000132130 001__ 132130 000132130 005__ 20250910073137.0 000132130 0247_ $$2doi$$a10.1109/ICCV51070.2023.00483 000132130 0248_ $$2sideral$$a137283 000132130 037__ $$aART-2024-137283 000132130 041__ $$aeng 000132130 100__ $$aMur-Labadia, Lorenzo$$uUniversidad de Zaragoza 000132130 245__ $$aMulti-label affordance mapping from egocentric vision 000132130 260__ $$c2024 000132130 5060_ $$aAccess copy available to the general public$$fUnrestricted 000132130 5203_ $$aAccurate affordance detection and segmentation with pixel precision is an important piece in many complex systems based on interactions, such as robots and assitive devices. We present a new approach to affordance perception which enables accurate multi-label segmentation. Our approach can be used to automatically extract grounded affordances from first person videos of interactions using a 3D map of the environment providing pixel level precision for the affordance location. We use this method to build the largest and most complete dataset on affordances based on the EPIC-Kitchen dataset, EPIC-Aff, which provides interaction-grounded, multi-label, metric and spatial affordance annotations. Then, we propose a new approach to affordance segmentation based on multi-label detection which enables multiple affordances to co-exists in the same space, for example if they are associated with the same object. We present several strategies of multi-label detection using several segmentation architectures. The experimental results highlight the importance of the multi-label detection. Finally, we show how our metric representation can be exploited for build a map of interaction hotspots in spatial action-centric zones and use that representation to perform a task-oriented navigation. 000132130 536__ $$9info:eu-repo/grantAgreement/ES/DGA/T45-23R$$9info:eu-repo/grantAgreement/ES/MICINN-AEI/PID2021-125209OB-I00$$9info:eu-repo/grantAgreement/EUR/MICINN/TED2021-129410B-I00$$9info:eu-repo/grantAgreement/EUR/MICINN/TED2021-131150B-I00 000132130 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/ 000132130 592__ $$a3.544$$b2024 000132130 593__ $$aSoftware$$c2024 000132130 593__ $$aComputer Vision and Pattern Recognition$$c2024 000132130 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion 000132130 700__ $$0(orcid)0000-0001-5209-2267$$aGuerrero, José J.$$uUniversidad de Zaragoza 000132130 700__ $$0(orcid)0000-0002-6741-844X$$aMartínez-Cantín, Rubén$$uUniversidad de Zaragoza 000132130 7102_ $$15007$$2520$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Ingen.Sistemas y Automát. 000132130 773__ $$g2023 (2024), 5215-5226$$pProceedings (IEEE International Conference on Computer Vision)$$tProceedings (IEEE International Conference on Computer Vision)$$x1550-5499 000132130 8564_ $$s7690358$$uhttps://zaguan.unizar.es/record/132130/files/texto_completo.pdf$$yPostprint$$zinfo:eu-repo/date/embargoEnd/2025-01-15 000132130 8564_ $$s2650642$$uhttps://zaguan.unizar.es/record/132130/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint$$zinfo:eu-repo/date/embargoEnd/2025-01-15 000132130 909CO $$ooai:zaguan.unizar.es:132130$$particulos$$pdriver 000132130 951__ $$a2025-09-08-12:49:33 000132130 980__ $$aARTICLE