## 3 Results and Discussion
### Sparse Point Cloud
higher intensity is attributed to the sensor sensitivity (i.e., ISO value) and the image format (JPG) that enables an auto adjustment to the level of brightness and sharpness of a captured image, thus making the SP's images brighter than those of DC's. The last two columns in Table 8 show the processing time and memory usage required to conduct the alignment process. The processing time is almost the same for both cameras in each data set. Nevertheless, the memory usage is slightly higher with the digital camera data sets due to the larger image size compared with the compressed smartphone images. In addition to the parameters provided in Table 8, the sparse point clouds can be analyzed and compared for both cameras based on the cloud noise. This can be achieved by computing the roughness of the sparse clouds using CloudCompare [46]. For each point in a sparse cloud, its shortest distance to the best fitting plane is calculated. The best-fitting plane is computed based on the neighbor points of the corresponding point. In this case, the neighbor points of a given point are those inside a sphere having a radius (\(r\)) = 5 cm, taking the sparse point cloud of the square beam as an example to estimate points roughness, Figure 14. An example of an outlier point that is considered as unwanted noise is demonstrated in both clouds having a roughness of 4 cm. Figure 14 also presents the points roughness histogram for both cameras. As indicated in the normal distribution fitting of the roughness histograms, the smartphone sparse cloud is associated with a slightly higher roughness (\(\mu\) = 4 mm) relative to the digital camera sparse cloud (\(\mu\) = 3 mm). Although this approach provides a quantitative assessment of the cloud noise, it is not precise since the nearest fitting plane figured by the algorithm might be a group of outlier points by itself. Nevertheless, Figure 15 demonstrates a further qualitative noise comparison between the two sparse clouds. Figure 15a,b presents the cross sections of the square beam sparse clouds of both cameras. As shown, the level of noise associated with the smartphone sparse cloud is considerably higher than the digital camera cloud. This can be attributed to the relatively smaller sensor size, lower resolution, and the image compression caused by the JPG format associated with the smartphone camera. Nevertheless, Agisoft Metashape performs a precise and powerful depth filtering to the sparse cloud when generating the depth maps for point cloud densification. Figure 15c,d provides the same cross sections of the clouds after densification. The smartphone densified cloud is noise-free and almost identical to the digital camera cloud. The filtering mode was set to \"aggressive\" for both clouds. Figure 12: An example of the R, G, B channels associated with the 3D points of the beam sparse cloud generated from the digital camera images. Figure 14: Computed points roughness with an outlier example of the beam sparse cloud generated from: (**a**) the digital camera data; (**c**) the smartphone data; along with the roughness histogram with a normal distribution fitting of (**b**) the digital camera data (\(\mu=3\) mm, \(\sigma=4\) mm); (**d**) the smartphone data (\(\mu\)= 4 mm, \(\sigma=5\) mm). Figure 13: An example of the R, G, B channels associated with the 3D points of the beam sparse cloud generated from the smartphone images.