Breast cancer is the leading cause of cancer death in women worldwide, emphasizing the need for prevention and early detection. Mammography screening plays a crucial role in secondary prevention, but large datasets of referred mammograms from hospital databases are hard to access due to privacy concerns, and publicly available datasets are often unreliable and unbalanced. We propose a novel workflow using a statistical generative model based on generative adversarial networks to generate high-resolution synthetic mammograms. Utilizing a unique 2D parametric model of the compressed breast in craniocaudal projection and image-to-image translation techniques, our approach allows full and precise control over breast features and the generation of both normal and tumor cases. Quality assessment was conducted through visual analysis, and statistical analysis using the first five statistical moments. Additionally a questionnaire was administered to 45 medical experts (radiologists and radiology residents). The results showed that the features of the real mammograms were accurately replicated in the synthetic ones, the image statistics overall correspond reasonably well, and the two groups of images were statistically indistinguishable in almost all cases according to the experts. The proposed workflow generates realistic synthetic mammograms with fine-tuned features. Synthetic mammograms are powerful tools that can create new or balance existing datasets, allowing for the training of machine learning and deep learning algorithms. These algorithms can then assist radiologists in tasks like classification and segmentation, improving diagnostic performance.

Digital reconstructions of ancient polychromy on marble and stone surfaces are increasingly used in research, conservation, and heritage communication. Despite their growing adoption, persistent methodological questions remain concerning accuracy, interpretive transparency, and the role of emerging technologies such as artificial intelligence (AI). This study explores professional perspectives through a two-phase expert survey: the first administered in person during the 12th Round Table on Polychromy at the Getty Museum (November 2024), and the second conducted online between May and July 2025. In total, 55 responses were collected from specialists working in archaeology, conservation, digital heritage, and museum practice. Findings reveal a strong consensus on the analytical and communicative value of 3D reconstructions, coupled with growing interest in AI-assisted methods for predictive modeling and hypothesis generation. However, respondents emphasized ethical concerns, the need for domain-specific training, and transparency in distinguishing documented from hypothetical elements. Current tools were criticized for difficulties in simulating pigment behavior and a lack of collaborative features, alongside requests for automated pigment detection, integration of historical data, and user-friendly interfaces. Expectations for realism varied: while some favored strict philological adherence, most preferred high-detail reconstructions with interpretive flexibility. These findings underline the importance of interdisciplinary standards, metadata-rich visualizations, and shared workflows in aligning technological innovation with scholarly rigor and conservation ethics.

In recent years, radiance field methods, and in particular 3D Gaussian Splatting (3DGS), have distinguished themselves in the field of image-based rendering and scene reconstruction techniques, gaining significant success in academia and being cited in numerous research papers. Like other methods, 3DGS requires a large and diverse dataset of images for network training as a fundamental step to ensure effectiveness and high-quality results. Consequently, the acquisition phase is highly time-consuming, especially considering that a portion of the acquired dataset is not actually used for training but is reserved for testing. This is necessary because all commonly used metrics for evaluating the quality of 3D reconstructions, such as PSNR and SSIM, are reference-based metrics; i.e., requiring a ground truth. In this work, we present NOVA, a study focused on no-reference evaluation of 3DGS renders, based on key metrics in this field: PSNR and SSIM.

We propose a method for designing 3D architectural free-form surfaces, represented as grid shells with beams sourced from inventories of reclaimed elements from dismantled buildings. In inventory-constrained design, the reused elements must be paired with elements in the target design. Traditional solutions to this assignment problem often result in cuts and material waste or geometric distortions that affect the surface aesthetics and buildability. Our method for inventory-constrained assisted design blends the traditional assignment problem with differentiable geometry optimization to reduce cut-off waste while preserving the design intent. Additionally, we extend our approach to incorporate strain energy minimization for structural efficiency. We design differentiable losses that account for inventory, geometry, and structural constraints, and streamline them into a complete pipeline, demonstrated through several case studies. Our approach enables the reuse of existing elements for new designs, reducing the need for sourcing new materials and disposing of waste. Consequently, it can serve as an initial step towards mitigating the significant environmental impact of the construction sector.

This paper addresses the design of surface shells as assemblies of tileable, flat 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 that would produce disconnected or weak patterns. We devise a strategy for creating continuous variations on the geometry of individual patterns, in both aesthetics and behavior, to enrich the catalog of available designs. Finally, 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 catalog of patterns with varied aesthetics and predefined mechanical behavior, to use for the direct design of mechanical metamaterials. To assess the feasibility of our design-to-fabricate approach, we show fabricated 3D objects with different curvatures, and compare physical and simulated experiments.

Structure from Motion (SfM) is a computer vision technique used to reconstruct three-dimensional (3D) structures from a series of two-dimensional (2D) images or video frames. However, SfM tools struggle with transparent objects, reflective surfaces, and low-resolution frames. In such situations, image-based interactive 3D modeling software packages are employed to model 3D objects and measure dimensions. Our contributions to this work are twofold. First, we have introduced new tools to improve 3D modeling software packages; such tools are aimed at easing the workload for users. Second, we have conducted a comprehensive user study to evaluate the efficacy of popular 3d modeling software packages. The task is to measure certain dimensions for which ground truth measurements are already known. A relative error is calculated for every measurement. The evaluation of each software tool is done through survey form, event logs, and measurement relative error. The results of this user study clearly show that our approach to 3D modeling using multiple images has a lower relative error and produces higher quality 3D models than other software packages. In addition, it shows our new tools reduce the required time for completing a task.

Large-area image acquisition techniques are essential in underwater investigations: high-resolution 3D image-based reconstructions have improved coral reef monitoring by enabling novel seascape ecological analysis. Artificial intelligence (AI) offers methods for significantly accelerating image data interpretation, such as automatically recognizing, enumerating, and measuring organisms. However, the rapid proliferation of these technological achievements has led to a relative lack of standardization of methods. Remarkably, there are notable differences in procedures for generating human and AI annotations, and there is also a scarcity of publicly available datasets and shared machine-learning models. The lack of standard procedures makes it challenging to compare and reproduce scientific findings. One way to overcome this problem is to make the most used platforms by coral reef scientists interoperable so that the analyses can all be exported into a common format. This paper introduces functionality to promote interoperability between three popular open-source software tools dedicated to the digital study of coral reefs: TagLab, CoralNet, and Viscore. As users of each platform may have different analysis pipelines, we discuss several workflows for managing and processing point and area annotations, improving collaboration among these tools. Our work sets the foundation for a more seamless ecosystem that maintains the established investigation procedures of various laboratories but allows for easier result sharing.

This study aims to demonstrate the potential of 3D analysis in the study of prehistoric ceramic complexes. Due to the production systems used, prehistoric pottery decorations can vary significantly. Examining the frequency of various decorative methods used at different sites may help identify areas potentially connected to specific traditional elements. However, this is usually a cumbersome and time-consuming task. 3D digital methodologies can bring several advantages in the interpretation of ceramic decoration, providing new tools that can improve analytical skills, speed up the decision process, and support the researcher’s conclusions. This work focuses on the application of these methodologies to the study of decorated ceramics from the Neolithic site of Rio Tana (Abruzzo, central Italy): firstly, a pottery complex from this site has been 3D digitised; then, an interactive and web-based 3D visualisation system has been developed to enable access and study of these digital data. By comparing archaeological data with experimental data, in parallel with the analysis of 3D models’ surfaces, it has been possible to better define the technique employed and quickly identify the type of tool used to create the ceramic decorations. Furthermore, the web-based system made it possible to share and disseminate the digital dataset with the community, creating the conditions to make it easier to compare the frequency of different decoration methods and tools used at Rio Tana with other sites.

This paper introduces a novel structural concept for free-form shells, in which the shape is decomposed into flat concrete tiles to be assembled sequentially with the help of falseworks. All tiles can be prefabricated in the shop with an adaptable and reusable molding system. Once the assembly is completed, the tiles are post-tensioned through a network of cables to minimize tension and avoid detachment. The top surface can finally be completed with an in situ cast layer that fills the gaps and activates the entire shell behavior. In contrast, the bottom surface maintains a jagged aesthetics. The paper presents the automatic pipeline supporting the computational design of these shells, from an input shape to its fabrication. The segmentation of the input shape is guided by a field-aligned quad mesh derived from the principal stresses of the thin shell. The tiles are flattened individually and extruded along the normal of the best-fitting plane. In this configuration, only edge midpoints of adjacent tiles share a contact point. Thus, forces can mainly flow along the tiles’ cross directions. The best configuration of cable paths and pre-loads is found by solving a constrained optimization problem exploiting a reduced model of the shell as a network of beams. Six different input shapes are tested to demonstrate the applicability of the proposed design method. The working hypotheses are validated through a higher-resolution nonlinear Finite Element Analysis. The fabrication pipeline is assessed utilizing a reduced-scale 3D-printed replica.

Mutual-capacitive sensing is the most common technology for detecting multi-touch, especially on flat and simple curvature surfaces. Its extension to a more complex shape is still challenging, as a uniform distribution of sensing electrodes is required for consistent touch sensitivity across the surface. To overcome this problem, we propose a method to adapt the sensor layout of common capacitive multi-touch sensors to more complex 3D surfaces, ensuring high-resolution, robust multi-touch detection. The method automatically computes a grid of transmitter and receiver electrodes with as regular distribution as possible over a general 3D shape. It starts with the computation of a proxy geometry by quad meshing used to place the electrodes through the dual-edge graph. It then arranges electrodes on the surface to minimize the number of touch controllers required for capacitive sensing and the number of input/output pins to connect the electrodes with the controllers. We reach these objectives using a new simplification and clustering algorithm for a regular quad-patch layout. The reduced patch layout is used to optimize the routing of all the structures (surface grooves and internal pipes) needed to host all electrodes on the surface and inside the object's volume, considering the geometric constraints of the 3D shape. Finally, we print the 3D object prototype ready to be equipped with the electrodes. We analyze the performance of the proposed quad layout simplification and clustering algorithm using different quad meshing and characterize the signal quality and accuracy of the capacitive touch sensor for different non-planar geometries. The tested prototypes show precise and robust multi-touch detection with good Signal-to-Noise Ratio and spatial accuracy of about 1mm.

This paper introduces NausicaaVR, a novel hardware/software system designed to acquire and render intricate 3D environments, with a particular emphasis on challenging and adverse contexts. In doing so, we navigate the complex landscape of system calibration and rendering, while seamlessly integrating data from multiple sensors. We explore the distinctive challenges inherent in adverse environments, juxtaposing them against conventional automotive scenarios. Through a comprehensive exposition of all constituent elements of the NausicaaVR system, we offer transparent insights into the encountered obstacles and the intricate decisions that were instrumental in surmounting them. This study seeks to illuminate the developmental trajectory of NausicaaVR and analogous systems, thereby furnishing a repository of knowledge and understanding poised to benefit future research and the pragmatic implementation of such cutting-edge technologies.

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.

We introduce a new method for designing reinforcement for grid shells and improving their resistance to out-of-plane forces inducing bending. The central concept is to support the base network of elements with an additional layer of beams placed at a certain distance from the base surface. We exploit two main techniques to design these structures: first, we derive the orientation of the beam network on a given initial surface forming the grid shell to be reinforced; then, we compute the height of the additional layer that maximizes its overall structural performance. Our method includes a new formulation to derive a smooth direction field that orients the quad remeshing and a novel algorithm that iteratively optimizes the height of the additional layer to minimize the structure’s compliance. We couple our optimization strategy with a set of constraints to improve buildability of the network and, simultaneously, preserve the initial surface. We showcase our method on a significant dataset of shapes to demonstrate its applicability to cases where free-form grid shells do not exhibit adequate structural performance due to their geometry.

We introduce context-aware translation, a novel method that combines the benefits of inpainting and image-to-image translation, respecting simultaneously the original input and contextual relevance -- where existing methods fall short. By doing so, our method opens new avenues for the controllable use of AI within artistic creation, from animation to digital art. As an use case, we apply our method to redraw any hand-drawn animated character eyes based on any design specifications - eyes serve as a focal point that captures viewer attention and conveys a range of emotions, however, the labor-intensive nature of traditional animation often leads to compromises in the complexity and consistency of eye design. Furthermore, we remove the need for production data for training and introduce a new character recognition method that surpasses existing work by not requiring fine-tuning to specific productions. This proposed use case could help maintain consistency throughout production and unlock bolder and more detailed design choices without the production cost drawbacks. A user study shows context-aware translation is preferred over existing work 95.16% of the time.

The digital technologies employed in archaeology since the 1990s have progressively and experimentally been utilized over the last two decades to document and re-present the ancient polychromy of Greek and Roman marble artworks. Given that this remains a developing field of investigation and application, this study offers, for the first time, a systematic review of the endeavours undertaken thus far in implementing information technology for the documentation, analysis, reconstruction, visualization, and presentation of ancient polychromy. This overview is supported by a literature review and existing implementations, organized into methods and techniques employed for 3D colour preservation, analysis, and reconstruction, as well as those used for the visualization and dissemination of findings. The goal is to identify gaps and provide intriguing insights for future research concerning the use of digital technologies as an essential tool in the stages of documenting and disseminating ancient polychromy in architecture and archaeological artefacts. This, in turn, aims to encourage data sharing, contribute to the dissemination of science-based knowledge and resolve substantial barriers associated with the long-term retention of digital data. (c) 2024 The Authors. Published by Elsevier Masson SAS on behalf of Consiglio Nazionale delle Ricerche (CNR). This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )