# Influence of Material on the Density of a Point Cloud Created Using a Structured-Light 3D Scanner
## 3 Materials and Methods
### Applied Process for the Research  
Figure 1 shows the process map, which was created according to the Business Process Model and Notation (BPMN) standard [30], of the process that was applied in this research. For this research, we have chosen a variety of materials (A1) that exhibit, in our own experience from previous research and in the experience of other researchers (see theoretical background) and practitioner colleagues working in this area, problems during scanning and data processing with either low point cloud density or no scanned points (D1). Such scanned objects have to be manually remodeled. This reduces the accuracy of the model compared to the original and also significantly increases the time needed to edit the scanned data. To evaluate the data in a relevant way, we had to choose the parameters for data capture, ensuring the same lighting conditions for each image, distance, and time of capture (A2). For the selected materials, we chose a scoring system (A3) that has three basic criteria with scores ranging from 1 to 10 (D2). A material rating of 1 indicates the greatest difficulty in obtaining data. A score of 10 indicates a slight problem with data acquisition. This activity is similar to building object reconnaissance, which is performed before scanning of a building object in practice. During the reconnaissance, issues with the scannability of a building object and its parts are estimated by experts. More details are provided in Section 3.4. This evaluation is important for the final evaluation of the measured data. This was followed by the first data capture of each material (A4). Each material was scanned five times, each time under the same conditions, which are described in Section 3.3. The scanning activities were performed five times to solve possible deviations and measurement errors, which are discussed in Section 5.1. Point clouds (PC) were created from which we evaluated the data (A5). The main parameter was the density of the point cloud across the entire area. Then, this was converted to the number of points per square centimeter. Materials with point clouds reaching a density of 2200 points/cm\({}^{2}\), which is in our experience sufficient to make 3D models of building objects, were excluded from the list and we continued only with those that did not reach these values (A6). These materials had to be treated to make their surface more suitable for sensing structured-light 3D scanning data. In most cases, it was the translucency/transparency or reflectivity of the material that needed to be reduced. In the remaining cases, it was the finer elements that needed to be made more compact (A7). The modified materials were scanned five times under the same conditions as in the previous data acquisition (A8). Additional point clouds (PCs) were created and evaluated on the basis of the above criteria (A9). Based on this evaluation, it was possible to exclude additional materials that already met the point cloud density requirement (A10). However, there were still materials whose point cloud was too sparse. Therefore, further treatment of their surface (A11) was designed and implemented to achieve a higher point cloud density on the same surface. The materials were scanned again five times under the same conditions mentioned above (A12), and the resulting point clouds (PC) were then evaluated (A13). From the evaluation, we found that after the surface modifications made to each material, the point clouds were already mostly dense enough to allow further work on them without reducing the density of the resulting model. More details about resulting point cloud density for particular materials and their treatments are given in the Results section.