## 4 Discussion  
The study results are summarized in Table 4.  
Based on the data presented in Table 4, it can be concluded that, in each analyzed case, both for laboratory and field data, the best results were obtained by MLP networks. The number of selected networks was five, and the number of hidden layers was one. The number of neurons did not exceed 10. In all analyzed cases, the optimal function turned out to be the linear activation function for the output layer, while for the hidden layer, the activation functions were different, with the predominance of linear ones. The maximum error of the selected networks did not exceed the value of 3\(\cdot\)10\({}^{-5}\) m in the validation and test set for the input data adopted with the precision of 1\(\cdot\)10\({}^{-3}\) m and expressed conventionally in meters, which should be considered a very good result. In the case of an average match error, the result is not that good. For laboratory data, ANNs have an advantage over the conformal transformation, being more precise. For the field data, in the case of the Z1 set, ANNs are slightly more accurate than Total Station (1\(\cdot\)10\({}^{-3}\) m) and Terrestrial Laser Scanning (1 \(-\)3\(\cdot\)10\({}^{-3}\) m), while in the case of the Z2 sets, the transformation by similarity turned out to be more accurate (due to the greater error of the \(Z\) coordinate for ANN). However, these results for this dataset are still an order more accurate than the precision  
Figure 8: Terrestrial Laser Scanning (TLS) point cloud of Navigator XXI vessel for field research validation. of the input data obtained by Terrestrial Laser Scanning, which should also be considered satisfactory. In the areas between the base points (validation and test data)--inside the transformation area--ANN has a decisive advantage, while in the case of common points in the case of field data, the errors in both the traditional and the proposed algorithms are at the same level. At the same time, the base point errors for the laboratory dataset are still reduced for ANN. It should therefore be summarized that machine learning (ANNs) with the applied method on the common points (baseline) obtains RMSEs of the same level in the case of field data, and better in the case of laboratory data for the minimum number of common points. At the same time, for the areas inside the base points (the area inside the transformation), ANNs give reduced errors in each case. In addition, it can be seen that different activation functions are found for the field data and the different sets of base points used for the transformation. The conclusion is that ANN transformations are on the same level of accuracy or even better suited to conformal transformations for the specific cases they concern (for minimum number of common points), but cannot be directly replicated for other sets of points. The uniqueness provides a better match between the input and output data, which is an advantage of ANN, but changing the dataset means that these functions should be searched anew, which in turn can be considered as a disadvantage (one case cannot be directly transposed into another). Comparing the average match error (Table 2), it can be observed that the obtained results from the transformation of the \(X\), \(Y\), \(Z\) coordinates using the analytical method and the ANN are slightly different. The difference that is shown in the diagrams below is in favor of using the ANN (Figure 9).