3D scanning

Analysing the particle size distribution (PSD) of nanomaterials is essential for many scientific and industrial applications, as PSD offers important details about the distribution, average size, and range of particles within a sample. All these properties have a huge impact on the characteristics and behaviour of the materials. In this work we investigated image-based PSD recognition of nanoparticles from synthetic SEM images. For the task we considered an image classification pipeline based on five conventional (hand-crafted) texture descriptors and five pre-trained convolutional neural networks (CNN). Fusion strategies for combining the best performing feature sets were also investigated. We found that both the conventional and the CNN-based features achieved good performance (best accuracy respectively 87.6% and 88.2%), whereas the optimal performance (90.2%) was obtained by combining the two best performing CNN-based feature sets. Altogether the results are promising and demonstrate the feasibility of the method.

Keywords: Deep Learning , Image processing , Nanomaterials , Scanning electron microscopy , Texture

A Roman village was recently found in Ono San Pietro, in the Mid Valle Camonica (Italy), including the rests of four rustic buildings with basement room. In one of these buildings two iron objects were found, attributable to the metallic parts of a wagon wheel. In particular, they were identified as a bushing mounted inside the wheel hub to prevent wear, and a rim mounted on the external diameter of the hub to prevent radial cracking. First, these finding were digitalized by laser-scanning, to study and measure them in detail without handling. The study of the virtual findings allowed hypothesizing their role, the concept behind their design, as well as the possible techniques used for their manufacturing. Based on the size of the findings, a reconstruction of the wooden wheel they could be mounted on was made. By means of structural analyses, the load capacity of the axle was determined, and from this a possible configuration of the wagon assembly was derived, also considering the orographic conformation of the site. By means of this reverse engineering procedure, a possible scenario of the freight transport in Mid Valle Camonica during the Roman Age was obtained.

Keywords: Laser Scanning , Reverse Engineering , Roman Age , Wagon Wheel

Additive Manufacturing (AM) displays unique capabilities in terms of fabricating parts including unconventional geometries. Much research is conducted to investigate the performances of AM from both the mechanical and the quality viewpoint. This research is aimed to complete the transition of AM from a rapid prototyping technology to a mature manufacturing option. Interestingly, this research is routinely conducted on standard geometries instead of those kind of topologies for which AM is an advantageous fabrication system. The study presented here originally compares the accuracy of AM for standard and complex shapes. As a case study, we used the model of a leg of a quadruped robot obtained with generative design in previous research. The model includes standard and non-standard geometries, with the latter being predominant. Ten copies of this model have been produced by AM, specifically by means of a 3D printer working with Fused Deposition Modelling. Two different orientations have been used for the model (five copies each). The 3D-printed physical models have been 3D-scanned and compared with the reference CAD model, which has allowed us to evidence deviations. The deviations have been displayed by using both global and local best-fit options in a quality assessment software application. The first analysis of achieved data suggests that printing parameters and the position of surfaces affect the accuracy of AM more than the characterization of surfaces as standard and non-standard.

Keywords: Additive Manufacturing , Deviations , freeform , Generative Design

Piezoelectric material extrusion is a novel material extrusion technique that deposits thermoplastic materials in droplets, by means of a piezoelectric nozzle regulating the material flow. This improves the performance of the machine, with respect to Fused Filament Fabrication, by enabling higher precision in material placement and better control over deposition volume. A common limitation of this technology lies in the fabrication of overhanging structures, where precise droplet deposition is inherently more complex and prone to inaccuracy. The research investigates the impact of a key process parameter, specifically Droplet Aspect Ratio, on the dimensional accuracy of printed specimens and on the quality of overhang surfaces. The research considers bridging and the prioritization of infills in the deposition strategy as additional variables. An experiment based on the fabrication of 50 specimens, optimized for overhang quality evaluation, was performed. Specimens were analysed through 3D scanning techniques. The study demonstrates a strong correlation between Droplet Aspect Ratio and the dimensional accuracy of overhanging surfaces. The integration of bridging structures significantly improved surface quality by providing additional support, with the greatest benefits observed at 27° inclination. Prioritizing infill deposition before outer contours resulted in a marked reduction in defect levels at high DAR values.

Keywords: additive manufacturing , droplet aspect ratio , overhang , pellet-based material extrusion , piezoelectric nozzle

Mandibular and maxillary bone regeneration is a significant challenge in orthodontics and maxillofacial surgery, particularly in patients with bone atrophy or advanced periodontal diseases. This study presents an innovative approach that integrates advanced imaging and manufacturing technologies to design and fabricate customized scaffolds based on data obtained through Cone Beam Computed Tomography (CBCT). The objective is to create biomimetic bone scaffolds suitable for infusion with autologous stem cells, promoting functional bone regeneration. The workflow comprises three key stages. CBCT images are processed to reconstruct detailed three-dimensional models of the mandibular or maxillary structure. Then, using generative optimization algorithms and surface subroutines in commercial CAD software, scaffolds are designed to replicate the mechanical, geometric, and biological properties of natural bone. The scaffolds are fabricated using 3D printing techniques with biocompatible and biodegradable materials. 3D printing enables high precision in creating porous structures optimized for vascularization, cellular integration, and mechanical support. This multidisciplinary approach, combining medical imaging, CAD/CAM design, and additive manufacturing, offers an advanced solution to address the challenges of bone regeneration in orthodontics. Preliminary results suggest that infusing scaffolds with stem cells could significantly accelerate bone regeneration and improve integration with surrounding tissues. Future studies will focus on preclinical validation of scaffolds.

Keywords: Biocompatible material , Biodegradable material , Bone scaffold , DICOM images , Mandibular and Maxillary Reconstruction

Lost-wax casting is an ancient technique of great precision, used for the creation of bronze artefacts. The artist makes the model in clay or plasticine and then creates the plaster or silicone mold. Inside the mould, wax is brushed to the ideal thickness of 5 to 6 mm and then wax “straws” were added at appropriate positions to allow optimal filling of the mould during bronze casting, starting with the lowest volumes. Finally, the wax model is incorporated into a refractory mass that surrounds it internally and externally and forms the cavity into which the bronze is poured. In this research, the results were obtained by analysing an innovative digital technique that avoids the creation of the mould and shrinkage during the intermediate stages of bronze production. In fact, using a 3D scanner and the latest rapid prototyping techniques, an accurate reproduction of the original clay model was recreated. The moulded model replaces the wax model. It is also enclosed between two forms of refractory material and, due to its similar melting temperature and flow properties, minimises build time and improves accuracy. The studied methodology also allows a more precise assignment and greater control of all surface thicknesses through a discretised digital finite element (FE) model analysis.

Keywords: Bronze sculptures' reconstruction , Laser scanning technology , Lost-wax bronze casting , Mold withdrawal , Rapid prototyping

Accurate three-dimensional (3D) reconstruction of individual dental elements from cone-beam computed tomography (CBCT) data is critical for adaptive prosthetic design and occlusal simulation in digital dentistry. However, challenges persist due to the complex morphology of teeth, the similar radiodensities of hard tissues, and the limited resolution of CBCT in capturing interproximal surfaces between adjacent teeth. This study presents a semi-automated, open-source compatible workflow that integrates CBCT image segmentation with the reconstruction of missing interproximal molar regions using Non-Uniform Rational B-Spline (NURBS) surface modeling. Molar datasets from CBCT acquisitions were segmented into 3D Slicer open-source software and refined to isolate individual teeth. Missing surface regions were reconstructed using third-degree NURBS patches in Rhinoceros® ver. 8 commercial software. Method reconstruction accuracy was quantitatively evaluated on 14 M by applying the method to manually created voids on intact tooth models. The average root-mean-square error (RMSE) and Hausdorff distance were 0.041 ± 0.015 mm and 0.049 ± 0.023 mm, respectively. The resulting models exhibited high surface continuity and anatomical fidelity, making them suitable for Computer Aided Engineering (CAE) analysis and prosthetic customization. This approach enables reproducible, high-precision dental modeling and represents a promising step toward fully automated 3D reconstruction workflows in clinical practice.

Keywords: CBCT segmentation , Digital prosthodontics , Molar morphology subdivision , NURBS modeling

Point clouds have become a widely used data format in computer vision, driven by the increasing availability of 3D scanning devices in applications such as robotics and autonomous driving. However, challenges in data acquisition, such as occlusion, reflection, and limited sensor resolution, often result in incomplete scans. Consequently, point cloud completion, the task of reconstructing the complete geometry of a point cloud object from partial data, has emerged as a key area of research. Numerous deep learning-based methods have been proposed to tackle this problem. Although some surveys on shape completion exist, the rapid growth of research in recent years calls for an updated review. In this work, we present a comprehensive survey of recent advancements in deep neural networks for point cloud completion, covering literature from 2024 up to December 2025. After establishing a reference context by analyzing the major foundational methods up to the year 2023, we review the most recent methods with an analytical perspective based on how they have improved each specific component of the network. Furthermore, we present a comparative analysis of the performance of the reviewed methods on the main benchmark datasets, and finally, we discuss open challenges, offering insights and suggestions for future research directions.

Keywords: 3D Object Reconstruction , Computer Vision , Deep Learning , Point Cloud Completion , Review , Shape Completion

Capturing accurate texture maps from physical materials remains a challenge in digital prototyping and projection-based spatial augmented reality (P-SAR). This paper presents an open-source material scanning system based on photometric stereo, designed for affordability, simplicity, and efficient operation. The system combines a consumer-grade digital camera, multifaceted reflector (MR16) LED lighting, and Arduino-controlled automation to acquire material data up to A4 size within 15 s. Accurate colour reproduction is achieved through a hybrid calibration workflow that integrates camera profiling with a 3D lookup table. The resulting images are processed in a streamlined Substance 3D Designer pipeline to generate albedo and normal maps compatible with physically based rendering (PBR). To evaluate performance under realistic conditions, two fabric samples were scanned and qualitatively compared with professionally digitised references. Albedo maps were assessed based on dominant colour accuracy using CIEDE2000 (ΔE00), while normal maps were evaluated through visual rendering comparisons and directional distribution analysis. Scanning and processing times were also measured to verify workflow efficiency. Results demonstrate that the proposed system produces perceptually consistent textures suitable for real-time rendering applications while offering a low-cost and customisable solution for material digitisation.

Keywords: Custom scanner , Material digitisation , PBR textures , Photometric stereo

This study presents a genetic algorithm-based methodology for reconstructing the nominal profile of airfoils belonging to the NACA four- and five-digit series. By minimizing the geometric deviations between measured point clouds and parametrically generated airfoil profiles, the algorithm identifies the best-fitting nominal geometry. The approach was implemented using Rhino 8, Grasshopper, and the Galapagos plugin, and validated through extensive testing on 3D-printed samples. Across 200 test runs, the algorithm consistently identified the correct nominal geometry, demonstrating robustness despite inherent stochastic variability and computational challenges. The average number of iterations needed to converge was found to be 953 across all cases. This methodology offers a valuable tool for reverse engineering and metrological applications, providing a parametric and efficient alternative to traditional free-form surface reconstruction.

Keywords: Airfoil Design , Airfoil inspection , Genetic Algorithm , Reverse Engineering

Point cloud registration is a central theme in computer vision, with alignment algorithms continuously improving for greater robustness. Commonly used methods evaluate Euclidean distances between point clouds and minimize an objective function, such as Root Mean Square Error (RMSE). However, these approaches are most effective when the point clouds are well-prealigned and issues such as differences in density, noise, holes, and limited overlap can compromise the results. Traditional methods, such as Iterative Closest Point (ICP), require choosing one point cloud as fixed, since Euclidean distances lack commutativity. When only one point cloud has issues, adjustments can be made, but in real scenarios, both point clouds may be affected, often necessitating preprocessing. The authors introduce a novel differential entropy-based metric, designed to serve as the objective function within an optimization framework for fine rigid pairwise 3D point cloud registration, denoted as Iterative Differential Entropy Minimization (IDEM). This metric does not depend on the choice of a fixed point cloud and, during transformations, reveals a clear minimum corresponding to the best alignment. Multiple case studies are conducted, and the results are compared with those obtained using RMSE, Chamfer distance, and Hausdorff distance. The proposed metric proves effective even with density differences, noise, holes, and partial overlap, where RMSE does not always yield optimal alignment.

Keywords: density , entropy , holes , iterative closest point (ICP) , noise , partial overlap , Point cloud , registration , root mean square error (RMSE)

Accurate identification of spinal landmarks is critical in postural assessment, spinal deformity monitoring, and biomechanical modeling. Conventional techniques, such as radiographic imaging and manual palpation, suffer from significant limitations, including radiation exposure and operator dependence. This study presents a novel, fully automated method for localizing spinal apophyses using high-resolution 3D surface scans and shape index analysis. The proposed approach extracts the dorsal symmetry line and analyzes perturbations in the shape index, a differential geometric descriptor computed from local principal curvatures, to infer spinous process locations. Experimental validation was conducted on 21 healthy subjects using a structured-light 3D optical scanner, with palpation-based identification serving as a clinical reference. The method demonstrated high sensitivity, with a false negative rate of only 1.58% relative to operator-identified landmarks. The approach also identified additional plausible landmarks not detected by the operator, suggesting potential for improved diagnostic accuracy. These results support the method's suitability for objective, repeatable spinal landmark detection, with implications for both clinical assessment and computational modeling. Future work will focus on validation against radiographic ground-truth and applicability in pathological cases.

Keywords: 3D Surface Topography , Automated Landmark Detection , Shape Index Analysis , Spinous Process Detection

This study aims to gather a sustainable understanding of additive manufacturing and other Manufacturing 4.0 approaches like horizontal and vertical integration and cloud computing techniques with a focus on industrial applications. The DfAM will apply 4.0 tools to gather product feasibility and execution, with CAE—FEM analysis and CAM. This publication focuses on the redesign of a vehicle suspension arm. The main objective is to apply innovative design techniques that optimize component performance while minimizing cost and time. The IDeS method and TRIZ methodology were used, resulting in a composite element, aiming to make the FDM-sourced process a viable option, with a weight reduction of more than 80%, with less material consumption and, hence, less vehicle energy consumption. The part obtained is holistically sustainable as it was obtained by reducing the overall labor used and material/scrap generated, and the IDES data sharing minimized rework and optimized the overall production time.

Keywords: additive manufacturing , IDeS , product design , product reverse engineering , TRIZ

3D scanners are professional, structured-light systems that capture geometry with sub-millimeter accuracy, enabling the creation of virtual garment digital twins. However, these high-resolution scans produce dense and heavy meshes that overwhelm performance and storage capacities in immersive environments. This study presents a scalable workflow to generate lightweight yet realistic 3D garment models for Metaverse applications. The novelty lies in the proposal and validation, via 20 padded garment case study, of a process that uses Artec Studio 19 for scan consolidation, Blender for retopology and texture baking, and Unity Engine 3D for deployment, while examining acquisition limitations (e.g., occluded folds, reflective materials) and quantitatively characterizing the trade-off between visual fidelity and performance. Starting from meshes of approximately 13 million triangles, we apply a 10% decimation followed by quad-based remeshing to achieve ∼ 30,000 quads and produce FBX assets of only 18–25 MB without perceptible quality loss. Side-by-side renderings in Blender and Unity confirm indistinguishability, and these asset sizes satisfy the 40 MB guideline for a dedicated Metaverse platform, supporting under 4 s of Web-AR load times. Best-practice guidelines detailing acquisition parameters, post-processing workflows, and optimization settings provide a reproducible framework for creating digital fashion models in Metaverse applications.

Keywords: 3D Scanning , Fashion industry , Mesh optimization , Metaverse , Reverse engineering

This paper introduces a Unity-based Virtual Reality (VR) system designed for real-time, incremental acquisition, processing, and interactive spatial filtering of LiDAR-generated point clouds on the Meta Quest 3 platform. Using an Intel RealSense L515 sensor and SLAM-inspired alignment techniques, the system enables dynamic, user-defined spatial segmentation through bounding boxes to refine collected data. The proposed framework supports multi-pass acquisitions, merging, and cleaning of point clouds, effectively addressing the limitations of traditional batch processing workflows. Additionally, the system provides an efficient modular structure that integrates live visualization using the Pcx Unity asset and optimizes performance through binary PLY file conversion. A case study on capturing a Meta Quest 3 protective case demonstrates the system's robustness and ability to preserve geometric fidelity across incremental scans, with minimal redundancy and automatic alignment. The results highlight the potential for future expansion into real-time volumetric reconstruction, automated point cloud registration, and advanced spatial computing applications. By combining modular software architecture with accessible hardware configurations, this work contributes a practical framework for immersive 3D data handling, aligning with ongoing research in real-time 3D reconstruction, interactive VR applications, and advanced point cloud processing.

Keywords: 3D Scanning , Point Cloud Processing , Virtual Prototyping , Virtual Reality

This paper discusses the development of two methods for reconstructing 3D models of painted or photographed scenes and subjects, particularly for the benefit of visually impaired individuals. In detail, two methods are discussed and applied to a set of case studies. The first method utilizes deterministic approaches paired with spatial scene reconstruction, to produce a tactile 2.5D model, or bas-relief representation, of the originally 2D scene. The second method implements artificial intelligence (AI) to create a 3D mesh from a 2D input of a painted subject. Both methods were tested against a set of case studies, including the reconstruction of subjects from paintings and photo printings. Results shows that the deterministic method is effective in reconstructing both subjects from paintings and even the entire depicted scene. However, the retrieved model is not fully 3D. Conversely, the AI-based approach produces rather accurate fully 3D models in a reasonable amount of time, but the models that are retrieved lack detail in a number of features, including the subjects' face and clothing.

Keywords: 2.5D , 3D Acquisition , 3D reconstruction , AI method , visually impaired people

In the field of boating, the design of eco-sustainable solutions has been recently focused on hydrofoils. Such technology is employed in small and large vessels. A great challenge may be represented by the reconstruction of geometric details in objects with large sizes through traditional approaches based on simple and manual instrumentations and measuring systems. With regard to reverse engineering techniques, photogrammetry may be suitable for reconstructing devices with large sizes. The aim of the work was to report and provide an analysis on the last progresses related to the feasibility of the photogrammetric approach in the hydrofoil research, summarising a specific methodology reported in the literature to acquire and reconstruct the outer sector of the front wing of a Kolkhida-class hydrofoil. The potential of computational fluid dynamics was also stressed in the evaluation of the characteristic curves, focusing on drag and lift coefficients as well as on the error due to the reconstruction through photogrammetry. Data analysis and research generally require the possibility of sifting through a great amount of information, thus providing a very overwhelming and time-consuming approach. In this scenario, the focused summarising approach may play an important role in providing a simplification of the complex content and technical features into digestible insights. This means that the proposed summarising approach should be able to boost retention providing a help in remembering key features more effectively through their condensation to a reduced number of information and steps.

Keywords: computational fluid dynamics , Design methods , hydrofoil , measurements , reverse engineering

The work is part of the research activities performed within the framework of the “Nemesi- New Engineering & Manufacturing Enhanced System Innovation” research and development program. The aim of the research is to evaluate the accuracy and measurement uncertainty characterizing the entire process of the digitized acquisition, scanning and measurement of surface defects on aluminum aircraft fuselage. Additionally, it aims to determine the combinations of the main process parameters that make it less sensitive, and therefore robust, to the effect of disturbing factors, such as technological imperfection and environmental variations. Special attention was put on the study of two specific types of aircraft surface defects: scratches and depressions (dents). For scratches, three different morphological configurations were studied: linear, crossed, and curved scratches. Reference depth measurement of the defects was obtained by using a digital depth micrometer (comparator), an instrument currently certified in the aeronautical field. These values were compared with the outputs measured using the 3D scanner system Atos Q – Zeiss and the related analysis software Gom Inspect Pro. The analysis of the results showed very accurate and reliable defect depth values when compared with the reference depth values obtained from the certified digital micrometer/comparator. Future developments of this research will focus on expanding the range of the analysed defect types, including further surface anomalies and complexities, as well as several materials. With regards to future trends, the integration of machine learning algorithms to enhance the predictive capabilities of defect detection and measurement processes will also be explored.

Keywords: Design of Experiments , Non-contact Reverse Engineering systems , Non-destructive inspection , Quality control , Structured light systems , Surface defects

Perishable materials are strongly affected by climate changes that can seriously damage them. In this study, ancient wooden furniture inside the UNESCO site of the Reggia di Caserta has been surveyed to provide 3D volumes for structural analysis. Wooden objects are mostly affected by moisture and changing in the temperature during the year, most likely in a building that faces substantial changes between winter and summer. The process comprehended the combined use of photogrammetry and hand-held device to obtain accurate models. With this surveying pipeline, it was possible to scale the photogrammetric models and complete the missing parts. The process of the creation of volumetric models with the use of voxels have been analyzed to identify the problems and set a methodology for the use of these volumes in FEA software. The aim is to use the models for diagnostic studies for understanding the level of decay integrated with in situ thermographic surveys.

Keywords: 3D survey , FEM , Integration of data , Laser scanning , Photogrammetry , Structural analysis

This study investigates the development of workwear designed to withstand harsh conditions and support physically demanding tasks. Its central aim is to create garments that enhance workers' comfort and mobility by optimizing ergonomic and anthropometric factors. First of all, expert surveys were collected, and the importance of posture adaptability and material comfort was highlighted. To investigate realistic body–garment interactions, the 3D body scans of the upper body from 34 participants in common working poses were captured. These scans revealed the zones of high deformation, guiding the placement of elastic inserts to improve flexibility in targeted areas. The redesigned garments underwent a two-stage evaluation process. First, Clo3D virtual fittings provided qualitative insights into overall jacket fit and movement behavior. Next, stress and strain mapping offered quantitative validation, showing that fabric stress levels remained below 120 kPa, providing evidence that the added elasticity effectively reduced mechanical load and improved wearability. Expert reviewers confirmed the enhanced fit and functional performance. Overall, the study demonstrates an integrated design strategy that unites textile behavior, body dimensions and biomechanics. This approach not only improves workwear but also offers a transferable framework for developing specialized clothing across other physically intensive professions.

Keywords: anthropometry , strain map , stress map , virtual fitting , workwear optimization