38.1 2024 info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion (orcid)0000-0001-5209-2267 Guerrero, José J. Universidad de Zaragoza (orcid)0000-0002-6741-844X Martínez-Cantín, Rubén Universidad de Zaragoza 5007 520 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Ingen.Sistemas y Automát. 2023 (2024), 5215-5226 Proceedings (IEEE Int. Conf. Comput. Vision) Proceedings (IEEE International Conference on Computer Vision) 1550-5499 7690358 http://zaguan.unizar.es/record/132130/files/texto_completo.pdf Postprint info:eu-repo/date/embargoEnd/2025-01-15 2650642 http://zaguan.unizar.es/record/132130/files/texto_completo.jpg?subformat=icon icon Postprint info:eu-repo/date/embargoEnd/2025-01-15 oai:zaguan.unizar.es:132130 articulos driver 2026-01-12-12:38:27 ARTICLE