Specifically conceived for applications related to face analytics and tracking, scene segmentation, hand/finger tracking, gaming, augmented reality, and RGB-D cameras are nowadays used even as 3-D scanners. Despite depth cameras' accuracy and precision are not comparable with professional 3-D scanners, they still constitute a promising device for reverse engineering (RE) applications in the close range, due to their low cost. This is particularly true for more recent devices, such as, for instance, the RealSense SR300, which promises to be among the best performing close range depth cameras in the market. Given the potentiality of this new device, and since to date a deep investigation on its performances has not been assessed in scientific literature, the main aim of this paper is to characterize and to provide metrological considerations on the Intel RealSense SR300 depth sensor when this is used as a 3-D scanner. To this end, the device sensor performances are first assessed by applying the existing normative guidelines (i.e. the one published by the Association of German Engineers-Verein Deutscher Ingenieure-VDI/VDE 2634) both to a set of raw captured depth data and to a set acquired with optimized setting of the camera. Then, further assessment of the device performances is carried out by applying some strategies proposed in the literature using optimized sensor setting, to reproduce "real life" conditions for the use as a 3-D scanner. Finally, the performance of the device is critically compared against the performance of latest short-range sensors, thus providing a useful guide, for researchers and practitioners, in an informed choice of the optimal device for their own RE application.

Background and objectiveThe purpose of the present paper is to pave the road to the systematic optimization of complex craniofacial surgical intervention and to validate a design methodology for the virtual surgery and the fabrication of cranium vault custom plates. Recent advances in the field of medical imaging, image processing and additive manufacturing (AM) have led to new insights in several medical applications. The engineered combination of medical actions and 3D processing steps, foster the optimization of the intervention in terms of operative time and number of sessions needed. Complex craniofacial surgical intervention, such as for instance severe hypertelorism accompanied by skull holes, traditionally requires a first surgery to correctly "resize" the patient cranium and a second surgical session to implant a customized 3D printed prosthesis. Between the two surgical interventions, medical imaging needs to be carried out to aid the design the skull plate. Instead, this paper proposes a CAD/AM-based one-in-all design methodology allowing the surgeons to perform, in a single surgical intervention, both skull correction and implantation. MethodsA strategy envisaging a virtual/mock surgery on a CAD/AM model of the patient cranium so as to plan the surgery and to design the final shape of the cranium plaque is proposed. The procedure relies on patient imaging, 3D geometry reconstruction of the defective skull, virtual planning and mock surgery to determine the hypothetical anatomic 3D model and, finally, to skull plate design and 3D printing. ResultsThe methodology has been tested on a complex case study. Results demonstrate the feasibility of the proposed approach and a consistent reduction of time and overall cost of the surgery, not to mention the huge benefits on the patient that is subjected to a single surgical operation. ConclusionsDespite a number of AM-based methodologies have been proposed for designing cranial implants or to correct orbital hypertelorism, to the best of the authors' knowledge, the present work is the first to simultaneously treat osteotomy and titanium cranium plaque.

The current paper presents a system for the dynamic simulation of the human hand. The simulation of the human hand offers the capability to acquire handshapes that correspond to letters of the finger alphabet, enabling an integrated representation of words and sentences. The hand model is designed using the Autodesk Inventor TM and Autodesk AutoCad TM design environments. The user is able to type words or sentences which are dynamically translated into postures according to the finger alphabet. The system is based on the physiometric characteristics of an average human hand. High precision design is utilized in every part through integration of all the necessary functionalities needed to perform the movements required. The system has been tested on more than 500 words with a letter representation success rate in the range of 95-97%.

Low-cost RGB-D cameras are increasingly being used in several research fields, including human–machine interaction, safety, robotics, biomedical engineering and even reverse engineering applications. Among the plethora of commercial devices, the Intel RealSense cameras have proven to be among the most suitable devices, providing a good compromise between cost, ease of use, compactness and precision. Released on the market in January 2018, the new Intel model RealSense D415 has a wide acquisition range (i.e., ~160–10,000 mm) and a narrow field of view to capture objects in rapid motion. Given the unexplored potential of this new device, especially when used as a 3D scanner, the present work aims to characterize and to provide metrological considerations for the RealSense D415. In particular, tests are carried out to assess the device performance in the near range (i.e., 100–1000 mm). Characterization is performed by integrating the guidelines of the existing standard (i.e., the German VDI/VDE 2634 Part 2) with a number of literature-based strategies. Performance analysis is finally compared against the latest close-range sensors, thus providing a useful guidance for researchers and practitioners aiming to use RGB-D cameras in reverse engineering applications.

The optical 3D index has a good match with the average subjective assessment in distinguishing patients with mild to severe PE. This innovative approach offers several advantages over existing indices, as it is repeatable and does not require cross-sectional imaging. The index might be particularly suitable for monitoring the efficacy of nonoperative treatment and, in the future, for designing an optimal personalized usage of therapeutic devices.