On the use of quality metrics to characterize structured light-based point cloud acquisitions

Accurately transferring the real world to the virtual one through reverse engineering is of utmost importance in Industry 4.0 applications. Indeed, acquiring good quality 3D representations of existing physical objects or systems has become mainstream to maintain the coherence between a real object and its digital twin. Compared with traditional contact measurement, contact-less scanning is undoubtedly a fast and direct acquisition technology. However, for a given acquisition, finding the right scanning configuration remains a challenging question whose resolution has attracted researchers in recent years. Using heuristics and visibility criteria, some approaches try to automatically plan the positions and path to be followed by a robot when scanning an object being manufactured [1]. Similarly, Joe Eastwood et al. use a genetic algorithm and a convolutional neural network to optimize the locations of the cameras with the purpose that maximize surface coverage and measurement quality [2]. However, all those techniques base their reasoning on theoretical models whose real behavior may diverge as compared to real measuring. Thus, being able to take decisions based on the results obtained from real acquisitions is crucial to minimize the deviations between what was planned and what has been obtained by the end. To do so, ad-hoc metrics need to be used to accurately characterize the quality of point clouds that are then used in the next engineering steps (e.g. reconstruction, control, simulation). The methods for evaluating point cloud (PC) quality can be divided into two types, i.e. subjective and objective. The former mainly evaluates the point cloud from a perceived visual quality for immersive representation of 3D contents [3][4], whereas the latter is more quantitatively based on values. For quantitative metrics for evaluating the quality of PC, some researchers only considered the properties of the PCs, assessing the qualities of the PC from four aspects [5]: noise, density, completeness, and accuracy of the point cloud data. Based on these achievements, some scholars further proposed an indicator for surface accessibility, to characterize how a region on the surface of the workpiece can be reached or not by the scanner. Besides, the coverage rate was proposed to reveal how much the area is scanned. Additionally, the normal angle error was figured out in [4]. However, all those metrics can behave differently depending on the adopted technology: laser scanner, photogrammetry, or structured- light measuring for instance. Catalucci et al. [6] compared the photogrammetry and structedlight measurements on additively manufactured parts and proposed quality indicators of PC that include measurement performance indicators and statistical indicators on the whole part measurement. However, their work focused on whole scans of the part that consist of many point clouds acquired from different scan positions and configurations. Although many criteria have been proposed, it remains to be investigated which are the most accurate and obvious metrics to evaluate the quality of the point cloud during a structured light-based sca