no code implementations • 13 May 2024 • Luigi Riz, Sergio Povoli, Andrea Caraffa, Davide Boscaini, Mohamed Lamine Mekhalfi, Paul Chippendale, Marjut Turtiainen, Birgitta Partanen, Laura Smith Ballester, Francisco Blanes Noguera, Alessio Franchi, Elisa Castelli, Giacomo Piccinini, Luca Marchesotti, Micael Santos Couceiro, Fabio Poiesi
Berry picking has long-standing traditions in Finland, yet it is challenging and can potentially be dangerous.
no code implementations • 1 Dec 2023 • Jaime Corsetti, Davide Boscaini, Changjae Oh, Andrea Cavallaro, Fabio Poiesi
We introduce the new setting of open-vocabulary object 6D pose estimation, in which a textual prompt is used to specify the object of interest.
no code implementations • 1 Dec 2023 • Andrea Caraffa, Davide Boscaini, Amir Hamza, Fabio Poiesi
We also introduce a novel algorithm to solve ambiguous cases due to geometrically symmetric objects that is based on visual features.
1 code implementation • 29 Aug 2023 • Mohamed L. Mekhalfi, Davide Boscaini, Fabio Poiesi
Unsupervised domain adaptation (UDA) plays a crucial role in object detection when adapting a source-trained detector to a target domain without annotated data.
1 code implementation • 28 Jul 2023 • Jaime Corsetti, Davide Boscaini, Fabio Poiesi
Recent works on 6D object pose estimation focus on learning keypoint correspondences between images and object models, and then determine the object pose through RANSAC-based algorithms or by directly regressing the pose with end-to-end optimisations.
1 code implementation • 28 Jul 2023 • Davide Boscaini, Fabio Poiesi
The recent trend in deep learning methods for 3D point cloud understanding is to propose increasingly sophisticated architectures either to better capture 3D geometries or by introducing possibly undesired inductive biases.
1 code implementation • 11 Apr 2023 • Luigi Riz, Andrea Caraffa, Matteo Bortolon, Mohamed Lamine Mekhalfi, Davide Boscaini, André Moura, José Antunes, André Dias, Hugo Silva, Andreas Leonidou, Christos Constantinides, Christos Keleshis, Dante Abate, Fabio Poiesi
MONET is different from previous thermal drone datasets because it features multimodal data, including rural scenes captured with thermal cameras containing both person and vehicle targets, along with trajectory information and metadata.
1 code implementation • 6 Dec 2022 • Pietro Astolfi, Ruben Verhagen, Laurent Petit, Emanuele Olivetti, Silvio Sarubbo, Jonathan Masci, Davide Boscaini, Paolo Avesani
A tractogram is a virtual representation of the brain white matter.
1 code implementation • 21 May 2021 • Fabio Poiesi, Davide Boscaini
An effective 3D descriptor should be invariant to different geometric transformations, such as scale and rotation, robust to occlusions and clutter, and capable of generalising to different application domains.
Ranked #1 on Point Cloud Registration on ETH (trained on 3DMatch)
2 code implementations • 1 Sep 2020 • Fabio Poiesi, Davide Boscaini
We present a simple but yet effective method for learning distinctive 3D local deep descriptors (DIPs) that can be used to register point clouds without requiring an initial alignment.
Ranked #2 on Point Cloud Registration on ETH (trained on 3DMatch)
no code implementations • 4 Aug 2020 • Levi O. Vasconcelos, Massimiliano Mancini, Davide Boscaini, Samuel Rota Bulo, Barbara Caputo, Elisa Ricci
Recent unsupervised domain adaptation methods based on deep architectures have shown remarkable performance not only in traditional classification tasks but also in more complex problems involving structured predictions (e. g. semantic segmentation, depth estimation).
1 code implementation • 6 Jul 2020 • Mohamed Ilyes Lakhal, Davide Boscaini, Fabio Poiesi, Oswald Lanz, Andrea Cavallaro
We first estimate the 3D mesh of the target body and transfer the rough textures from the 2D images to the mesh.
no code implementations • 15 Apr 2020 • Antonio Alliegro, Davide Boscaini, Tatiana Tommasi
Point cloud processing and 3D shape understanding are very challenging tasks for which deep learning techniques have demonstrated great potentials.
no code implementations • 24 Mar 2020 • Pietro Astolfi, Ruben Verhagen, Laurent Petit, Emanuele Olivetti, Jonathan Masci, Davide Boscaini, Paolo Avesani
The intuitive idea is to model a fiber as a point cloud and the goal is to investigate whether and how a geometric deep learning model might capture its anatomical properties.
no code implementations • 2 Feb 2020 • Davide Boscaini, Fabio Poiesi
The semantic segmentation of 3D shapes with a high-density of vertices could be impractical due to large memory requirements.
4 code implementations • CVPR 2017 • Federico Monti, Davide Boscaini, Jonathan Masci, Emanuele Rodolà, Jan Svoboda, Michael M. Bronstein
Recently, there has been an increasing interest in geometric deep learning, attempting to generalize deep learning methods to non-Euclidean structured data such as graphs and manifolds, with a variety of applications from the domains of network analysis, computational social science, or computer graphics.
Ranked #4 on Document Classification on Cora
no code implementations • NeurIPS 2016 • Davide Boscaini, Jonathan Masci, Emanuele Rodolà, Michael M. Bronstein
Establishing correspondence between shapes is a fundamental problem in geometry processing, arising in a wide variety of applications.
no code implementations • 26 Jan 2015 • Jonathan Masci, Davide Boscaini, Michael M. Bronstein, Pierre Vandergheynst
Feature descriptors play a crucial role in a wide range of geometry analysis and processing applications, including shape correspondence, retrieval, and segmentation.
no code implementations • 7 Jun 2014 • Davide Boscaini, Davide Eynard, Michael M. Bronstein
Shape-from-X is an important class of problems in the fields of geometry processing, computer graphics, and vision, attempting to recover the structure of a shape from some observations.