## 1 Introduction  
Additive manufacturing (AM) currently makes it possible to manufacture highly accurate and optimized components. However, there is still not enough information on the quality of such parts. The comparison of certain parameters of the surface texture, geometry, and internal porous structure of additive products requires the use of computerized measurement technologies with a high resolution and accuracy. This issue has been investigated by several authors. [PERSON] et al. [1] dealt with the minimization of surface texture functional parameter selection. For this purpose, they determined a linear correlation between the parameters. Four surfaces of IX15 steel products were used as samples after grinding,round grinding, superfinishing, and polishing. It was found that in 16-18 cases out of 26, there was a significant correlation, with a Pearson coefficient of more than 0.8. Random surface topography modeling methods were summarized in the paper of [PERSON] et al. [2]. Methods for the generation of surface topographies with a Gaussian and non-Gaussian height probability distribution were presented. Particular attention was paid to the modeling of two-process random surfaces. The selection of a suitable method for the random modeling of different types of surface topographies to obtain the dependence of the surface characteristics, production process, and surface function was discussed by [PERSON] et al. [3]. In their conclusions, the authors stated that further research needs to focus on the development of a generalized feature-based technology tool-kit (an interdisciplinary subject, involving computer vision, image processing, and geometric modeling) with robustness for universal application, including fast algorithms, soft gauges, and uncertainty, to guarantee computational fidelity. [PERSON] et al. [4] characterized, in detail, the X-ray computed tomography method, which enables complex geometry assessment, and they researched the unique properties of additive manufacturing components' surfaces. An improved understanding of the impacts of these features will assist in efforts to predict the performance of additive manufacturing parts, e.g., fatigue, heat exchange, and osseointegration. The use of an in situ sensor and cameras to obtain process data for the real-time monitoring and quality analysis of 3D-printed parts was discussed in detail by [PERSON] et al. [5]. They presented a review of quality control in seven different techniques in AM technology and provided detailed discussions of each quality process stage. Their research was limited to the review of only seven techniques of AM technology, which included photopolymer vat processes, material jetting processes, binder jetting processes, extrusion-based processes, powder bed fusion processes, direct energy deposition processes, and sheet lamination processes. [PERSON] et al. [6] presented a holistic approach to help a designer in developing and manufacturing a product for additive manufacturing. The methodology itself was discussed and explained based on a real sample metal part. The general methodology was discussed on the basis of the space industry. By using computer measurement technologies with a high resolution and accuracy, images of the unique surface textures of irregular surfaces, accurate images of the complex geometry, and the internal structural variability of various materials can be obtained. Several authors should be mentioned in this area. [PERSON] [7], in his dissertation, dealt with the verification of scanning systems and the determination of conformance to specifications in order to achieve the best performance--usually corresponding to the manufacturer's specifications. Various methods and reference objects were developed in this project to establish the metrological traceability of the measurements. Moreover, investigations as well as international comparisons in the application of the two different areas were carried out. [PERSON] et al.