## 1 Introduction  
Three-dimensional digitization of physical objects has become a fundamental component of reverse engineering, a process that facilitates the conversion of physical objects into digital models. The approach of capturing the geometry of objects through discrete data and converting them into continuous CAD models was described as early as the late 1990s. Reverse engineering is the process of analyzing a product or system to understand its design, structure, and functionality. Among other utilizations, a critical component of reverse engineering is the 3D digitization of the surface of objects, which facilitates the acquisition of an accurate three-dimensional model of physical components in the absence of existing technical documentation. Others include the accuracy of the manufacturing and quality control, such as comparing a casting, forging, 3D printed object or workpiece against a nominal model [1], the identification and quantification of the wear of components [2], creating personalized prosthetics [3] and collecting real data for FEM analysis [4]. The utilization of 3D digitizing devices has gained prominence across diverse industrial sectors. The necessity for such systems has arisen in domains where access is particularly challenging, such as the inspection of hard-to-reach places. Conventional endoscopic visual inspections, employed for decades in domains such as turbines, aircraft engines, and pipeline maintenance, offer a 2D image, lacking the capacity to provide the full 3D surface shape data necessary for comprehensive inspection. Consequently, while inspectors may discern fractures or defects, direct measurement of their depth and dimensions from the 2D image remains challenging. The acquisition of three-dimensional information has typically necessitated intricate procedures, often involving the utilization of specialized scanning apparatus equipped with lasers or the disassembly of the equipment itself, followed by scanning of the components from an external location [5]. This research builds on the scientific work of authors [6]. Conventionally used industrial scanners often require the attachment of reference markers or the movement of the scanner around the object to combine the individual scans into a coherent model. However, this is particularly a problem in confined spaces where it is not possible to attach markers or move around the object from all directions. The necessity to avoid using external references has led to research into automatic scan registration algorithms; for example, the Iterative Closest Point (ICP) algorithm has become the standard for merging point clouds without the need for reference features, based on aligning scans to the shortest distance to each other [7]. This technique of merging surface scans requires having common shape regions that are unique and can be registered on the merged scans. However, the ICP itself is functional when the scanning device is moved relative to the object. Successive improvements in 3D point cloud processing have enabled modern handheld scanners to track their motion relative to an object using natural surface features. Examples include simultaneous localization and mapping (SLAM) [8] or surface-based registration [9]. A comprehensive review of 3D scanning methods was published by the authors of [9], who summarized the development of photogrammetry, laser and structured light scanners and compared their accuracy and reliability. They pointed out that although scene scanning is often time consuming, new algorithms and sensors are continuously making the process more efficient. These advances have opened the way for applications of 3D digitization in specialized fields as well. For example, non-contact body scanners have been used in medicine [10; 11], which described several types of scanners used to digitize human body parts for the production of personalized orthoses.