# Influence of Material on the Density of a Point Cloud Created Using a Structured-Light 3D Scanner
## 1 Introduction  
Current research in the field of creating 3D models of real objects is moving in the direction of obtaining very high-density and voluminous point clouds, either by laser 3D scanning or structured-light 3D scanning [1]. Although these methods provide relatively high-density data in the form of point clouds, it is very time- and cost-intensive to process these data for the creation of digital models [2]. The current need to quickly provide as many 3D models [3] of historic buildings, infrastructure structures [4; 5], etc., as possible, especially for maintenance, repair, and passporting purposes [6; 7; 8], requires finding scanning and data processing methods that are faster and more cost-effective. One such method is digital photogrammetry, during which images of the object's surface are taken, and a digital model is then created from them using software. [PERSON] et al. [9], in their study of historical building data acquisition, show that for higher density point clouds, it is better to use a structured light 3D scanner than laser 3D scanning. This is valid in the case of historic buildings and their significant elements, decorations, reliefs, etc. [9]. At the same time, it is proven that the purchase of a quality camera, a drone with a high-resolution camera, and image processing software is significantly cheaper than the purchase of a laser 3D scanner with accessories [10]. It is obvious that when the amount of data contained in a point cloud is limited, distortions and inaccuracies occur in the creation of a 3D model [10]. However, in many cases, the mere geometry of the shapes of the scanned object will be sufficient, and we will not need to acquire data about the surface texture of the material or its color [1; 11]. It turnsout, however, that when scanning processes are simplified, the density of the scanned point clouds will be directly influenced by the type of surface in question [12; 13]. Therefore, the research that we designed and carried out focused on obtaining information about which materials affect the quality of the acquired data and how surfaces of materials can be treated so that their surface can be scanned in higher quality. Subsequently, this approach will make it significantly easier to obtain very high-quality 3D models at a relatively lower cost and in a short time [9]. This could speed up the passporting of a wide range of existing buildings and provide very high-quality documentation at a favorable price, even for investors who are limited by budget [14]. This research is a follow-up to the diploma thesis entitled \"The influence of the material, texture, and shape of the object on the density of the point cloud created using optical measurement methods\" [15] and expands the number of measurements for some materials and, at the same time, selects other materials that have not yet been investigated from this point of view. The point clouds are also examined regarding other issues, like arrangement of points in point clouds, distribution of points in the measured area, and vertical dispersion of points, which may also influence the process of 3D data acquisition and its results. At a time when laser 3D scanning and structured-light 3D scanning are no longer new, we focus on the issue of different characteristics of materials and their impact on the resulting quality of the 3D model of real objects. When improperly chosen structured light 3D scanning measuring technology is used, instead of a digital model, we can only obtain an incomplete point cloud that appears to resemble a digital model [16]. Many researchers in the field of laser 3D scanners and structured-light 3D scanning mention this issue (see Section 2.1) but do not examine it. It is, therefore, an unexplored topic in the field of structured-light 3D scanning measurement, which can help to speed up measurement and refine digital models in the future. The density of the point cloud can be evaluated by various metrics. For our research, the point-to-point metric was chosen. This metric is quite commonly used.