## 4 Results  
For the tests that were conducted using the Huawei smartphone, we mentioned previously a RMSE of 3.44 m between the camera positions measured by the RTK GNSS and by the Smartphone's internal GNSS (cf. Section 3.3). This deviation can be explained by the low precision of the internal GNSS module (-5 m) in comparison to RTK GNSS (-cm). Unsurprisingly, this has an impact on the SfM reconstruction performed while using the Geotag of the photographs (Test 2b). Figure 5 presents a top view comparison of the cliff front position obtained with the TLS and by SfM reconstruction using the Geotagged photographs. The mean error comparing the Geotagged SfM point cloud to the TLS mesh is 2.13 m with a standard deviation of 1.22 m. However, for estimating this error, CloudCompare(r) measures the normal distance between a point and the closest facet on the TLS mesh, even if it does not correspond to the same portion of the cliff. The difference between the Geotagged SfM point cloud and the TLS point cloud was assessed by performing an automatic registration in CloudCompare(r), which is based on an Iterative Closest Point (ICP) algorithm. The difference is mainly a translation (with Tx = 0.55 m, Ty = -4.46 m and Tz = -0.36 m), but, correcting this translation, the residual average error between the Geotagged SfM point cloud and the TLS mesh is still of 0.10 m with a standard deviation of 0.36 m, implying that other sources of errors (e.g., scale, deformation) are also present. As the geotagged Smartphone dataset is not exploitable without having to recur to a reference dataset for correction, this configuration will not be used again for the rest of the study. In the following, the SfM reconstructions obtained with the RTK GNSS-assisted system combined with Nikon D800 Reflex photographs (Test 1) and Huawei Smartphone photographs (Test 2a) are compared with the TLS point cloud and the resulting TLS mesh. In terms of point density, the TLS point cloud averages 84.6 points/m\({}^{2}\) (Table 3), with the actual point density varying depending on the TLS scan position and the angle of incidence of the laser ray with the cliff face (Figure 6a). The dense point clouds obtained by RTK-GNSS assisted SfM photogrammetry are four to six times denser than the TLS point cloud, with an average density of 476.9 points/m\({}^{2}\) for the Nikon D800 Reflex camera and 388.3 points/m\({}^{2}\) for the Huawei Y5 Smartphone camera (Table 3). Furthermore, the point density obtained with the RTK GNSS-assisted SfM photogrammetry is more homogeneous than for the TLS acquisition (Figure 6). Comparisons of SfM point clouds with the TLS mesh (Table 4) show a mean error of 0.3 cm and standard deviation of 4.7 cm for the Nikon D800 Reflex camera (Figure 7a) and, respectively, a mean error of 0.2 cm and standard deviation of 3.8 cm when using the Huawei Y5 Smartphone camera (Figure 7b). Comparing the SfM point cloud obtained with the Nikon D800 Reflex camera to the one obtained with the Huawei Y5 Smartphone camera (Figure 7c), the mean error is of 0.5 cm and the standard deviation of 6.1 cm. Figure 7c suggests that the Nikon camera point cloud is often ahead of the Smartphone point cloud, although this observation is not verified when looking at the mean error (Table 4). We think that the mean error calculation might be impacted by large negative deviations between the two point clouds, which could, in effect, balance out the more frequent but smaller positive differences. For both SfM point clouds, larger differences are situated on the fringes of the measured area and may be due to slight differences in spatial data coverage between the SfM point clouds and the TLS mesh. For the Reflex camera SfM point cloud, most of the difference corresponds to vegetated areas (Figure 7a), which are challenging to measure using either SfM photogrammetry or TLS.