Search Results for author: Matteo Matteucci

Found 36 papers, 11 papers with code

On the utility and protection of optimization with differential privacy and classic regularization techniques

no code implementations7 Sep 2022 Eugenio Lomurno, Matteo Matteucci

According to the literature, this approach has proven to be a successful defence against several models' privacy attacks, but its downside is a substantial degradation of the models' performance.

L2 Regularization Privacy Preserving

Object Structural Points Representation for Graph-based Semantic Monocular Localization and Mapping

1 code implementation21 Jun 2022 Davide Tateo, Davide Antonio Cucci, Matteo Matteucci, Andrea Bonarini

In this paper, we propose the use of an efficient representation, based on structural points, for the geometry of objects to be used as landmarks in a monocular semantic SLAM system based on the pose-graph formulation.

Semantic SLAM

SGDE: Secure Generative Data Exchange for Cross-Silo Federated Learning

no code implementations24 Sep 2021 Eugenio Lomurno, Alberto Archetti, Lorenzo Cazzella, Stefano Samele, Leonardo Di Perna, Matteo Matteucci

In this context, federated learning is one of the most influential frameworks for privacy-preserving distributed machine learning, achieving astounding results in many natural language processing and computer vision tasks.

BIG-bench Machine Learning Fairness +2

Deep Learning of Transferable MIMO Channel Modes for 6G V2X Communications

no code implementations31 Aug 2021 Lorenzo Cazzella, Dario Tagliaferri, Marouan Mizmizi, Damiano Badini, Christian Mazzucco, Matteo Matteucci, Umberto Spagnolini

Algebraic Low-rank (LR) channel estimation exploits space-time channel sparsity through the computation of position-dependent MIMO channel eigenmodes leveraging recurrent training vehicle passages in the coverage cell.

Transfer Learning

Improving Multi-View Stereo via Super-Resolution

no code implementations28 Jul 2021 Eugenio Lomurno, Andrea Romanoni, Matteo Matteucci

Today, Multi-View Stereo techniques are able to reconstruct robust and detailed 3D models, especially when starting from high-resolution images.

Super-Resolution

Neural Weighted A*: Learning Graph Costs and Heuristics with Differentiable Anytime A*

2 code implementations4 May 2021 Alberto Archetti, Marco Cannici, Matteo Matteucci

Recently, the trend of incorporating differentiable algorithms into deep learning architectures arose in machine learning research, as the fusion of neural layers and algorithmic layers has been beneficial for handling combinatorial data, such as shortest paths on graphs.

Probabilistic electric load forecasting through Bayesian Mixture Density Networks

no code implementations23 Dec 2020 Alessandro Brusaferri, Matteo Matteucci, Stefano Spinelli, Andrea Vitali

Probabilistic load forecasting (PLF) is a key component in the extended tool-chain required for efficient management of smart energy grids.

Load Forecasting Management +1

Spatial Temporal Transformer Network for Skeleton-based Action Recognition

1 code implementation11 Dec 2020 Chiara Plizzari, Marco Cannici, Matteo Matteucci

Skeleton-based human action recognition has achieved a great interest in recent years, as skeleton data has been demonstrated to be robust to illumination changes, body scales, dynamic camera views, and complex background.

Action Recognition Skeleton Based Action Recognition

Facetwise Mesh Refinement for Multi-View Stereo

no code implementations1 Dec 2020 Andrea Romanoni, Matteo Matteucci

The refinement step is applied for each facet using only the camera pair selected.

3D Reconstruction

Identification of Probability weighted ARX models with arbitrary domains

no code implementations29 Sep 2020 Alessandro Brusaferri, Matteo Matteucci, Stefano Spinelli

Hybrid system identification is a key tool to achieve reliable models of Cyber-Physical Systems from data.

Estimation of Switched Markov Polynomial NARX models

no code implementations29 Sep 2020 Alessandro Brusaferri, Matteo Matteucci, Stefano Spinelli

This work targets the identification of a class of models for hybrid dynamical systems characterized by nonlinear autoregressive exogenous (NARX) components, with finite-dimensional polynomial expansions, and by a Markovian switching mechanism.

Skeleton-based Action Recognition via Spatial and Temporal Transformer Networks

1 code implementation17 Aug 2020 Chiara Plizzari, Marco Cannici, Matteo Matteucci

Skeleton-based Human Activity Recognition has achieved great interest in recent years as skeleton data has demonstrated being robust to illumination changes, body scales, dynamic camera views, and complex background.

Action Recognition Action Recognition In Videos +2

A Differentiable Recurrent Surface for Asynchronous Event-Based Data

1 code implementation ECCV 2020 Marco Cannici, Marco Ciccone, Andrea Romanoni, Matteo Matteucci

Dynamic Vision Sensors (DVSs) asynchronously stream events in correspondence of pixels subject to brightness changes.

Optical Flow Estimation

Towards Affordance Prediction with Vision via Task Oriented Grasp Quality Metrics

no code implementations10 Jul 2019 Luca Cavalli, Gianpaolo Di Pietro, Matteo Matteucci

Indeed, we assess the validity of our novel framework both in the context of perfect information, i. e., known object model, and in the partial information context, i. e., inferring task oriented metrics from vision, underlining advantages and limitations of both situations.

Mesh-based Camera Pairs Selection and Occlusion-Aware Masking for Mesh Refinement

no code implementations21 May 2019 Andrea Romanoni, Matteo Matteucci

Many Multi-View-Stereo algorithms extract a 3D mesh model of a scene, after fusing depth maps into a volumetric representation of the space.

TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo

no code implementations ICCV 2019 Andrea Romanoni, Matteo Matteucci

One of the most successful approaches in Multi-View Stereo estimates a depth map and a normal map for each view via PatchMatch-based optimization and fuses them into a consistent 3D points cloud.

A Data-driven Prior on Facet Orientation for Semantic Mesh Labeling

no code implementations26 Jul 2018 Andrea Romanoni, Matteo Matteucci

Mesh labeling is the key problem of classifying the facets of a 3D mesh with a label among a set of possible ones.

Attention Mechanisms for Object Recognition with Event-Based Cameras

no code implementations25 Jul 2018 Marco Cannici, Marco Ciccone, Andrea Romanoni, Matteo Matteucci

Event-based cameras are neuromorphic sensors capable of efficiently encoding visual information in the form of sparse sequences of events.

Event-based vision Object Recognition +1

ReConvNet: Video Object Segmentation with Spatio-Temporal Features Modulation

no code implementations14 Jun 2018 Francesco Lattari, Marco Ciccone, Matteo Matteucci, Jonathan Masci, Francesco Visin

We introduce ReConvNet, a recurrent convolutional architecture for semi-supervised video object segmentation that is able to fast adapt its features to focus on any specific object of interest at inference time.

Semantic Segmentation Semi-Supervised Video Object Segmentation +1

Asynchronous Convolutional Networks for Object Detection in Neuromorphic Cameras

no code implementations21 May 2018 Marco Cannici, Marco Ciccone, Andrea Romanoni, Matteo Matteucci

Event-based cameras, also known as neuromorphic cameras, are bioinspired sensors able to perceive changes in the scene at high frequency with low power consumption.

object-detection Object Detection

Predicting the Next Best View for 3D Mesh Refinement

1 code implementation16 May 2018 Luca Morreale, Andrea Romanoni, Matteo Matteucci

Finding the best poses to capture part of the scene is one of the most challenging topic that goes under the name of Next Best View.

3D Reconstruction Robot Navigation

Multi-View Stereo 3D Edge Reconstruction

1 code implementation17 Jan 2018 Andrea Bignoli, Andrea Romanoni, Matteo Matteucci

This paper presents a novel method for the reconstruction of 3D edges in multi-view stereo scenarios.

Mesh-based 3D Textured Urban Mapping

no code implementations18 Aug 2017 Andrea Romanoni, Daniele Fiorenti, Matteo Matteucci

In the era of autonomous driving, urban mapping represents a core step to let vehicles interact with the urban context.

Autonomous Driving Surface Reconstruction

Multi-View Stereo with Single-View Semantic Mesh Refinement

no code implementations16 Aug 2017 Andrea Romanoni, Marco Ciccone, Francesco Visin, Matteo Matteucci

In this paper we propose a novel method to refine both the geometry and the semantic labeling of a given mesh.

3D Reconstruction

Robust Moving Objects Detection in Lidar Data Exploiting Visual Cues

no code implementations29 Sep 2016 Gheorghii Postica, Andrea Romanoni, Matteo Matteucci

Detecting moving objects in dynamic scenes from sequences of lidar scans is an important task in object tracking, mapping, localization, and navigation.

Object Tracking

Sparse vs. Non-sparse: Which One Is Better for Practical Visual Tracking?

no code implementations30 Jul 2016 Yashar Deldjoo, Shengping Zhang, Bahman Zanj, Paolo Cremonesi, Matteo Matteucci

Recently, sparse representation based visual tracking methods have attracted increasing attention in the computer vision community.

Visual Tracking

Incremental Reconstruction of Urban Environments by Edge-Points Delaunay Triangulation

no code implementations21 Apr 2016 Andrea Romanoni, Matteo Matteucci

Urban reconstruction from a video captured by a surveying vehicle constitutes a core module of automated mapping.

Automatic 3D Reconstruction of Manifold Meshes via Delaunay Triangulation and Mesh Sweeping

no code implementations21 Apr 2016 Andrea Romanoni, Amaël Delaunoy, Marc Pollefeys, Matteo Matteucci

In this paper we propose a new approach to incrementally initialize a manifold surface for automatic 3D reconstruction from images.

3D Reconstruction

Efficient moving point handling for incremental 3D manifold reconstruction

no code implementations20 Jul 2015 Andrea Romanoni, Matteo Matteucci

From the 3D Delaunay triangulation of these points, state-of-the-art algorithms build a manifold rough model of the scene.

3D Reconstruction Management

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