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.

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.

RGB-D cameras are employed in several research fields and application scenarios. Choosing the most appropriate sensor has been made more difficult by the increasing offer of available products. Due to the novelty of RGB-D technologies, there was a lack of tools to measure and compare performances of this type of sensor from a metrological perspective. The recent ISO 10360-13:2021 represents the most advanced international standard regulating metrological characterization of coordinate measuring systems. Part 13, specifically, considers 3D optical sensors. This paper applies the methodology of ISO 10360-13 for the characterization and comparison of three RGB-D cameras produced by Intel^® RealSense™ (D415, D455, L515) in the close range (100–1500 mm). ISO 10360-13 procedures, which focus on metrological performances, are integrated with additional tests to evaluate systematic errors (acquisition of flat objects, 3D reconstruction of objects). The present paper proposes an off-the-shelf comparison which considers the performance of the sensors throughout their acquisition volume. Results have exposed the strengths and weaknesses of each device. The D415 device showed better reconstruction quality on tests strictly related to the short range. The L515 device performed better on systematic depth errors; finally, the D455 device achieved better results on tests related to the standard.

Template-Based reverse engineering approaches represent a relatively poorly explored strategy in the field of CAD reconstruction from polygonal models. Inspired by recent works suggesting the possibility/opportunity of exploiting a parametric description (i.e. CAD template) of the object to be reconstructed in order to retrieve a meaningful digital representation, a novel reverse engineering approach for the reconstruction of CAD models starting from 3D mesh data is proposed. The reconstruction process is performed relying on a CAD template, whose feature tree and geometric constraints are defined according to the a priori information on the physical object. The CAD template is fitted upon the mesh data, optimizing its dimensional parameters and positioning/orientation by means of a particle swarm optimization algorithm. As a result, a parametric CAD model that perfectly fulfils the imposed geometric relations is produced and a feature tree, defining an associative modelling history, is available to the reverse engineer. The proposed implementation exploits a cooperation between a CAD software package (Siemens NX) and a numerical software environment (MATLAB). Five reconstruction tests, covering both synthetic and real-scanned mesh data, are presented and discussed in the manuscript; the results are finally compared with models generated by state of the art reverse engineering software and key aspects to be addressed in future work are hinted at. Highlights A novel CAD reconstruction method fitting a CAD template model to mesh data. A feature-based parametric-associative modelling history is retrieved. Fitting process is controlled by a Particle Swarm Optimization algorithm. Accuracy of reconstructed models is comparable/better than state of the art results. Computational costs and required time are at the moment considerable.