2023
Conference article
Open Access
Social and hUman ceNtered XR
Vairo C., Callieri M., Carrara F., Cignoni P., Di Benedetto M., Gennaro C., Giorgi D., Palma G., Vadicamo L., Amato G.
The Social and hUman ceNtered XR (SUN) project is focused on developing eXtended Reality (XR) solutions that integrate the physical and virtual world in a way that is convincing from a human and social perspective. In this paper, we outline the limitations that the SUN project aims to overcome, including the lack of scalable and cost-effective solutions for developing XR applications, limited solutions for mixing the virtual and physical environment, and barriers related to resource limitations of end-user devices. We also propose solutions to these limitations, including using artificial intelligence, computer vision, and sensor analysis to incrementally learn the visual and physical properties of real objects and generate convincing digital twins in the virtual environment. Additionally, the SUN project aims to provide wearable sensors and haptic interfaces to enhance natural interaction with the virtual environment and advanced solutions for user interaction. Finally, we describe three real-life scenarios in which we aim to demonstrate the proposed solutions.Source: Ital-IA 2023 - Workshop su AI per l'industria, Pisa, Italy, 29-31/05/2023
Vairo C., Callieri M., Carrara F., Cignoni P., Di Benedetto M., Gennaro C., Giorgi D., Palma G., Vadicamo L., Amato G.
The Social and hUman ceNtered XR (SUN) project is focused on developing eXtended Reality (XR) solutions that integrate the physical and virtual world in a way that is convincing from a human and social perspective. In this paper, we outline the limitations that the SUN project aims to overcome, including the lack of scalable and cost-effective solutions for developing XR applications, limited solutions for mixing the virtual and physical environment, and barriers related to resource limitations of end-user devices. We also propose solutions to these limitations, including using artificial intelligence, computer vision, and sensor analysis to incrementally learn the visual and physical properties of real objects and generate convincing digital twins in the virtual environment. Additionally, the SUN project aims to provide wearable sensors and haptic interfaces to enhance natural interaction with the virtual environment and advanced solutions for user interaction. Finally, we describe three real-life scenarios in which we aim to demonstrate the proposed solutions.Source: Ital-IA 2023 - Workshop su AI per l'industria, Pisa, Italy, 29-31/05/2023
See at:
ceur-ws.org 
| ISTI Repository | ISTI Repository | CNR ExploRA
2023
Conference article
Open Access
A geometry-preserving shape optimization tool based on deep learning
Favilli A., Laccone F., Cignoni P., Malomo L., Giorgi D.
In free-form architecture, computational design tools have made it easy to create geometric models. However, obtaining good structural performance is difficult and requires further steps, such as shape optimization, to enhance system efficiency and material savings. This paper provides a user interface for form-finding and shape optimization of triangular grid shells. Users can minimize structural compliance, while ensuring small changes in their original design. A graph neural network learns to update the nodal coordinates of the grid shell to reduce a loss function based on strain energy. The interface can manage complex shapes and irregular tessellations. A variety of examples prove the effectiveness of the tool.Source: IWSS 2023 - Italian Workshop on Shell and Spatial Structures, pp. 549–558, Torino, Italy, 26-28/06/2023
DOI: 10.1007/978-3-031-44328-2_57
Metrics:
Favilli A., Laccone F., Cignoni P., Malomo L., Giorgi D.
In free-form architecture, computational design tools have made it easy to create geometric models. However, obtaining good structural performance is difficult and requires further steps, such as shape optimization, to enhance system efficiency and material savings. This paper provides a user interface for form-finding and shape optimization of triangular grid shells. Users can minimize structural compliance, while ensuring small changes in their original design. A graph neural network learns to update the nodal coordinates of the grid shell to reduce a loss function based on strain energy. The interface can manage complex shapes and irregular tessellations. A variety of examples prove the effectiveness of the tool.Source: IWSS 2023 - Italian Workshop on Shell and Spatial Structures, pp. 549–558, Torino, Italy, 26-28/06/2023
DOI: 10.1007/978-3-031-44328-2_57
Metrics:
See at:
link.springer.com | CNR ExploRA
2023
Conference article
Open Access
Computational design of fabricable geometric patterns
Scandurra E., Laccone F., Malomo L., Callieri M., Cignoni P., Giorgi D.
This paper addresses the design of surfaces as assemblies of geometric patterns with predictable performance in response to mechanical stimuli. We design a family of tileable and fabricable patterns represented as triangle meshes, which can be assembled for creating surface tessellations. First, a regular recursive subdivision of the planar space generates different geometric configurations for candidate patterns, having interesting and varied aesthetic properties. Then, a refinement step addresses manufacturability by solving for non-manifold configurations and sharp angles which would produce disconnected or fragile patterns. We simulate our patterns to evaluate their mechanical response when loaded in different scenarios targeting out-of-plane bending. Through a simple browsing interface, we show that our patterns span a variety of different bending behaviors. The result is a library of patterns with varied aesthetics and predefined mechanical behavior, to use for the direct design of mechanical metamaterials. To assess the feasibility of our approach, we show a pair of fabricated 3D objects with different curvatures.Source: STAG 2023 - Smart Tools and Applications in Graphics 2023 - Eurographics Italian Chapter Conference, pp. 81–91, Matera, Italy, 16-17/11/2023
DOI: 10.2312/stag.20231297
Metrics:
Scandurra E., Laccone F., Malomo L., Callieri M., Cignoni P., Giorgi D.
This paper addresses the design of surfaces as assemblies of geometric patterns with predictable performance in response to mechanical stimuli. We design a family of tileable and fabricable patterns represented as triangle meshes, which can be assembled for creating surface tessellations. First, a regular recursive subdivision of the planar space generates different geometric configurations for candidate patterns, having interesting and varied aesthetic properties. Then, a refinement step addresses manufacturability by solving for non-manifold configurations and sharp angles which would produce disconnected or fragile patterns. We simulate our patterns to evaluate their mechanical response when loaded in different scenarios targeting out-of-plane bending. Through a simple browsing interface, we show that our patterns span a variety of different bending behaviors. The result is a library of patterns with varied aesthetics and predefined mechanical behavior, to use for the direct design of mechanical metamaterials. To assess the feasibility of our approach, we show a pair of fabricated 3D objects with different curvatures.Source: STAG 2023 - Smart Tools and Applications in Graphics 2023 - Eurographics Italian Chapter Conference, pp. 81–91, Matera, Italy, 16-17/11/2023
DOI: 10.2312/stag.20231297
Metrics:
See at:
diglib.eg.org | ISTI Repository | CNR ExploRA
2023
Journal article
Open Access
Geometric deep learning for statics-aware grid shells
Favilli A., Laccone F., Cignoni P., Malomo L., Giorgi D.
This paper introduces a novel method for shape optimization and form-finding of free-form, triangular grid shells, based on geometric deep learning. We define an architecture which consumes a 3D mesh representing the initial design of a free-form grid shell, and outputs vertex displacements to get an optimized grid shell that minimizes structural compliance, while preserving design intent. The main ingredients of the architecture are layers that produce deep vertex embeddings from geometric input features, and a differentiable loss implementing structural analysis. We evaluate the method performance on a benchmark of eighteen free-form grid shell structures characterized by various size, geometry, and tessellation. Our results demonstrate that our approach can solve the shape optimization and form finding problem for a diverse range of structures, more effectively and efficiently than existing common tools.Source: Computers & structures 292 (2023). doi:10.1016/j.compstruc.2023.107238
DOI: 10.1016/j.compstruc.2023.107238
Metrics:
Favilli A., Laccone F., Cignoni P., Malomo L., Giorgi D.
This paper introduces a novel method for shape optimization and form-finding of free-form, triangular grid shells, based on geometric deep learning. We define an architecture which consumes a 3D mesh representing the initial design of a free-form grid shell, and outputs vertex displacements to get an optimized grid shell that minimizes structural compliance, while preserving design intent. The main ingredients of the architecture are layers that produce deep vertex embeddings from geometric input features, and a differentiable loss implementing structural analysis. We evaluate the method performance on a benchmark of eighteen free-form grid shell structures characterized by various size, geometry, and tessellation. Our results demonstrate that our approach can solve the shape optimization and form finding problem for a diverse range of structures, more effectively and efficiently than existing common tools.Source: Computers & structures 292 (2023). doi:10.1016/j.compstruc.2023.107238
DOI: 10.1016/j.compstruc.2023.107238
Metrics:
See at:
ISTI Repository | www.sciencedirect.com | CNR ExploRA
2022
Report
Closed Access
Statics-aware 3D gridshells: a differential approach towards shape optimization
Favilli A., Giorgi D., Laccone F., Malomo L., Cignoni P.
In the context of architecture, gridshells are three-dimensional frame structures in which loads are entirely born by edges, or beams. Our contribution is to draw the way to a computational method that, given an input gridshell provided by a designer, slightly changes the input to ensure good static performance. The changing is induced by structure node repositioning. If the gridshell is represented as a surface mesh, the problem boils down to finding a proper vertex displacement. The vertex displacement should strike a happy medium between structure rigidity, with load deformation as low as possible, and structure resistance, preventing stress caused breaks. In this report, we introduce a shape optimization strategy based on automatic differentiation of a loss function, which embeds the static equilibrium problem of a girdshell.Source: ISTI Technical Report, ISTI-2022-TR/017, 2022
DOI: 10.32079/isti-tr-2022/017
Metrics:
Favilli A., Giorgi D., Laccone F., Malomo L., Cignoni P.
In the context of architecture, gridshells are three-dimensional frame structures in which loads are entirely born by edges, or beams. Our contribution is to draw the way to a computational method that, given an input gridshell provided by a designer, slightly changes the input to ensure good static performance. The changing is induced by structure node repositioning. If the gridshell is represented as a surface mesh, the problem boils down to finding a proper vertex displacement. The vertex displacement should strike a happy medium between structure rigidity, with load deformation as low as possible, and structure resistance, preventing stress caused breaks. In this report, we introduce a shape optimization strategy based on automatic differentiation of a loss function, which embeds the static equilibrium problem of a girdshell.Source: ISTI Technical Report, ISTI-2022-TR/017, 2022
DOI: 10.32079/isti-tr-2022/017
Metrics:
See at: CNR ExploRA
2022
Report
Closed Access
Geometric deep learning for statics-aware 3D gridshells
Favilli A., Giorgi D., Laccone F., Malomo L., Cignoni P.
In the context of architecture, gridshells are three-dimensional frame structures in which loads are entirely born by edges, or beams. Our contribution is to draw the way to a computational method that, given an input gridshell provided by a designer, slightly changes the input to ensure good static performance. The changing is induced by structure node repositioning. If the gridshell is represented as a surface mesh, the problem boils down to finding a proper vertex displacement. The vertex displacement should strike a happy medium between structure rigidity, with load deformation as low as possible, and structure resistance, preventing stress caused breaks. In this report, we inculde a solution to solve this mesh vertex displacement learning problem with a target goal of reducing a physically-based loss function, namely the mean strain energy of a gridshell, by means of a graph neural network. We adopt several geometric input features and discuss their effects on the results.Source: ISTI Technical Report, ISTI-2022-TR/016, 2022
DOI: 10.32079/isti-tr-2022/016
Metrics:
Favilli A., Giorgi D., Laccone F., Malomo L., Cignoni P.
In the context of architecture, gridshells are three-dimensional frame structures in which loads are entirely born by edges, or beams. Our contribution is to draw the way to a computational method that, given an input gridshell provided by a designer, slightly changes the input to ensure good static performance. The changing is induced by structure node repositioning. If the gridshell is represented as a surface mesh, the problem boils down to finding a proper vertex displacement. The vertex displacement should strike a happy medium between structure rigidity, with load deformation as low as possible, and structure resistance, preventing stress caused breaks. In this report, we inculde a solution to solve this mesh vertex displacement learning problem with a target goal of reducing a physically-based loss function, namely the mean strain energy of a gridshell, by means of a graph neural network. We adopt several geometric input features and discuss their effects on the results.Source: ISTI Technical Report, ISTI-2022-TR/016, 2022
DOI: 10.32079/isti-tr-2022/016
Metrics:
See at: CNR ExploRA
2021
Journal article
Open Access
Smart Working at CNR-ISTI in the COVID-19 Era
Scopigno R., Giorgi D.
The CNR Institute for Information Science and Technologies describes its experience in adapting to smart working, which has dramatically changed the institute's modus operandi for most of the year 2020.Source: ERCIM news (2021): 6–7.
Scopigno R., Giorgi D.
The CNR Institute for Information Science and Technologies describes its experience in adapting to smart working, which has dramatically changed the institute's modus operandi for most of the year 2020.Source: ERCIM news (2021): 6–7.
See at:
ercim-news.ercim.eu | ISTI Repository | CNR ExploRA
2021
Journal article
Open Access
Computational design, fabrication and evaluation of rubber protein models
Alderighi T., Giorgi D., Malomo L., Cignoni P., Zoppè M.
Tangible 3D molecular models conceptualize complex phenomena in a stimulating and engaging format. This is especially true for learning environments, where additive manufacturing is increasingly used to produce teaching aids for chemical education. However, the 3D models presented previously are limited in the type of molecules they can represent and the amount of information they carry. In addition, they have little role in representing complex biological entities such as proteins. We present the first complete workflow for the fabrication of soft models of complex proteins of any size. We leverage on molding technologies to generate accurate, soft models which incorporate both spatial and functional aspects of large molecules. Our method covers the whole pipeline from molecular surface preparation and editing to actual 3D model fabrication. The models fabricated with our strategy can be used as aids to illustrate biological functional behavior, such as assembly in quaternary structure and docking mechanisms, which are difficult to convey with traditional visualization methods. We applied the proposed framework to fabricate a set of 3D protein models, and we validated the appeal of our approach in a classroom setting.Source: Computers & graphics 98 (2021): 177–187. doi:10.1016/j.cag.2021.05.010
DOI: 10.1016/j.cag.2021.05.010
Metrics:
Alderighi T., Giorgi D., Malomo L., Cignoni P., Zoppè M.
Tangible 3D molecular models conceptualize complex phenomena in a stimulating and engaging format. This is especially true for learning environments, where additive manufacturing is increasingly used to produce teaching aids for chemical education. However, the 3D models presented previously are limited in the type of molecules they can represent and the amount of information they carry. In addition, they have little role in representing complex biological entities such as proteins. We present the first complete workflow for the fabrication of soft models of complex proteins of any size. We leverage on molding technologies to generate accurate, soft models which incorporate both spatial and functional aspects of large molecules. Our method covers the whole pipeline from molecular surface preparation and editing to actual 3D model fabrication. The models fabricated with our strategy can be used as aids to illustrate biological functional behavior, such as assembly in quaternary structure and docking mechanisms, which are difficult to convey with traditional visualization methods. We applied the proposed framework to fabricate a set of 3D protein models, and we validated the appeal of our approach in a classroom setting.Source: Computers & graphics 98 (2021): 177–187. doi:10.1016/j.cag.2021.05.010
DOI: 10.1016/j.cag.2021.05.010
Metrics:
See at:
ISTI Repository | Computers & Graphics 
| Computers & Graphics | www.sciencedirect.com | CNR ExploRA
2020
Journal article
Closed Access
ReviewerNet: A visualization platform for the selection of academic reviewers
Salinas M., Giorgi D., Ponchio F., Cignoni P.
We propose ReviewerNet, an online, interactive visualization system aimed to improve the reviewer selection process in the academic domain. Given a paper submitted for publication, we assume that good candidate reviewers can be chosen among the authors of a small set of pertinent papers; ReviewerNet supports the construction of such set of papers, by visualizing and exploring a literature citation network. The system helps journal editors and Program Committee members to select reviewers that do not have any conflict-of-interest and are representative of different research groups, by visualising the careers and co-authorship relations of candidate reviewers. The system is publicly available, and is demonstrated in the field of Computer Graphics.Source: Computers & graphics 89 (2020): 77–87. doi:10.1016/j.cag.2020.04.006
DOI: 10.1016/j.cag.2020.04.006
Metrics:
Salinas M., Giorgi D., Ponchio F., Cignoni P.
We propose ReviewerNet, an online, interactive visualization system aimed to improve the reviewer selection process in the academic domain. Given a paper submitted for publication, we assume that good candidate reviewers can be chosen among the authors of a small set of pertinent papers; ReviewerNet supports the construction of such set of papers, by visualizing and exploring a literature citation network. The system helps journal editors and Program Committee members to select reviewers that do not have any conflict-of-interest and are representative of different research groups, by visualising the careers and co-authorship relations of candidate reviewers. The system is publicly available, and is demonstrated in the field of Computer Graphics.Source: Computers & graphics 89 (2020): 77–87. doi:10.1016/j.cag.2020.04.006
DOI: 10.1016/j.cag.2020.04.006
Metrics:
See at:
Computers & Graphics | www.sciencedirect.com | CNR ExploRA
2020
Journal article
Open Access
Optimizing object decomposition to reduce visual artifacts in 3D printing
Filoscia I., Alderighi T., Giorgi D., Malomo L., Callieri M., Cignoni P.
We propose a method for the automatic segmentation of 3D objects into parts which can be individually 3D printed and then reassembled by preserving the visual quality of the final object. Our technique focuses on minimizing the surface affected by supports, decomposing the object into multiple parts whose printing orientation is automatically chosen. The segmentation reduces the visual impact on the fabricated model producing non-planar cuts that adapt to the object shape. This is performed by solving an optimization problem that balances the effects of supports and cuts, while trying to place both in occluded regions of the object surface. To assess the practical impact of the solution, we show a number of segmented, 3D printed and reassembled objects.Source: Computer graphics forum (Print) 39 (2020): 423–434. doi:10.1111/cgf.13941
DOI: 10.1111/cgf.13941
Project(s): EVOCATION
Metrics:
Filoscia I., Alderighi T., Giorgi D., Malomo L., Callieri M., Cignoni P.
We propose a method for the automatic segmentation of 3D objects into parts which can be individually 3D printed and then reassembled by preserving the visual quality of the final object. Our technique focuses on minimizing the surface affected by supports, decomposing the object into multiple parts whose printing orientation is automatically chosen. The segmentation reduces the visual impact on the fabricated model producing non-planar cuts that adapt to the object shape. This is performed by solving an optimization problem that balances the effects of supports and cuts, while trying to place both in occluded regions of the object surface. To assess the practical impact of the solution, we show a number of segmented, 3D printed and reassembled objects.Source: Computer graphics forum (Print) 39 (2020): 423–434. doi:10.1111/cgf.13941
DOI: 10.1111/cgf.13941
Project(s): EVOCATION
Metrics:
See at:
onlinelibrary.wiley.com | ISTI Repository | Computer Graphics Forum | CNR ExploRA
2019
Journal article
Open Access
Volume-aware design of composite molds
Alderighi T., Malomo L., Giorgi D., Bickel B., Cignoni P. Pietroni N.
We propose a novel technique for the automatic design of molds to cast highly complex shapes. The technique generates composite, two-piece molds. Each mold piece is made up of a hard plastic shell and a flexible silicone part. Thanks to the thin, soft, and smartly shaped silicone part, which is kept in place by a hard plastic shell, we can cast objects of unprecedented complexity. An innovative algorithm based on a volumetric analysis defines the layout of the internal cuts in the silicone mold part. Our approach can robustly handle thin protruding features and intertwined topologies that have caused previous methods to fail. We compare our results with state of the art techniques, and we demonstrate the casting of shapes with extremely complex geometry.Source: ACM transactions on graphics 38 (2019). doi:10.1145/3306346.3322981
DOI: 10.1145/3306346.3322981
Project(s): EVOCATION, EMOTIVE , MATERIALIZABLE
Metrics:
Alderighi T., Malomo L., Giorgi D., Bickel B., Cignoni P. Pietroni N.
We propose a novel technique for the automatic design of molds to cast highly complex shapes. The technique generates composite, two-piece molds. Each mold piece is made up of a hard plastic shell and a flexible silicone part. Thanks to the thin, soft, and smartly shaped silicone part, which is kept in place by a hard plastic shell, we can cast objects of unprecedented complexity. An innovative algorithm based on a volumetric analysis defines the layout of the internal cuts in the silicone mold part. Our approach can robustly handle thin protruding features and intertwined topologies that have caused previous methods to fail. We compare our results with state of the art techniques, and we demonstrate the casting of shapes with extremely complex geometry.Source: ACM transactions on graphics 38 (2019). doi:10.1145/3306346.3322981
DOI: 10.1145/3306346.3322981
Project(s): EVOCATION
, EMOTIVE , MATERIALIZABLE Metrics:
See at:
ISTI Repository | ACM Transactions on Graphics | dl.acm.org | ACM Transactions on Graphics | CNR ExploRA
2019
Report
Unknown
A survey on 3D shape segmentation with focus on digital fabrication
Filoscia I., Alderighi T. Cignoni P., Giorgi D., Malomo L.
Segmenting 3D objects into parts is fundamental to a number of applications in computer graphics, including parametrization, texture mapping, shape matching, morphing, multi-resolution modeling, mesh editing, compression and animation [22]. Broadly speaking, shape segmentation techniques can be divided into geometry-based and semantics-based techniques. Geometry-based segmentations aim to partition the object into parts which have well-defined geometric properties such as size, curvature, or distance to a fitting primitive like a plane. Semantics-based segmentations, in turn, aim at identifying parts which are either visually relevant or meaningful in a given context, such as functional parts on mechanical objects or body parts on human models. Recently, 3D segmentation also drawn attention as a tool for efficient fabrication. The decomposition of objects into parts, indeed, helps solving different issues related to fabrication, such as height field constraints, volume constraints and need for supporting structures. In this work we present a complete survey of segmentation techniques, also highlighting their strengths and weaknesses. Our aim is to produce a handy overview to people who want to approach the problem of segmentation, especially if they want to apply it to digital fabrication.Source: ISTI Technical reports, 2019
Filoscia I., Alderighi T. Cignoni P., Giorgi D., Malomo L.
Segmenting 3D objects into parts is fundamental to a number of applications in computer graphics, including parametrization, texture mapping, shape matching, morphing, multi-resolution modeling, mesh editing, compression and animation [22]. Broadly speaking, shape segmentation techniques can be divided into geometry-based and semantics-based techniques. Geometry-based segmentations aim to partition the object into parts which have well-defined geometric properties such as size, curvature, or distance to a fitting primitive like a plane. Semantics-based segmentations, in turn, aim at identifying parts which are either visually relevant or meaningful in a given context, such as functional parts on mechanical objects or body parts on human models. Recently, 3D segmentation also drawn attention as a tool for efficient fabrication. The decomposition of objects into parts, indeed, helps solving different issues related to fabrication, such as height field constraints, volume constraints and need for supporting structures. In this work we present a complete survey of segmentation techniques, also highlighting their strengths and weaknesses. Our aim is to produce a handy overview to people who want to approach the problem of segmentation, especially if they want to apply it to digital fabrication.Source: ISTI Technical reports, 2019
See at: CNR ExploRA
2019
Report
Unknown
A novel segmentation algorithm for support-free 3D printing
Filoscia I., Alderighi T., Cignoni P., Giorgi D., Malomo L.
Digital fabrication, and 3D printing in particular, are growing important in a variety of fields, from industry to medicine, from cultural heritage to art, as often they are more rapid and cheaper than traditional manufacturing techniques. In this context, our aim is to make it easier for people to print high-quality objects at home, even of complex shape, by incorporating into software some of the professional skills that are needed to fully exploit the potential of 3D printing A major limitation of FDM printers is that the material must be supported when it is deposited: bridge-like structures or hanging features, which are not supported by other object parts, often need additional support structures. Indeed, most printers can produce overhangs, but only up to a certain tolerance angle, usually in-between 30 and 60 degrees. To solve this problem, additional columns of material are built to support the parts in overhang. These supports need to be removed in a postprocessing step, which may cause imperfections on the surface, or even break thin parts. A possible solution, which we adopt in this work, is to segment the object into smaller parts which can be printed individually with no or minimal need for supports. The main drawback is that the decomposition introduces cuts on the object surface, in correspondence of the boundaries between parts. Such cuts can be as visually disturbing as the imperfections due to the removal of supports, or even more. Therefore, given a 3D mesh representing the input object, our aim is to develop a segmentation technique to partition the mesh into a small number of simpler parts, each of which can be printed with no or minimal support, and such that the boundaries between the parts (i.e., where the cuts in the object surface will be) affect the appearance of the printed model as little as possible. We pose the segmentation problem as a multi-labeling problem solved via functional minimization. In our formulation, the data points will be mesh elements (either faces or clusters of faces), and the labels will be potential printing directions. We will define an objective function that takes into account the area of supported regions and support footings, as well as the visual impact of the cuts, in terms of both their length and location on the surface. We formulate this multi-labeling problem as an Integer Linear Program (ILP), which can be solved using standard optimization packages such as Gurobi.Source: ISTI Technical reports, 2019
Filoscia I., Alderighi T., Cignoni P., Giorgi D., Malomo L.
Digital fabrication, and 3D printing in particular, are growing important in a variety of fields, from industry to medicine, from cultural heritage to art, as often they are more rapid and cheaper than traditional manufacturing techniques. In this context, our aim is to make it easier for people to print high-quality objects at home, even of complex shape, by incorporating into software some of the professional skills that are needed to fully exploit the potential of 3D printing A major limitation of FDM printers is that the material must be supported when it is deposited: bridge-like structures or hanging features, which are not supported by other object parts, often need additional support structures. Indeed, most printers can produce overhangs, but only up to a certain tolerance angle, usually in-between 30 and 60 degrees. To solve this problem, additional columns of material are built to support the parts in overhang. These supports need to be removed in a postprocessing step, which may cause imperfections on the surface, or even break thin parts. A possible solution, which we adopt in this work, is to segment the object into smaller parts which can be printed individually with no or minimal need for supports. The main drawback is that the decomposition introduces cuts on the object surface, in correspondence of the boundaries between parts. Such cuts can be as visually disturbing as the imperfections due to the removal of supports, or even more. Therefore, given a 3D mesh representing the input object, our aim is to develop a segmentation technique to partition the mesh into a small number of simpler parts, each of which can be printed with no or minimal support, and such that the boundaries between the parts (i.e., where the cuts in the object surface will be) affect the appearance of the printed model as little as possible. We pose the segmentation problem as a multi-labeling problem solved via functional minimization. In our formulation, the data points will be mesh elements (either faces or clusters of faces), and the labels will be potential printing directions. We will define an objective function that takes into account the area of supported regions and support footings, as well as the visual impact of the cuts, in terms of both their length and location on the surface. We formulate this multi-labeling problem as an Integer Linear Program (ILP), which can be solved using standard optimization packages such as Gurobi.Source: ISTI Technical reports, 2019
See at: CNR ExploRA
2019
Journal article
Open Access
Towards a topological-geometrical theory of group equivariant non-expansive operators for data analysis and machine learning
Bergomi M. G., Frosini P., Giorgi D., Quercioli N.
We provide a general mathematical framework for group and set equivariance in machine learning. We define group equivariant non-expansive operators (GENEOs) as maps between function spaces associated with groups of transformations. We study the topological and metric properties of the space of GENEOs to evaluate their approximating power and set the basis for general strategies to initialize and compose operators. We define suitable pseudo-metrics for the function spaces, the equivariance groups and the set of non-expansive operators. We prove that, under suitable assumptions, the space of GENEOs is compact and convex. These results provide fundamental guarantees in a machine learning perspective. By considering isometry-equivariant non-expansive operators, we describe a simple strategy to select and sample operators. Thereafter, we show how selected and sampled operators can be used both to perform classical metric learning and to inject knowledge in artificial neural networks.Source: Nature Machine Intelligence 1 (2019): 423–433. doi:10.1038/s42256-019-0087-3
DOI: 10.1038/s42256-019-0087-3
DOI: 10.48550/arxiv.1812.11832
Project(s): 5HTCircuits
Metrics:
Bergomi M. G., Frosini P., Giorgi D., Quercioli N.
We provide a general mathematical framework for group and set equivariance in machine learning. We define group equivariant non-expansive operators (GENEOs) as maps between function spaces associated with groups of transformations. We study the topological and metric properties of the space of GENEOs to evaluate their approximating power and set the basis for general strategies to initialize and compose operators. We define suitable pseudo-metrics for the function spaces, the equivariance groups and the set of non-expansive operators. We prove that, under suitable assumptions, the space of GENEOs is compact and convex. These results provide fundamental guarantees in a machine learning perspective. By considering isometry-equivariant non-expansive operators, we describe a simple strategy to select and sample operators. Thereafter, we show how selected and sampled operators can be used both to perform classical metric learning and to inject knowledge in artificial neural networks.Source: Nature Machine Intelligence 1 (2019): 423–433. doi:10.1038/s42256-019-0087-3
DOI: 10.1038/s42256-019-0087-3
DOI: 10.48550/arxiv.1812.11832
Project(s): 5HTCircuits
Metrics:
See at:
arXiv.org e-Print Archive | Nature Machine Intelligence | Nature Machine Intelligence | Archivio istituzionale della ricerca - Alma Mater Studiorum Università di Bologna | ISTI Repository | doi.org | www.nature.com | CNR ExploRA
2019
Contribution to conference
Open Access
Computational fabrication of macromolecules to enhance perception and understanding of biological mechanisms
Alderighi T., Giorgi D., Malomo L., Cignoni P., Zoppè M.
We propose a fabrication technique for the fast and cheap production of 3D replicas of proteins. We leverage silicone casting with rigid molds, to produce flexible models which can be safely extracted from the mold, and easily manipulated to simulate the biological interaction mechanisms between proteins. We believe that tangible models can be useful in education as well as in laboratory settings, and that they will ease the understanding of fundamental principles of macromolecular organization.Source: Smart Tools and Applications in Graphics (STAG) 2019, pp. 103–104, Cagliari, Italy, 14-15/11/2019
DOI: 10.2312/stag.20191369
Metrics:
Alderighi T., Giorgi D., Malomo L., Cignoni P., Zoppè M.
We propose a fabrication technique for the fast and cheap production of 3D replicas of proteins. We leverage silicone casting with rigid molds, to produce flexible models which can be safely extracted from the mold, and easily manipulated to simulate the biological interaction mechanisms between proteins. We believe that tangible models can be useful in education as well as in laboratory settings, and that they will ease the understanding of fundamental principles of macromolecular organization.Source: Smart Tools and Applications in Graphics (STAG) 2019, pp. 103–104, Cagliari, Italy, 14-15/11/2019
DOI: 10.2312/stag.20191369
Metrics:
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diglib.eg.org | ISTI Repository | CNR ExploRA
2018
Journal article
Open Access
Computational topology to monitor human occupancy
Barsocchi P., Cassará P., Giorgi D., Moroni D., Pascali M. A.
The recent advances in sensing technologies, embedded systems, and wireless communication technologies, make it possible to develop smart systems to monitor human activities continuously. The occupancy of specific areas or rooms in a smart building is an important piece of information, to infer the behavior of people, or to trigger an advanced surveillancemodule. We propose a method based on computational topology to infer the occupancy of a room monitored for a week by a system of low-cost sensors.Source: Proceedings (MDPI) 2 (2018). doi:10.3390/proceedings2020099
DOI: 10.3390/proceedings2020099
DOI: 10.5281/zenodo.1159169
DOI: 10.5281/zenodo.1159170
Metrics:
Barsocchi P., Cassará P., Giorgi D., Moroni D., Pascali M. A.
The recent advances in sensing technologies, embedded systems, and wireless communication technologies, make it possible to develop smart systems to monitor human activities continuously. The occupancy of specific areas or rooms in a smart building is an important piece of information, to infer the behavior of people, or to trigger an advanced surveillancemodule. We propose a method based on computational topology to infer the occupancy of a room monitored for a week by a system of low-cost sensors.Source: Proceedings (MDPI) 2 (2018). doi:10.3390/proceedings2020099
DOI: 10.3390/proceedings2020099
DOI: 10.5281/zenodo.1159169
DOI: 10.5281/zenodo.1159170
Metrics:
See at:
doi.org | ZENODO | ZENODO | www.mdpi.com | Hyper Article en Ligne | www.mdpi.com | CNR ExploRA
2018
Report
Unknown
Metamolds: Computational design of silicone molds
Alderighi T., Malomo L., Giorgi D., Pietroni N., Bickel B., Cignoni P.
We propose a new method for fabricating digital objects through reusable silicone molds. Molds are generated by casting liquid silicone into custom 3D printed containers called metamolds. Metamolds automatically define the cuts that are needed to extract the cast object from the silicone mold. The shape of metamolds is designed through a novel segmentation technique, which takes into account both geometric and topological constraints involved in the process of mold casting. Our technique is simple, does not require to change the shape or topology of the input objects, and only requires off-the-shelf materials and technologies. We successfully tested our method on a set of challenging examples with complex shapes and rich geometric detail.Source: ISTI Technical reports, 2018
Project(s): EMOTIVE
Alderighi T., Malomo L., Giorgi D., Pietroni N., Bickel B., Cignoni P.
We propose a new method for fabricating digital objects through reusable silicone molds. Molds are generated by casting liquid silicone into custom 3D printed containers called metamolds. Metamolds automatically define the cuts that are needed to extract the cast object from the silicone mold. The shape of metamolds is designed through a novel segmentation technique, which takes into account both geometric and topological constraints involved in the process of mold casting. Our technique is simple, does not require to change the shape or topology of the input objects, and only requires off-the-shelf materials and technologies. We successfully tested our method on a set of challenging examples with complex shapes and rich geometric detail.Source: ISTI Technical reports, 2018
Project(s): EMOTIVE
See at: CNR ExploRA
2018
Journal article
Open Access
Metamolds: computational design of silicone molds
Alderighi T., Malomo L., Giorgi D., Pietroni N., Bickel B., Cignoni P.
We propose a new method for fabricating digital objects through reusable silicone molds. Molds are generated by casting liquid silicone into custom 3D printed containers called metamolds. Metamolds automatically define the cuts that are needed to extract the cast object from the silicone mold. The shape of metamolds is designed through a novel segmentation technique, which takes into account both geometric and topological constraints involved in the process of mold casting. Our technique is simple, does not require changing the shape or topology of the input objects, and only requires off-the-shelf materials and technologies. We successfully tested our method on a set of challenging examples with complex shapes and rich geometric detailSource: ACM transactions on graphics 37 (2018): 136:1–136:13. doi:10.1145/3197517.3201381
DOI: 10.1145/3197517.3201381
Project(s): EMOTIVE
Metrics:
Alderighi T., Malomo L., Giorgi D., Pietroni N., Bickel B., Cignoni P.
We propose a new method for fabricating digital objects through reusable silicone molds. Molds are generated by casting liquid silicone into custom 3D printed containers called metamolds. Metamolds automatically define the cuts that are needed to extract the cast object from the silicone mold. The shape of metamolds is designed through a novel segmentation technique, which takes into account both geometric and topological constraints involved in the process of mold casting. Our technique is simple, does not require changing the shape or topology of the input objects, and only requires off-the-shelf materials and technologies. We successfully tested our method on a set of challenging examples with complex shapes and rich geometric detailSource: ACM transactions on graphics 37 (2018): 136:1–136:13. doi:10.1145/3197517.3201381
DOI: 10.1145/3197517.3201381
Project(s): EMOTIVE
Metrics:
See at:
ISTI Repository | IST PubRep | dl.acm.org | ACM Transactions on Graphics | CNR ExploRA
2018
Contribution to book
Open Access
Computer Vision for Ambient Assisted Living: Monitoring Systems for Personalized Healthcare and Wellness That Are Robust in the Real World and Accepted by Users, Carers, and Society
Colantonio S., Coppini G., Giorgi D., Morales M. A., Pascali M. A.
The Ambient Assisted Living (AAL) paradigm proposes advanced technologies and services to improve the quality of life, health, and wellbeing of citizens by making their daily-life activities easier and more secure, by monitoring patients under specific treatment, and by addressing at-risk subjects with proper counseling. The challenges brought by AAL range from robust, accurate, and nonintrusive data acquisition in dailylife settings to the development of services that are easy to use and appealing to the users and that support long-term engagement. This chapter offers a brief survey of existing vision-based monitoring solutions for personalized healthcare and wellness, and introduces the Wize Mirror, a multisensory platform featuring advanced algorithms fo cardiometabolic risk prevention and quality-of-life improvement.Source: Computer Vision for assistive Healthcare, edited by Marco Leo, Giovanni M. Farinella, pp. 147–182, 2018
DOI: 10.1016/b978-0-12-813445-0.00006-x
Metrics:
Colantonio S., Coppini G., Giorgi D., Morales M. A., Pascali M. A.
The Ambient Assisted Living (AAL) paradigm proposes advanced technologies and services to improve the quality of life, health, and wellbeing of citizens by making their daily-life activities easier and more secure, by monitoring patients under specific treatment, and by addressing at-risk subjects with proper counseling. The challenges brought by AAL range from robust, accurate, and nonintrusive data acquisition in dailylife settings to the development of services that are easy to use and appealing to the users and that support long-term engagement. This chapter offers a brief survey of existing vision-based monitoring solutions for personalized healthcare and wellness, and introduces the Wize Mirror, a multisensory platform featuring advanced algorithms fo cardiometabolic risk prevention and quality-of-life improvement.Source: Computer Vision for assistive Healthcare, edited by Marco Leo, Giovanni M. Farinella, pp. 147–182, 2018
DOI: 10.1016/b978-0-12-813445-0.00006-x
Metrics:
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ISTI Repository | doi.org | www.sciencedirect.com | CNR ExploRA
2017
Journal article
Open Access
Persistent homology to analyse 3D faces and assess body weight gain
Giorgi D., Pascali M. A., Henriquez P., Matuszewski B. J., Colantonio S., Salvetti O.
In this paper, we analyse patterns in face shape variation due to weight gain. We propose the use of persistent homology descriptors to get geometric and topological information about the configuration of anthropometric 3D face landmarks. In this way, evaluating face changes boils down to comparing the descriptors computed on 3D face scans taken at different times. By applying dimensionality reduction techniques to the dissimilarity matrix of descriptors, we get a space in which each face is a point and face shape variations are encoded as trajectories in that space. Our results show that persistent homology is able to identify features which are well related to overweight and may help assessing individual weight trends. The research was carried out in the context of the European project SEMEOTICONS, which developed a multisensory platform which detects and monitors over time facial signs of cardio-metabolic risk.Source: The visual computer 33 (2017): 549–563. doi:10.1007/s00371-016-1344-7
DOI: 10.1007/s00371-016-1344-7
Project(s): SEMEOTICONS
Metrics:
Giorgi D., Pascali M. A., Henriquez P., Matuszewski B. J., Colantonio S., Salvetti O.
In this paper, we analyse patterns in face shape variation due to weight gain. We propose the use of persistent homology descriptors to get geometric and topological information about the configuration of anthropometric 3D face landmarks. In this way, evaluating face changes boils down to comparing the descriptors computed on 3D face scans taken at different times. By applying dimensionality reduction techniques to the dissimilarity matrix of descriptors, we get a space in which each face is a point and face shape variations are encoded as trajectories in that space. Our results show that persistent homology is able to identify features which are well related to overweight and may help assessing individual weight trends. The research was carried out in the context of the European project SEMEOTICONS, which developed a multisensory platform which detects and monitors over time facial signs of cardio-metabolic risk.Source: The visual computer 33 (2017): 549–563. doi:10.1007/s00371-016-1344-7
DOI: 10.1007/s00371-016-1344-7
Project(s): SEMEOTICONS
Metrics:
See at:
Central Lancashire Online Knowledge | ISTI Repository | The Visual Computer | link.springer.com | CNR ExploRA