1 code implementation • 22 Aug 2023 • Tommaso Apicella, Alessio Xompero, Edoardo Ragusa, Riccardo Berta, Andrea Cavallaro, Paolo Gastaldo
To train the model, we annotated the visual affordances of an existing dataset with mixed-reality images of hand-held containers in third-person (exocentric) images.
no code implementations • 18 Nov 2022 • Xavier Weber, Alessio Xompero, Andrea Cavallaro
In this paper, we present a mixed-reality dataset of hand-occluded containers for category-level 6D object pose and size estimation.
no code implementations • 24 Aug 2022 • Alessio Xompero, Andrea Cavallaro
We propose a decentralised view-overlap recognition framework that operates across freely moving cameras without the need of a reference 3D map.
no code implementations • 27 Jul 2021 • Alessio Xompero, Santiago Donaher, Vladimir Iashin, Francesca Palermo, Gökhan Solak, Claudio Coppola, Reina Ishikawa, Yuichi Nagao, Ryo Hachiuma, Qi Liu, Fan Feng, Chuanlin Lan, Rosa H. M. Chan, Guilherme Christmann, Jyun-Ting Song, Gonuguntla Neeharika, Chinnakotla Krishna Teja Reddy, Dinesh Jain, Bakhtawar Ur Rehman, Andrea Cavallaro
In this paper, we present a range of methods and an open framework to benchmark acoustic and visual perception for the estimation of the capacity of a container, and the type, mass, and amount of its content.
no code implementations • 8 Feb 2021 • Apostolos Modas, Alessio Xompero, Ricardo Sanchez-Matilla, Pascal Frossard, Andrea Cavallaro
We investigate the problem of classifying - from a single image - the level of content in a cup or a drinking glass.
1 code implementation • 27 Nov 2019 • Alessio Xompero, Ricardo Sanchez-Matilla, Apostolos Modas, Pascal Frossard, Andrea Cavallaro
The 3D localisation of an object and the estimation of its properties, such as shape and dimensions, are challenging under varying degrees of transparency and lighting conditions.
no code implementations • 4 Sep 2015 • Sileye . Ba, Xavier Alameda-Pineda, Alessio Xompero, Radu Horaud
In this paper, we propose an on-line variational Bayesian model for multi-person tracking from cluttered visual observations provided by person detectors.