Reverse Engineering (RE) is a long-term goal of engineering and computer science; it aims at the reconstruction of CAD models from measured data by means of 3D mathematical surfaces and geometrical features representing the geometry of a physical part. In the last two decades, reviews and surveys have occasionally covered this topic, but a systematic dissertation of modeling methods from a mechanical engineering point of view is still missing. The purpose of this paper is to fill this gap; starting from a general description of the overall RE framework (acquisition, segmentation, classification, fitting), both an up-to-date survey and a categorization of available modeling techniques and tools working on 3D data are provided. The main aspects of various strategies are discussed as well, in order to highlight strengths and weaknesses characterizing different approaches. Moreover, an overview of commercial software for RE is presented, considering both dedicated solutions and packages supplied as add-on with ‘traditional’ CAD systems. Finally, possible improvements to be addressed by the research in the RE field are discussed, outlining potential future trends that are still to be investigated.

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.

Wheel alignment, consisting of properly checking the wheel characteristic angles against vehicle manufacturers' specifications, is a crucial task in the automotive field since it prevents irregular tyre wear and affects vehicle handling and safety. In recent years, systems based on Machine Vision have been widely studied in order to automatically detect wheels' characteristic angles. In order to overcome the limitations of existing methodologies, due to measurement equipment being mounted onto the wheels, the present work deals with design and assessment of a 3D machine vision-based system for the contactless reconstruction of vehicle wheel geometry, with particular reference to characteristic planes. Such planes, properly referred to as a global coordinate system, are used for determining wheel angles. The effectiveness of the proposed method was tested against a set of measurements carried out using a commercial 3D scanner; the absolute average error in measuring toe and camber angles with the machine vision system resulted in full compatibility with the expected accuracy of wheel alignment systems.