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Posts

Future Blog Post

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This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

Blog Post number 4

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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 3

less than 1 minute read

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Blog Post number 2

less than 1 minute read

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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 1

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

portfolio

Mega RoBoxe

2D multiplayer fighting game for a Global Game Jam.

OpenGL Fractals

Published:

An OpenGL implementation for common 2D fractals.

publications

SBML2Modelica: integrating biochemical models within open-standard simulation ecosystems

Published in Bioinformatics, 2020

Abstract

SBML is the most widespread language for the definition of biochemical models. Although dozens of SBML simulators are available, there is a general lack of support to the integration of SBML models within open-standard general-purpose simulation ecosystems. This hinders co-simulation and integration of SBML models within larger model networks, in order to, e.g. enable in silico clinical trials of drugs, pharmacological protocols, or engineering artefacts such as biomedical devices against Virtual Physiological Human models. Modelica is one of the most popular existing open-standard general-purpose simulation languages, supported by many simulators. Modelica models are especially suited for the definition of complex networks of heterogeneous models from virtually all application domains. Models written in Modelica (and in 100+ other languages) can be readily exported into black-box Functional Mock-Up Units (FMUs), and seamlessly co-simulated and integrated into larger model networks within open-standard language-independent simulation ecosystems. In order to enable SBML model integration within heterogeneous model networks, we present SBML2Modelica, a software system translating SBML models into well-structured, user-intelligible, easily modifiable Modelica models. SBML2Modelica is SBML Level 3 Version 2—compliant and succeeds on 96.47% of the SBML Test Suite Core (with a few rare, intricate and easily avoidable combinations of constructs unsupported and cleanly signalled to the user). Our experimental campaign on 613 models from the BioModels database (with up to 5438 variables) shows that the major open-source (general-purpose) Modelica and FMU simulators achieve performance comparable to state-of-the-art specialized SBML simulators.

Recommended citation: Maggioli Filippo, et al. "SBML2Modelica: integrating biochemical models within open-standard simulation ecosystems." Bioinformatics 36.7 (2020): 2165-2172.
Download Paper | Download Bibtex

Orthogonalized Fourier Polynomials for Signal Approximation and Transfer

Published in Computer Graphics Forum - EUROGRAPHICS, 2021

Abstract

We propose a novel approach for the approximation and transfer of signals across 3D shapes. The proposed solution is based on taking pointwise polynomials of the Fourier-like Laplacian eigenbasis, which provides a compact and expressive representation for general signals defined on the surface. Key to our approach is the construction of a new orthonormal basis upon the set of these linearly dependent polynomials. We analyze the properties of this representation, and further provide a complete analysis of the involved parameters. Our technique results in accurate approximation and transfer of various families of signals between near-isometric and non-isometric shapes, even under poor initialization. Our experiments, showcased on a selection of downstream tasks such as filtering and detail transfer, show that our method is more robust to discretization artifacts, deformation and noise as compared to alternative approaches.

Recommended citation: Maggioli Filippo, et al. "Orthogonalized fourier polynomials for signal approximation and transfer." Computer Graphics Forum. Vol. 40. No. 2. 2021.
Download Paper | Download Bibtex

Efficiently Parallelizable Strassen-Based Multiplication of a Matrix by its Transpose

Published in International Conference on Parallel Processing, 2021

Abstract

The multiplication of a matrix by its transpose, \(A^TA\), appears as an intermediate operation in the solution of a wide set of problems. In this paper, we propose a new cache-oblivious algorithm (ATA) for computing this product, based upon the classical Strassen algorithm as a sub-routine. In particular, we decrease the computational cost to \(2/3\) the time required by Strassen’s algorithm, amounting to \(14/3n^{\mathrm{log}_2 7}\) floating point operations. ATA works for generic rectangular matrices, and exploits the peculiar symmetry of the resulting product matrix for saving memory. In addition, we provide an extensive implementation study of ATA in a shared memory system, and extend its applicability to a distributed environment. To support our findings, we compare our algorithm with state-of-the-art solutions specialized in the computation of \(A^TA\), as well as with solutions for the computation of the general \(A^TB\) product applied to this problem. Our experiments highlight good scalability with respect to both the matrix size and the number of involved processes, as well as favorable performance for both the parallel paradigms and the sequential implementation when compared with other methods in the literature.

Recommended citation: Arrigoni Viviana, et al. "Efficiently parallelizable strassen-based multiplication of a matrix by its transpose." Proceedings of the 50th International Conference on Parallel Processing. 2021.
Download Paper | Download Bibtex

Learning Spectral Unions of Partial Deformable 3D Shapes

Published in Computer Graphics Forum - EUROGRAPHICS, 2022

Abstract

Spectral geometric methods have brought revolutionary changes to the field of geometry processing. Of particular interest is the study of the Laplacian spectrum as a compact, isometry and permutation-invariant representation of a shape. Some recent works show how the intrinsic geometry of a full shape can be recovered from its spectrum, but there are approaches that consider the more challenging problem of recovering the geometry from the spectral information of partial shapes. In this paper, we propose a possible way to fill this gap. We introduce a learning-based method to estimate the Laplacian spectrum of the union of partial non-rigid 3D shapes, without actually computing the 3D geometry of the union or any correspondence between those partial shapes. We do so by operating purely in the spectral domain and by defining the union operation between short sequences of eigenvalues. We show that the approximated union spectrum can be used as-is to reconstruct the complete geometry [MRC*19], perform region localization on a template [RTO*19] and retrieve shapes from a database, generalizing ShapeDNA [RWP06] to work with partialities. Working with eigenvalues allows us to deal with unknown correspondence, different sampling, and different discretizations (point clouds and meshes alike), making this operation especially robust and general. Our approach is data-driven and can generalize to isometric and non-isometric deformations of the surface, as long as these stay within the same semantic class (e.g., human bodies or horses), as well as to partiality artifacts not seen at training time.

Recommended citation: Moschella Luca, et al. "Learning spectral unions of partial deformable 3D shapes." Computer Graphics Forum. Vol. 41. No. 2. 2022.
Download Paper | Download Bibtex

Newton’s Fractals on Surfaces via Bicomplex Algebra

Published in ACM SIGGRAPH Posters, 2022

Abstract

Real-time procedural texturing is becoming more and more important as the need of large variety of contents increases. We propose a new technique that exploits the properties of a 4-dimensional algebraic field for efficiently computing on GPU a procedural texture with fractal properties. The produced pattern are shown to be well suitable in many applications, especially when used as building blocks for complex materials.

Recommended citation: Maggioli Filippo, et al. "Newton’s fractals on surfaces via bicomplex algebra." ACM SIGGRAPH 2022 Posters. 2022. 1-2.
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Fluid Dynamics Network: Topology-Agnostic 4D Reconstruction via Fluid Dynamics Priors

Published in arXiv, 2023

Abstract

Representing 3D surfaces as level sets of continuous functions over \(\mathbb{R}^3\) is the common denominator of neural implicit representations, which recently enabled remarkable progress in geometric deep learning and computer vision tasks. In order to represent 3D motion within this framework, it is often assumed (either explicitly or implicitly) that the transformations which a surface may undergo are homeomorphic: this is not necessarily true, for instance, in the case of fluid dynamics. In order to represent more general classes of deformations, we propose to apply this theoretical framework as regularizers for the optimization of simple 4D implicit functions (such as signed distance fields). We show that our representation is capable of capturing both homeomorphic and topology-changing deformations, while also defining correspondences over the continuously-reconstructed surfaces.

Recommended citation: Baieri Daniele, et al. "Fluid dynamics network: topology-agnostic 4d reconstruction via fluid dynamics priors." arXiv preprint arXiv:2303.09871. 2023.
Download Paper | Download Bibtex

MoMaS: Mold Manifold Simulation for real-time procedural texturing

Published in Computer Graphics Forum - Pacific Graphics, 2023

Abstract

The slime mold algorithm has recently been under the spotlight thanks to its compelling properties studied across many disciplines like biology, computation theory, and artificial intelligence. However, existing implementations act only on planar surfaces, and no adaptation to arbitrary surfaces is available. Inspired by this gap, we propose a novel characterization of the mold algorithm to work on arbitrary curved surfaces. Our algorithm is easily parallelizable on GPUs and allows to model the evolution of millions of agents in real-time over surface meshes with several thousand triangles, while keeping the simplicity proper of the slime paradigm. We perform a comprehensive set of experiments, providing insights on stability, behavior, and sensibility to various design choices. We characterize a broad collection of behaviors with a limited set of controllable and interpretable parameters, enabling a novel family of heterogeneous and high-quality procedural textures. The appearance and complexity of these patterns are well-suited to diverse materials and scopes, and we add another layer of generalization by allowing different mold species to compete and interact in parallel.

Recommended citation: Maggioli Filippo, et al. "MoMaS: Mold Manifold Simulation for real‐time procedural texturing." Computer Graphics Forum. Vol. 41. No. 7. 2022.
Download Paper | Download Bibtex

A Physically-inspired Approach to the Simulation of Plant Wilting

Published in SIGGRAPH Asia, 2023

Abstract

Plants are among the most complex objects to be modeled in computer graphics. While a large body of work is concerned with structural modeling and the dynamic reaction to external forces, our work focuses on the dynamic deformation caused by plant internal wilting processes. To this end, we motivate the simulation of water transport inside the plant which is a key driver of the wilting process. We then map the change of water content in individual plant parts to branch stiffness values and obtain the wilted plant shape through a position based dynamics simulation. We show, that our approach can recreate measured wilting processes and does so with a higher fidelity than approaches ignoring the internal water flow. Realistic plant wilting is not only important in a computer graphics context but can also aid the development of machine learning algorithms in agricultural applications through the generation of synthetic training data.

Recommended citation: Maggioli Filippo, et al. "A physically-inspired approach to the simulation of plant wilting." SIGGRAPH Asia 2023 Conference Papers. 2023.
Download Paper | Download Bibtex

SShaDe: scalable shape deformation via local representations

Published in arXiv, 2024

Abstract

With the increase in computational power for the available hardware, the demand for high-resolution data in computer graphics applications increases. Consequently, classical geometry processing techniques based on linear algebra solutions are starting to become obsolete. In this setting, we propose a novel approach for tackling mesh deformation tasks on high-resolution meshes. By reducing the input size with a fast remeshing technique and preserving a consistent representation of the original mesh with local reference frames, we provide a solution that is both scalable and robust in multiple applications, such as as-rigid-as-possible deformations, non-rigid isometric transformations, and pose transfer tasks. We extensively test our technique and compare it against state-of-the-art methods, proving that our approach can handle meshes with hundreds of thousands of vertices in tens of seconds while still achieving results comparable with the other solutions.

Recommended citation: Maggioli Filippo, et al. "SShaDe: scalable shape deformation via local representations." arXiv preprint arXiv:2409.17961. 2024.
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Implicit-ARAP: Efficient Handle-Guided Deformation of High-Resolution Meshes and Neural Fields via Local Patch Meshing

Published in arXiv, 2024

Abstract

In this work, we present the local patch mesh representation for neural signed distance fields. This technique allows to discretize local regions of the level sets of an input SDF by projecting and deforming flat patch meshes onto the level set surface, using exclusively the SDF information and its gradient. Our analysis reveals this method to be more accurate than the standard marching cubes algorithm for approximating the implicit surface. Then, we apply this representation in the setting of handle-guided deformation: we introduce two distinct pipelines, which make use of 3D neural fields to compute As-Rigid-As-Possible deformations of both high-resolution meshes and neural fields under a given set of constraints. We run a comprehensive evaluation of our method and various baselines for neural field and mesh deformation which show both pipelines achieve impressive efficiency and notable improvements in terms of quality of results and robustness. With our novel pipeline, we introduce a scalable approach to solve a well-established geometry processing problem on high-resolution meshes, and pave the way for extending other geometric tasks to the domain of implicit surfaces via local patch meshing.

Recommended citation: Baieri Daniele, et al. "Implicit-ARAP: Efficient Handle-Guided Deformation of High-Resolution Meshes and Neural Fields via Local Patch Meshing." arXiv preprint arXiv:2405.12895. 2024.
Download Paper | Download Bibtex

Efficient Generation of Multimodal Fluid Simulation Data

Published in Smart Tools and Applications in Graphics, 2024

Abstract

In this work, we introduce an efficient generation procedure to produce synthetic multi-modal datasets of fluid simulations. The procedure can reproduce the dynamics of fluid flows and allows for exploring and learning various properties of their complex behavior, from distinct perspectives and modalities. We employ our framework to generate a set of thoughtfully designed training datasets, which attempt to span specific fluid simulation scenarios in a meaningful way. The properties of our contributions are demonstrated by evaluating recently published algorithms for the neural fluid simulation and fluid inverse rendering tasks using our benchmark datasets. Our contribution aims to fulfill the community’s need for standardized training data, fostering more reproducibile and robust research.

Recommended citation: Baieri Daniele, et al. "Efficient generation of multimodal fluid simulation data". Smart Tools and Applications in Graphics-Eurographics Italian Chapter Conference. 2024.
Download Paper | Download Bibtex

S4A: Scalable Spectral Statistical Shape Analysis

Published in Smart Tools and Applications in Graphics, 2024

Abstract

Statistical shape analysis is a crucial technique for studying deformations within collections of shapes, particularly in the field of Medical Imaging. However, the high density of meshes typically used to represent medical data poses a challenge for standard geometry processing tools due to their limited efficiency. While spectral approaches offer a promising solution by effectively handling high-frequency variations inherent in such data, their scalability is questioned by their need to solve eigendecompositions of large sparse matrices. In this paper, we introduce S4A, a novel and efficient method based on spectral geometry processing, that addresses these issues with a low computational cost. It operates in four stages: (i) establishing correspondences between each pair of shapes in the collection, (ii) defining a common latent space to encode deformations across the entire collection, (iii) computing statistical quantities to identify, highlight, and measure the most representative variations within the collection, and iv) performing information transfer from labeled data to large collections of shapes. Unlike previous methods, S4A provides a highly efficient solution across all stages of the process.We demonstrate the advantages of our approach by comparing its accuracy and computational efficiency to existing pipelines, and by showcasing the comprehensive statistical insights that can be derived from applying our method to a collection of medical data.

Recommended citation: Maccarone Francesca, et al. "S4a: Scalable spectral statistical shape analysis." Smart Tools and Applications in Graphics-Eurographics Italian Chapter Conference. 2024.
Download Paper | Download Bibtex

TACO: a Benchmark for Connectivity-invariance in Shape Correspondence

Published in Smart Tools and Applications in Graphics, 2024

Abstract

In real-world scenarios, a major limitation for shape-matching datasets is represented by having all the meshes of the same subject share their connectivity across different poses. Specifically, similar connectivities could provide a significant bias for shape matching algorithms, simplifying the matching process and potentially leading to correspondences based on the recurring triangle patterns rather than geometric correspondences between mesh parts. As a consequence, the resulting correspondence may be meaningless, and the evaluation of the algorithm may be misled. To overcome this limitation, we introduce TACO, a new dataset where meshes representing the same subject in different poses do not share the same connectivity, and we compute new ground truth correspondences between shapes. We extensively evaluate our dataset to ensure that ground truth isometries are properly preserved. We also use our dataset for validating state-of-the-art shape-matching algorithms, verifying a degradation in performance when the connectivity gets altered.

Recommended citation: Pedico Simone, et al. "Taco: A benchmark for connectivity-invariance in shape correspondence". Smart Tools and Applications in Graphics-Eurographics Italian Chapter Conference. 2024.
Download Paper | Download Bibtex

ReMatching: Low-Resolution Representations for Scalable Shape Correspondence

Published in European Conference on Computer Vision, 2024

Abstract

We introduce ReMatching, a novel shape correspondence solution based on the functional maps framework. Our method, by exploiting a new and appropriate re-meshing paradigm, can target shape-matching tasks even on meshes counting millions of vertices, where the original functional maps does not apply or requires a massive computational cost. The core of our procedure is a time-efficient remeshing algorithm which constructs a low-resolution geometry while acting conservatively on the original topology and metric. These properties allow translating the functional maps optimization problem on the resulting low-resolution representation, thus enabling efficient computation of correspondences with functional map approaches. Finally, we propose an efficient technique for extending the estimated correspondence to the original meshes. We show that our method is more efficient and effective through quantitative and qualitative comparisons, outperforming state-of-the-art pipelines in quality and computational cost.

Recommended citation: Maggioli Filippo, et al. "Rematching: Low-resolution representations for scalable shape correspondence". European Conference on Computer Vision (ECCV) 2024. Lecture Notes in Computer Science, vol 15095. Springer, Cham. 2025.
Download Paper | Download Bibtex

Volumetric Functional Maps

Published in arXiv, 2025

Abstract

The computation of volumetric correspondences between 3D shapes has great potential for medical and industrial applications. In this work, we pave the way for spectral volume mapping, extending for the first time the functional maps framework from the surface setting to the volumetric domain. We show that the eigenfunctions of the volumetric Laplace operator define a functional space that is suitable for high-quality signal transfer. We also experiment with various techniques that edit this functional space, porting them from the surface to the volume setting. We validate our method on novel volumetric datasets and on tetrahedralizations of well established surface datasets, also showcasing practical applications involving both discrete and continuous signal mapping, for segmentation transfer, mesh connectivity transfer and solid texturing. Last but not least, we show that considering the volumetric spectrum greatly improves the accuracy for classical shape matching tasks among surfaces, consistently outperforming existing surface-only spectral methods.

Recommended citation: Maggioli Filippo, et al. "Volumetric functional maps." arXiv preprint arXiv:2506.13212. 2025.
Download Paper | Download Bibtex

talks

Orthogonalized Fourier polynomials for signal approximation and transfer

Published:

Abstract
We propose a novel approach for the approximation and transfer of signals across 3D shapes. The proposed solution is based on taking pointwise polynomials of the Fourier-like Laplacian eigenbasis, which provides a compact and expressive representation for general signals defined on the surface. Key to our approach is the construction of a new orthonormal basis upon the set of these linearly dependent polynomials. We analyze the properties of this representation, and further provide a complete analysis of the involved parameters. Our technique results in accurate approximation and transfer of various families of signals between near-isometric and non-isometric shapes, even under poor initialization. Our experiments, showcased on a selection of downstream tasks such as filtering and detail transfer, show that our method is more robust to discretization artifacts, deformation and noise as compared to alternative approaches.

Efficiently parallelizable Strassen-based multiplication of a matrix by its transpose

Published:

Abstract
The multiplication of a matrix by its transpose, \(A^TA\), appears as an intermediate operation in the solution of a wide set of problems. In this paper, we propose a new cache-oblivious algorithm (ATA) for computing this product, based upon the classical Strassen algorithm as a sub-routine. In particular, we decrease the computational cost to \(2/3\) the time required by Strassen’s algorithm, amounting to \(14/3n^{\mathrm{log}_2 7}\) floating point operations. ATA works for generic rectangular matrices, and exploits the peculiar symmetry of the resulting product matrix for saving memory. In addition, we provide an extensive implementation study of ATA in a shared memory system, and extend its applicability to a distributed environment. To support our findings, we compare our algorithm with state-of-the-art solutions specialized in the computation of \(A^TA\), as well as with solutions for the computation of the general \(A^TB\) product applied to this problem. Our experiments highlight good scalability with respect to both the matrix size and the number of involved processes, as well as favorable performance for both the parallel paradigms and the sequential implementation when compared with other methods in the literature.

MoMaS: mold manifold simulation for real-time procedural texturing

Published:

Abstract
The slime mold algorithm has recently been under the spotlight thanks to its compelling properties studied across many disciplines like biology, computation theory, and artificial intelligence. However, existing implementations act only on planar surfaces, and no adaptation to arbitrary surfaces is available. Inspired by this gap, we propose a novel characterization of the mold algorithm to work on arbitrary curved surfaces. Our algorithm is easily parallelizable on GPUs and allows to model the evolution of millions of agents in real-time over surface meshes with several thousand triangles, while keeping the simplicity proper of the slime paradigm. We perform a comprehensive set of experiments, providing insights on stability, behavior, and sensibility to various design choices. We characterize a broad collection of behaviors with a limited set of controllable and interpretable parameters, enabling a novel family of heterogeneous and high-quality procedural textures. The appearance and complexity of these patterns are well-suited to diverse materials and scopes, and we add another layer of generalization by allowing different mold species to compete and interact in parallel.

A physically-inspired approach to the simulation of plant wilting

Published:

Abstract
Plants are among the most complex objects to be modeled in computer graphics. While a large body of work is concerned with structural modeling and the dynamic reaction to external forces, our work focuses on the dynamic deformation caused by plant internal wilting processes. To this end, we motivate the simulation of water transport inside the plant which is a key driver of the wilting process. We then map the change of water content in individual plant parts to branch stiffness values and obtain the wilted plant shape through a position based dynamics simulation. We show, that our approach can recreate measured wilting processes and does so with a higher fidelity than approaches ignoring the internal water flow. Realistic plant wilting is not only important in a computer graphics context but can also aid the development of machine learning algorithms in agricultural applications through the generation of synthetic training data.

Efficient Generation of Multimodal Fluid Simulation Data

Published:

Abstract
In this work, we introduce an efficient generation procedure to produce synthetic multi-modal datasets of fluid simulations. The procedure can reproduce the dynamics of fluid flows and allows for exploring and learning various properties of their complex behavior, from distinct perspectives and modalities. We employ our framework to generate a set of thoughtfully designed training datasets, which attempt to span specific fluid simulation scenarios in a meaningful way. The properties of our contributions are demonstrated by evaluating recently published algorithms for the neural fluid simulation and fluid inverse rendering tasks using our benchmark datasets. Our contribution aims to fulfill the community’s need for standardized training data, fostering more reproducibile and robust research.

Scalable geometry processing in computer graphics applications

Published:

Abstract
This thesis explores and investigates scalable solutions, grounded on geometry processing and differential geometry concepts, to different computer graphics tasks. My Ph.D. path gave me the opportunity to probe many research topics in the field of computer graphics, as well as delve into mathematical and computational problems. As a summary of my research activity, this thesis echoes my exploration, collecting results from different areas of computer graphics and computational geometry. From novel unified frameworks in spectral geometry to procedural texturing techniques, simulations, and matrix multiplication algorithms, all the discussed topics find their communion in the idea of providing geometry processing solutions made to scale for large volumes of data.

TACO: a benchmark for connectivity-invariance in shape correspondence

Published:

Abstract
In real-world scenarios, a major limitation for shape-matching datasets is represented by having all the meshes of the same subject share their connectivity across different poses. Specifically, similar connectivities could provide a significant bias for shape matching algorithms, simplifying the matching process and potentially leading to correspondences based on the recurring triangle patterns rather than geometric correspondences between mesh parts. As a consequence, the resulting correspondence may be meaningless, and the evaluation of the algorithm may be misled. To overcome this limitation, we introduce TACO, a new dataset where meshes representing the same subject in different poses do not share the same connectivity, and we compute new ground truth correspondences between shapes. We extensively evaluate our dataset to ensure that ground truth isometries are properly preserved. We also use our dataset for validating state-of-the-art shape-matching algorithms, verifying a degradation in performance when the connectivity gets altered.

Volumetric Functional Maps

Published:

Abstract
The computation of volumetric correspondences between 3D shapes has great potential for medical and industrial applications. In this work, we pave the way for spectral volume mapping, extending for the first time the functional maps framework from the surface setting to the volumetric domain. We show that the eigenfunctions of the volumetric Laplace operator define a functional space that is suitable for high-quality signal transfer. We also experiment with various techniques that edit this functional space, porting them from the surface to the volume setting. We validate our method on novel volumetric datasets and on tetrahedralizations of well established surface datasets, also showcasing practical applications involving both discrete and continuous signal mapping, for segmentation transfer, mesh connectivity transfer and solid texturing. Last but not least, we show that considering the volumetric spectrum greatly improves the accuracy for classical shape matching tasks among surfaces, consistently outperforming existing surface-only spectral methods.

teaching

Introduction to Algorithms

Teaching assistant – Undergraduate course, Sapienza – University of Rome, Department of Computer Science, 2021

Teaching assistant role for the undergrad course on Introduction to Algorithms.
The role involved teaching duties, tutoring responsibilities, and supervision during exams.

Computer Architecture

Undergraduate course, Pegaso University, Department of Computer Science and Information Technologies, 2024

Adjunct professor in charge of the undergrad course on Computer Architecture.

Networking and Cybersecurity

Undergraduate course, Pegaso University, Department of Computer Science and Information Technologies, 2024

Adjunct professor in charge of the undergrad course on Networking and Cybersecurity.

Teaching Networking

Teaching qualification training course, Pegaso University, 2025

Teaching qualification training course on Computer Networking.

Digital Skills for Teaching

Teaching qualification training course, Pegaso University, 2025

Teaching qualification training course on Digital Skills for Teaching.