Italian Chapter Conference 2025 - Smart Tools and Apps in Graphics

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Algorithms in Geometric Deep learning and 3D AI: Theoretical Survey
(The Eurographics Association, 2025) Katturu, Vaibhav; Thind, Parampuneet Kaur; Comino Trinidad, Marc; Mancinelli, Claudio; Maggioli, Filippo; Romanengo, Chiara; Cabiddu, Daniela; Giorgi, Daniela
The study of shapes and geometric representations has long been central to Artificial Intelligence (AI). Early neural networks were limited to Euclidean domains such as images and sequences. The first extensions to non-Euclidean structures appeared in the 1990s and 2000s with recursive neural networks for hierarchical data and random walk-based graph methods. A major step forward came with spectral graph convolutional networks, which introduced convolution in the Fourier domain but faced scalability issues. Spatial methods later enabled more practical graph neural networks (GNNs). In parallel, 3D vision advanced with point cloud models such as PointNet and DGCNN, and mesh-based approaches like Geodesic CNN and MeshCNN, driving progress in classification, segmentation, and reconstruction. As algorithms in geometric deep learning and 3D AI expand, the field has grown both powerful and complex. This paper categorizes major algorithmic families, surveys key datasets across Euclidean and non-Euclidean domains, and highlights emerging advances and open research challenges.
ASDGen: A Shape Dataset Generator using a Simulated CAD Process
(The Eurographics Association, 2025) Komar, Alexander; Rakuschek, Julian; Meszlender, David; Lackner, Sebastian; Barzegar Khalilsaraei, Saeedeh; Augsdörfer, Ursula; Comino Trinidad, Marc; Mancinelli, Claudio; Maggioli, Filippo; Romanengo, Chiara; Cabiddu, Daniela; Giorgi, Daniela
Neural networks have shown great promise in 3D applications like shape analysis, object recognition, and design optimization. Machine learning methods depend on high-quality, structured datasets. While databases exist for general 3D shapes, there is a lack of databases tailored for subdivision surface representations. To address this, we introduce ASDGen, an algorithm to generate quadrilateral meshes through a sequence of CAD operations arbitrarily applied to an initial user-defined seed mesh. The resulting meshes are guaranteed to be manifold and can serve as control meshes for generating Catmull-Clark subdivision surfaces. The algorithm may be employed to generate large sets of synthetic shape data represented as quadrilateral meshes of varying degree of refinement, along with all CAD operations applied to a seed mesh to create the shape. The resulting data is ideal to be employed for data-driven analysis of subdivision surfaces. In addition to the shape-data generator, we provide a robust pipeline for extracting various differential shape properties as metadata, e.g. curvature and complexity measures, and for converting these meshes into signed distance fields. We generate a sample dataset of Catmull-Clark subdivision shapes which we make publicly available together with the generator. To demonstrate the potential of ASDGen, present two learning-based applications: a neural network model trained to predict mesh complexity and a prediction of maximum curvature points from the signed distance field of the shape. Our work lays the groundwork for a new class of learning problems rooted in CAD-inspired geometry, and provides both the tools and data necessary to support further research in this domain.
BASS-MLIC: a Novel Synthetic Dataset for Single-View Inverse Rendering Tasks on Cultural Heritage Artifacts
(The Eurographics Association, 2025) Righetto, Leonardo; Ullah, Shakir; Giachetti, Andrea; Comino Trinidad, Marc; Mancinelli, Claudio; Maggioli, Filippo; Romanengo, Chiara; Cabiddu, Daniela; Giorgi, Daniela
BASS-MLIC is a synthetic dataset created with Blender to support multi-light image processing and inverse rendering tasks. It features orthographic views of culturally significant surfaces rendered with realistic materials and includes rich ground truth annotations, such as normals, depth, shadows, materials, and BRDF parameters. These annotations enable evaluation across diverse tasks like relighting, Photometric Stereo, shadow-aware estimations, and BRDF fitting. Preliminary experiments highlight its practical utility.
Combining CNN Feature Extraction and Kolmogorov-Arnold Networks Regression for Procedural 3D Shape Generation
(The Eurographics Association, 2025) Manfredi, Gilda; Capece, Nicola; Erra, Ugo; Gruosso, Antonio; Comino Trinidad, Marc; Mancinelli, Claudio; Maggioli, Filippo; Romanengo, Chiara; Cabiddu, Daniela; Giorgi, Daniela
Generating 3D objects with complex, nonlinear shapes directly from images is still an open research area. To address this problem, several state-of-the-art methods use Deep Learning (DL) to predict a set of parameters from images, which are then used to generate the 3D geometry, leveraging the characteristics of procedural modeling. Recently, Kolmogorov-Arnold Networks (KANs) have emerged as an alternative to traditional Multilayer Perceptrons (MLPs) in DL, and have been successfully integrated into architectures such as Convolutional Neural Networks (CNNs), Graph Neural Networks, and Transformers. In this work, we propose a DL architecture consisting of a hybrid CNN-KAN network for parametric 3D model generation from images. The model combines the ability of KANs to capture complex nonlinear functions with the strong visual feature extraction capabilities of CNNs. The method is evaluated using both quantitative error metrics and qualitative visualizations comparing predicted shapes with ground truth, and its performance is compared against a more standard CNN-MLP architecture.
Degenerancy-Resilient LIDAR Odometry via Reflectance-Dervied Correspondences
(The Eurographics Association, 2025) Marmaglio, Simone; Nguyen Hoang, Nam; Savardi, Mattia; Sgrenzaroli, Matteo; Vassena, Giorgio; Signoroni, Alberto; Comino Trinidad, Marc; Mancinelli, Claudio; Maggioli, Filippo; Romanengo, Chiara; Cabiddu, Daniela; Giorgi, Daniela
The key challenge in LiDAR odometry is estimating motion in geometrically degenerate environments, where standard geometrybased feature alignment often fails. LiDAR reflectance offers complementary information: it can be rendered as an image, letting the LiDAR to act like an active camera sensor and to add constraints where geometry is weak. We use these images to detect repeatable keypoints, match them across sweeps, and lift their locations to 3D, creating sparse and reflectance-informed correspondences. Our method follows a standard LiDAR-Inertial Odometry (LIO) pipeline. An Error-State Kalman Filter (ESKF) provides high-rate motion estimates for scan deskewing and for initializing ICP. We fuse reflectance-derived constraints into scan-to-map registration with a joint objective that combines a sparse point-to-point term with point-to-plane residuals, stabilizing motion directions that are otherwise weakly observable. We also select both reflectance and geometric correspondences to specifically constrain these weak directions. Experiments in geometrically degenerate and GNSS-denied settings, and even in presence of highly spatially anisotropic LiDAR acquisitions, show that adding reflectance-derived correspondences reduces drift and guides convergence toward the true pose.
Game mechanics and interaction for mixed reality and gamification
(The Eurographics Association, 2025) Beni, Andrea De; Castellani, Umberto; Comino Trinidad, Marc; Mancinelli, Claudio; Maggioli, Filippo; Romanengo, Chiara; Cabiddu, Daniela; Giorgi, Daniela
Realistic and effective interaction plays a very important role for extended reality scenarios, especially when a full integration between the real and synthetic world is required. Augmented reality (AR) frameworks still provide unsatisfactory performance due to the unreliability of the real-time AR systems and the lack of a systematic study on the user experience in the mixed reality environment. Gaming is a strong potential technique to make AR effective to combine immersion, engagement, and (social) interaction. However, the most of current AR games involve standard interaction mechanics that have been designed for generic games without considering the peculiarity of the augmented reality paradigm. In this work we address how to fill this gap between game and mixed reality. Our contribution is twofold. First, we introduce a practical and reliable mixed reality framework that adopt a model-based and marker-less approach to compute the camera matching procedure. Second, we propose and evaluate several game mechanics properly designed to work on the mixed reality scenarios. We employed our methods for cultural heritage application showing the effectiveness of the proposed mixed reality mechanics in improving the knowledge of archaeological findings in a playful way (i.e., gamification).
Generalizing Shape-from-Template to Topological Changes
(The Eurographics Association, 2025) Manogue, Kevin; Schang, Tomasz M.; Kuş, Dilara; Müller, Jonas; Zachow, Stefan; Sengupta, Agniva; Comino Trinidad, Marc; Mancinelli, Claudio; Maggioli, Filippo; Romanengo, Chiara; Cabiddu, Daniela; Giorgi, Daniela
Reconstructing the surfaces of deformable objects from correspondences between a 3D template and a 2D image is well studied under Shape-from-Template (SfT) methods; however, existing approaches break down when topological changes accompany the deformation. We propose a principled extension of SfT that enables reconstruction in the presence of such changes. Our approach is initialized with a classical SfT solution and iteratively adapts the template by partitioning its spatial domain so as to minimize an energy functional that jointly encodes physical plausibility and reprojection consistency. We demonstrate that the method robustly captures a wide range of practically relevant topological events including tears and cuts on bounded 2D surfaces, thereby establishing the first general framework for topological-change-aware SfT. Experiments on both synthetic and real data confirm that our approach consistently outperforms baseline methods.
Geometric aware local optimization for robust primitive fitting
(The Eurographics Association, 2025) Ferraris, Andrea; Leveni, Filippo; Baieri, Daniele; Maggioli, Filippo; Melzi, Simone; Magri, Luca; Comino Trinidad, Marc; Mancinelli, Claudio; Maggioli, Filippo; Romanengo, Chiara; Cabiddu, Daniela; Giorgi, Daniela
The decomposition of 3D point clouds into meaningful geometric primitives is a longstanding challenge in Computer Vision and Computer Graphics. While recent advances in data-driven methods and neural representations have achieved significant progress in 3D reconstruction and abstraction, traditional primitive-based representations remain invaluable for tasks requiring interpretability, compactness, and robustness. This work introduces a novel framework for primitive decomposition in 2D and 3D point clouds, designed to cope with noise, outliers, and overlapping structures. Building upon traditional RANSACbased approaches, the proposed method integrates geometric priors to enhance its effectiveness in identifying interpretable and meaningful geometric primitives within complex data. Central to our approach is a novel geometric-aware inlier refinement step, which incorporates geometric constraints such as surface completeness and normal consistency. This refinement step is formulated as an optimization problem solved through the GRAPH-CUT algorithm. This optimization process penalizes excessive surface extensions and promotes coherence in normal orientations, ensuring that the refined inlier sets closely match the geometric structures the point cloud represents. Experiments on synthetic and real-world datasets validate the robustness and accuracy of the proposed method, demonstrating its ability to outperform state-of-the-art techniques in terms of both result quality and computational efficiency.
GUIDÆTA - A Versatile Interactions Dataset with extensive Context Information and Metadata
(The Eurographics Association, 2025) Lengauer, Stefan; Götz, Sarah Annabelle von; Hoesch, Marie-Therese; Steinwidder, Florian; Tytarenko, Mariia; Bedek, Michael A.; Schreck, Tobias; Comino Trinidad, Marc; Mancinelli, Claudio; Maggioli, Filippo; Romanengo, Chiara; Cabiddu, Daniela; Giorgi, Daniela
Interaction data is widely used in multiple domains such as cognitive science, visualization, human computer interaction, and cybersecurity, among others. Applications range from cognitive analyses over user/behavior modeling, adaptation, recommendations, to (user/bot) identification/verification. That is, research on these applications - in particular those relying on learned models - require copious amounts of structured data for both training and evaluation. Different application domains thereby impose different requirements. I.e., for some purposes it is vital that the data is based on a guided interaction process, meaning that monitored subjects pursued a given task, while other purposes require additional context information, such as widget interactions or metadata. Unfortunately, the amount of publicly available datasets is small and their respective applicability for specific purposes limited. We present GUIDEd Interaction DATA (GUIDÆTA) - a new dataset, collected from a large-scale guided user study with more than 250 users, each working on three pre-defined information retrieval tasks using a custom-built consumer information system. Besides being larger than most comparable datasets - with 716 completed tasks, 2.39 million mouse and keyboard events (2.35 million and 40 thousand, respectively) and a total observation period of almost 50 hours - its interactions exhibit encompassing context information in the form of widget information, triggered (system) events and associated displayed content. Combined with extensive metadata such as sociodemographic user data and answers to explicit feedback questionnaires (regarding perceived usability, experienced cognitive load, pre-knowledge on the information system's topic), GUIDÆTA constitutes a versatile dataset, applicable for various research domains and purposes. Alongside the data itself, we publish the software tools we use for handling and analyzing the dataset.
Implicit Field-Based Stylization of 2D and 3D Liquid Animations
(The Eurographics Association, 2025) Stevenson-Regla, Rodrigo; Rohmer, Damien; Barthe, Loïc; Cani, Marie-Paule; Comino Trinidad, Marc; Mancinelli, Claudio; Maggioli, Filippo; Romanengo, Chiara; Cabiddu, Daniela; Giorgi, Daniela
Combining physically-based simulation with stylized visual effects not only requires to change the aspect of surfaces, but their shapes as well. We describe a new expressive rendering method for liquid animations, which can be used on top of any preexisting particle-based simulation. Our solution builds on visual particles that carry both water and air distributions, both evolving through particle history based on kinematics information from the simulation. These density fields are combined at each frame to create the implicit iso-surface of interest, rendered in adapted style. By defining series of visual particle states, we parametrize this model to capture the typical stylized geometry of water bodies used to highlight dynamic motion in paintings and cartoons, such as elongating droplets, concavities carved at the crest of breaking waves, and stylized air-water mixtures such as bubbles and foam. Regardless of the 2D or 3D nature of the input simulation, our solution maintains temporal coherence and ensures that water bodies keep an approximately constant surface in 2D, resp. or volume in 3D, over time. Finally, we conducted a user study to show the effectiveness of our method against state of the art AI-based tools, in a variety of animation scenarios where stylized shapes are needed.
Integrating Multi-Modal Solutions for Personalized and Accessible VR Museum Experiences
(The Eurographics Association, 2025) Bonino, Brigida; Giannini, Franca; Comino Trinidad, Marc; Mancinelli, Claudio; Maggioli, Filippo; Romanengo, Chiara; Cabiddu, Daniela; Giorgi, Daniela
Virtual Reality offers the opportunity to provide immersive and engaging experiences that can overcome physical, cultural, and economic barriers. However, defining and implementing principles and methodologies for accessible and inclusive virtual experiences remains a challenge, particularly because such systems must adapt to users with diverse needs related to age, culture, temporary physical conditions, or motor and sensory impairments. This work presents a virtual museum application that integrates multiple functionalities and provides options to address different physical and sensory issues, aiming to adapt the experience as much as possible to each single user. The system is designed to operate in a highly automatic way, minimizing user effort and stress while ensuring a comfortable and engaging experience.
Learning to Predict Aboveground Biomass from RGB Images with 3D Synthetic Scenes
(The Eurographics Association, 2025) Zuffi, Silvia; Comino Trinidad, Marc; Mancinelli, Claudio; Maggioli, Filippo; Romanengo, Chiara; Cabiddu, Daniela; Giorgi, Daniela
Forests play a critical role in global ecosystems by supporting biodiversity and mitigating climate change via carbon sequestration. Accurate aboveground biomass (AGB) estimation is essential for assessing carbon storage and wildfire fuel loads, yet traditional methods rely on labor-intensive field measurements or remote sensing approaches with significant limitations in dense vegetation. In this work, we propose a novel learning-based method for estimating AGB from a single ground-based RGB image. We frame this as a dense prediction task, introducing AGB density maps, where each pixel represents tree biomass normalized by the plot area and each tree's image area. We leverage the recently introduced synthetic 3D SPREAD dataset, which provides realistic forest scenes with per-image tree attributes (height, trunk and canopy diameter) and instance segmentation masks. Using these assets, we compute AGB via allometric equations and train a model to predict AGB density maps, integrating them to recover the AGB estimate for the captured scene. Our approach achieves a median AGB estimation error of 1:22kg=m2 on held-out SPREAD data and 1:94kg=m2 on a real-image dataset. To our knowledge, this is the first method to estimate aboveground biomass directly from a single RGB image, opening up the possibility for a scalable, interpretable, and cost-effective solution for forest monitoring, while also enabling broader participation through citizen science initiatives.
Learning to Wait: Preventing Global Congestion from Local Observations in Real-Time Crowd Navigation
(The Eurographics Association, 2025) Ruprecht, Irena; Michelic, Florian; Preiner, Reinhold; Comino Trinidad, Marc; Mancinelli, Claudio; Maggioli, Filippo; Romanengo, Chiara; Cabiddu, Daniela; Giorgi, Daniela
We present a real-time crowd simulation approach based on reinforcement learning (RL), addressing congestion prevention in confined spaces. We learn a local navigation policy that uses compact, fast-to-compute per-agent observations of a small set of neighbors, including their desired directions. Alongside goal progress and inter-agent spacing, we reward agents for waiting when neighbors ahead pursue similar goals. This formulation fosters global self-organization from purely local interactions. Preliminary results show reduced congestion and consistent goal attainment for large crowds with hundreds of agents.
LiD2LOD: Generating LOD1 Urban Models from Airborne LiDAR
(The Eurographics Association, 2025) Sorgente, Tommaso; Moscoso Thompson, Elia; Romanengo, Chiara; Comino Trinidad, Marc; Mancinelli, Claudio; Maggioli, Filippo; Romanengo, Chiara; Cabiddu, Daniela; Giorgi, Daniela
The increasing availability of large-scale airborne LiDAR (Light Detection And Ranging) data related to urban scenarios requires the development of methods for transforming raw point clouds into structured 3D urban representations. The ability to generate accurate representations starting from raw data meets fundamental requirements in the fields of urban visualization, interactive simulation, and digital twins, providing a solid foundation for graphics and immersive reality applications. In this work, we present LiD2LOD, a framework for the automatic generation of Level of Detail 1 (LOD1) city models from LiDAR point clouds. Our tool can create both semantic models according to the CityGML standard, suitable for geospatial data integration, and lightweight triangular meshes, optimized for visualization and rendering. We test our approach on point clouds that represent historical cities characterized by complex morphology, thereby proving its scalability and robustness.
A lightweight open-source tool for Meshing within Geosciences
(The Eurographics Association, 2025) Miola, Marianna; Cabiddu, Daniela; Pittaluga, Simone; Raviola, Micaela; Zuccolini, Marino Vetuschi; Comino Trinidad, Marc; Mancinelli, Claudio; Maggioli, Filippo; Romanengo, Chiara; Cabiddu, Daniela; Giorgi, Daniela
Understanding and modeling the complex geometries of the natural world is a key challenge in the Geosciences. Representing terrains, subsurfaces, catchments, and environmental domains requires not only accurate data acquisition but also robust and flexible geometric modeling tools. While computer graphics and geometry processing have made significant advances in representing and manipulating complex 2D and 3D shapes, their uptake in environmental modeling remains limited due to the lack of geoscientific constraints and interoperability with geospatial standards. We present MUSE-geometry, a lightweight computational geometry tool designed to bridge this gap. Our tool integrates geometric primitives, structured and unstructured meshing, and editing operations with explicit support for geospatial data formats and coordinate reference systems. By combining computational geometry with geospatial awareness, the tool enables the creation of simulation-ready 2D/3D models that preserve input integrity and topological consistency. Implemented as a selfcontained command-line application in C++, our tool has been tested on real-world scenarios, including modeling topographic surfaces or coastal water volumes, demonstrating its ability to unify data structures and geometric processing within a single pipeline. The tool offers flexible and interoperable operations that enhance the integration of advanced geometric processing into geoscientific workflows in compliance with geospatial standards.
Metrics and Tools for Geometry-Based Analysis of Urban Scenes from a Human Perception Perspective
(The Eurographics Association, 2025) Cutruzzulà, Simona; Mortara, Michela; Spagnuolo, Michela; Comino Trinidad, Marc; Mancinelli, Claudio; Maggioli, Filippo; Romanengo, Chiara; Cabiddu, Daniela; Giorgi, Daniela
The morphology of a city, the distribution of services and attractions, the presence of vegetation, or the openness of public spaces are all elements that shape how citizens perceive the urban environment they live in. Understanding which features most strongly influence the impression people have of a place is an essential step toward designing effective strategies for urban improvement. In this work, we propose useful tools to analyse, and possibly quantify, the impact that environmental characteristics, such as surrounding colours, amount of light and spatial distribution of urban elements, can have on human perception. Specifically, we consider the geometry of the human field of view to delimit the visible portion of an urban area from a given position, and we introduce new metrics and algorithms for the analysis of the scene. Furthermore, we model the way visual information is captured by our eyes and processed by our brain, relying on basic principles of optics and common techniques in colour engineering. The project is still ongoing, but early results highlight its potential to obtain deeper insights into the citizens' perception of urban environments.
NeuroShape: exploiting neural architectures for shape analysis of ultrastructural 3D neuroscience morphologies
(The Eurographics Association, 2025) Shaffique, Humaira; Shah, Uzair; Alzubaidi, Mahmood; Schneider, Jens; Magistretti, Pierre Julius; Cali, Corrado; Househ, Mowafa; Agus, Marco; Comino Trinidad, Marc; Mancinelli, Claudio; Maggioli, Filippo; Romanengo, Chiara; Cabiddu, Daniela; Giorgi, Daniela
Recent advances in volume electron microscopy (EM) enable nanometric-scale 3D reconstructions of neural tissue, providing unprecedented opportunities for studying cellular and subcellular morphology in neuroscience. The geometry of structures such as nuclei, neurites, and organelles can encode phenotypic information relevant to both functional specialization and pathological conditions, and thus represents a valuable complement to connectivity-based approaches in connectomics. While previous studies relied on handcrafted descriptors and classical machine learning for morphology analysis, recent progress in deep learning for 3D shape understanding offers new opportunities to learn robust, task-specific representations directly from geometric data. In this work we present NeuroShape, a first exploration of modern deep learning methods for shape analysis of ultrastructural 3D neuroscience morphologies. We introduce two annotated datasets derived from EM reconstructions: one of nuclei envelopes, and one of neurites and neural organelles. We benchmark two state-of-the-art neural architectures for 3D geometry (DiffusionNet [SACO22] and Laplacian2Mesh [DWL∗24]) and compare them against traditional feature-based descriptors previously used in neural morphology analysis. Our preliminary results highlight both the feasibility and the challenges of applying deep learning shape analysis techniques in this domain, and we release the datasets as a reference resource for future studies.
Organising and Enriching Urban 3D Models for Digital Twin Applications
(The Eurographics Association, 2025) Pittaluga, Simone; Cabiddu, Daniela; Mortara, Michela; Spagnuolo, Michela; Comino Trinidad, Marc; Mancinelli, Claudio; Maggioli, Filippo; Romanengo, Chiara; Cabiddu, Daniela; Giorgi, Daniela
The creation of an urban digital twin for the city of Matera, including 3D geometric models of the city landscape and heterogeneous information coming from different data sources, highlighted the need for a graphical user interface capable of integrating complex 3D urban scenes with geo-referenced data and knowledge (e.g., from real-time sensors, administrative information systems, algorithmic analysis and simulation, manual annotation) and making them easily accessible to a range of users with different levels of expertise. From these requirements, a new tool Matera3D has been developed as a specialized software platform for visualizing, analyzing and documenting 3D urban models. The application integrates point cloud and triangle mesh management with interactive semantic annotation, CityGML compliant dictionaries, and basic morphological analyzes, such as shadow computation and street slope measurements, as an initial set of processing tools that will be extended in the future. The software organizes all project data, including geometry, annotations, metadata, and scalar fields embedded in the geometry, within a coherent folder structure to ensure interoperability and efficient sharing. The software can show data from different sources and of different types in an integrated 3D view to facilitate research and practical applications in urban planning, environmental assessment, and infrastructure management beyond the popular 2D (GIS) approach.
ReCoGS: Real-time ReColoring for Gaussian Splatting scenes
(The Eurographics Association, 2025) Rutayisire, Lorenzo; Capodieci, Nicola; Pellacini, Fabio; Comino Trinidad, Marc; Mancinelli, Claudio; Maggioli, Filippo; Romanengo, Chiara; Cabiddu, Daniela; Giorgi, Daniela
Gaussian Splatting has emerged as a leading method for novel view synthesis, offering superior training efficiency and realtime inference compared to NeRF approaches, while still delivering high-quality reconstructions. Beyond view synthesis, this 3D representation has also been explored for editing tasks. Many existing methods leverage 2D diffusion models to generate multi-view datasets for training, but they often suffer from limitations such as view inconsistencies, lack of fine-grained control, and high computational demand. In this work, we focus specifically on the editing task of recoloring. We introduce a userfriendly pipeline that enables precise selection and recoloring of regions within a pre-trained Gaussian Splatting scene. To demonstrate the real-time performance of our method, we also present an interactive tool that allows users to experiment with the pipeline in practice. Code is available at https://github.com/loryruta/recogs.
Smart Tools and Applications in Graphics - Eurographics Italian Chapter Conference: Frontmatter
(The Eurographics Association, 2025) Comino Trinidad, Marc; Mancinelli, Claudio; Maggioli, Filippo; Romanengo, Chiara; Cabiddu, Daniela; Giorgi, Daniela; Comino Trinidad, Marc; Mancinelli, Claudio; Maggioli, Filippo; Romanengo, Chiara; Cabiddu, Daniela; Giorgi, Daniela

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