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The 3D reconstruction of upper limb anatomy plays a significant role in many biomedical fields such as ergonomics, motion rehabilitation, and prosthesis design. In the last few years, the technical advancement of consumer-grade depth cameras has supported the development of portable and low-cost optical 3D body scanners for healthcare applications. The real-time scanning of human body parts, however, still represents a complex task due to the non-stationary nature of the scanning target. This issue imposes that the scanning time must be reduced as much as possible to minimize scanning artifacts. In this regard, depth cameras can capture geometrical information at video frame rates, thus guaranteeing fast acquisition times. Furthermore, the simultaneous use of multiple sensors would minimize undercut geometries, which impair the 3D reconstruction’s completeness. In this work, a portable 3D optical scanner has been developed by rigidly assembling three Intel® RealSense™ D415 depth cameras on a lightweight circular frame. The three sensors are mutually calibrated, by using a 3D printed calibration specimen, to simultaneously align acquisitions from the three different camera viewpoints for each scanner pose. The system’s effectiveness has been assessed by acquiring the geometry of both a plaster hand and a human hand and comparing the results with those obtained by a high-end stationary structured light scanner. The developed system represents a low-cost handheld alternative to existing body scanners for collecting and storing 3D anatomical data, which can be used in the design process of bespoke medical devices.

In the field of orthodontics, the use of Removable Thermoplastic Appliances (RTAs) to treat moderate malocclusion problems is progressively replacing traditional fixed brackets. Generally, these orthodontic devices are designed on the basis of individual anatomies and customised requirements. However, many elements may affect the effectiveness of a RTA-based therapy: accuracies of anatomical reference models, clinical treatment strategies, shape features and mechanical properties of the appliances. In this paper, a numerical model for customised orthodontic treatments planning is proposed by means of the finite element method. The model integrates individual patient’s teeth, periodontal ligaments, bone tissue with structural and geometrical attributes of the appliances. The anatomical tissues are reconstructed by a multi-modality imaging technique, which combines 3D data obtained by an optical scanner (visible tissues) and a computerised tomography system (internal tissues). The mechanical interactions between anatomical shapes and appliance models are simulated through finite element analyses. The numerical approach allows a dental technician to predict how the RTA attributes affect tooth movements. In this work, treatments considering rotation movements for a maxillary incisor and a maxillary canine have been analysed by using multi-tooth models.

The use of numerical and experimental methods to determine the stress field of mechanical components is well known. In particular, 3D photoelasticity can be considered the only experimental technique for the complete stress state evaluation of 3D components. The advent of rapid prototyping techniques has allowed the manufacturing of complex models in a matter of hours by using birifrangent materials. The present paper is focused on the description of a Computer Aided Engineering (CAE) approach which combines Finite Element (FE) simulations and automatic photoelastic investigations for the stress analysis of face gear drives, made by stereolithography. Computer Aided Design (CAD) geometries, used to manufacture the stereolithographic models, are directly used to perform FE analyses, thus allowing the stress analysis process to become simpler and easier. The substantial agreement observed between experimental and numerical results proved the potentialities of the adopted approach and the usefulness of FE simulations to optimize photoelastic analyses through cost- and time-effective experiments even for complex 3D shapes.

In recent years, various methodologies of shape reconstruction have been proposed with the aim at creating Computer-Aided Design models by digitising physical objects using optical sensors. Generally, the acquisition of 3D geometrical data includes crucial tasks, such as planning scanning strategies and aligning different point clouds by multiple view approaches, which differ for user’s interaction and hardware cost. This paper describes a methodology to automatically measure three-dimensional coordinates of fiducial markers to be used as references to align point clouds obtained by an active stereo vision system based on structured light projection. Intensity-based algorithms and stereo vision principles are combined to detect passive fiducial markers localised in a scene. 3D markers are uniquely recognised on the basis of geometrical similarities. The correlation between fiducial markers and point clouds allows the digital creation of complete object surfaces. The technology has been validated by experimental tests based on nominal benchmarks and reconstructions of target objects with complex shapes.