Multi-label Affordance Mapping from Egocentric Vision
Lorenzo Mur-Labadia, Jose J. Guerrero, Ruben Martinez-Cantin
Abstract
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 annotate 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 automatic, 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 highlights 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.
BibTeX
@inproceedings{iccv2023_multilabelafford,
title = {Multi-label Affordance Mapping from Egocentric Vision},
author = {Lorenzo Mur-Labadia and Jose J. Guerrero and Ruben Martinez-Cantin},
booktitle = {ICCV 2023},
year = {2023}
}