doi:10.1109/ICCV51070.2023.00483engMur-Labadia, LorenzoGuerrero, José J.Martínez-Cantín, RubénMulti-label affordance mapping from egocentric visionART-2024-137283Accurate 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.2024http://zaguan.unizar.es/record/13213010.1109/ICCV51070.2023.00483http://zaguan.unizar.es/record/132130oai:zaguan.unizar.es:132130info:eu-repo/grantAgreement/ES/DGA/T45-23Rinfo:eu-repo/grantAgreement/ES/MICINN-AEI/PID2021-125209OB-I00info:eu-repo/grantAgreement/EUR/MICINN/TED2021-129410B-I00info:eu-repo/grantAgreement/EUR/MICINN/TED2021-131150B-I00Proceedings (IEEE International Conference on Computer Vision) 2023 (2024), 5215-5226All rights reservedhttp://www.europeana.eu/rights/rr-f/info:eu-repo/semantics/openAccess