Abstract 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.

Abstract 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.

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.

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 of-the-shelf materials and technologies. We successfully tested our method on a set of challenging examples with complex shapes and rich geometric detail.

A person's well-being status is reflected by their face through a combination of facial expressions and physical signs. The SEMEOTICONS project translates the semeiotic code of the human face into measurements and computational descriptors that are automatically extracted from images, videos, and three-dimensional scans of the face. SEMEOTICONS developed a multisensory platform in the form of a smart mirror to identify signs related to cardio-metabolic risk. The aim was to enable users to self-monitor their well-being status over time and guide them to improve their lifestyle. Significant scientific and technological challenges have been addressed to build the multisensory mirror, from touchless data acquisition, to real-time processing and integration of multimodal data.

In the recent years personal health monitoring systems have been gaining popularity, both as a result of the pull from the general population, keen to improve well-being and early detection of possibly serious health conditions and the push from the industry eager to translate the current significant progress in computer vision and machine learning into commercial products. One of such systems is the Wize Mirror, built as a result of the FP7 funded SEMEOTICONS (SEMEiotic Oriented Technology for Individuals CardiOmetabolic risk self-assessmeNt and Self-monitoring) project. The project aims to translate the semeiotic code of the human face into computational descriptors and measures, automatically extracted from videos, multispectral images, and 3D scans of the face. The multisensory platform, being developed as the result of that project, in the form of a smart mirror, looks for signs related to cardio-metabolic risks. The goal is to enable users to self-monitor their well-being status over time and improve their life-style via tailored user guidance. This paper is focused on the description of the part of that system, utilising computer vision and machine learning techniques to perform 3D morphological analysis of the face and recognition of psycho-somatic status both linked with cardio-metabolic risks. The paper describes the concepts, methods and the developed implementations as well as reports on the results obtained on both real and synthetic datasets.

The face reveals the healthy status of an individual, through a combination of physical signs and facial expressions. The project SEMEOTICONS is translating the semeiotic code of the human face into computational descriptors and measures, automatically extracted from videos, images, and 3D scans of the face. SEMEOTICONS is developing a multisensory platform, in the form of a smart mirror, looking for signs related to cardio-metabolic risk. The goal is to enable users to self-monitor their well-being status over time and improve their life-style via tailored user guidance. Building the multisensory mirror requires addressing significant scientific and technological challenges, from touch-less data acquisition, to real-time processing and integration of multimodal data.

Abstract In this paper we target the problem of textured 3D object retrieval. As a first contribution, we show how to include photometric information in the persistence homology setting, also proposing a novel theoretical result about multidimensional persistence spaces. As a second contribution, we introduce a generalization of the integral geodesic distance which fuses shape and color properties. Finally, we adopt a purely geometric description based on the selection of geometric functions that are mutually independent. The photometric, hybrid and geometric descriptions are combined into a signature, whose performance is tested on a benchmark dataset.

In this work we evaluate purely structural graph measures for 3D objects classification. We extract spectral features from different Reeb graph representations. Information-theoretic feature selection gives an insight on which are the most relevant features.

We present the results of a project aimed to evaluate 2D and 3D face recognition algorithms. In particular, we focused on the potentialities of 3D-based techniques to overcome typical limitations of 2D methods in non-controlled situations. According to the reference scenario of people identification at airport check points, we built a representative database on which we tested different face recognition algorithms. We implemented and tested an improved version of a well-known state-of-the-art 3D approach, and verified that on our dataset it performs better than a widely used commercial system.

This track focuses on 3D shape classification, i.e. the assignment of a query object to one of the classes in a database. A total amount of 646 watertight models have been classified, and released as training, testing and query data. Three different levels of categorization have been taken into account, from coarse to fine.

We propose an algorithm to convert a tetrahedral mesh with singularities to a combinatorial 3-manifold using only local modifications. We outline sufficient conditions on the mesh to guarantee the feasibility of the approach and we show how singularities can be both identified and removed according to the configuration of their link. Furthermore, we demonstrate that the algorithm can be implemented using a flexible state-of-the-art data structure for manifold tetrahedral meshes suitable for efficient and general applications.