Table 1 Metrics for evaluating the hand-crafted and learned-based features
Metrics | Description |
|---|---|
Number and tie point distribution | The high number of points can lead to detecting the correct points used in the registration step. Additionally, it also affects the possible determination of tie points between multiple scans and the final robustness of the registration |
The completeness of data registration | The data registration is understood as the ability to orientate all pair scans to each other with a minimum number of connections and determine the robustness and effectiveness of the TLS-SfM approach |
The registration accuracy | The registration accuracy determines the final quality of matching between points clouds on marked check points. The final step of documentation generation results in the final accuracy of 3D models and documentation |
The reliability assessment | The reliability assessment makes it possible to assess the correctness of the geometric distribution of the tie points in a fully automatic manner. By meeting the minimum requirements for the values of these coefficients, it is possible to assess whether a network of tie points is robust |
The distance between a pair of point clouds | The analysis of the distance between point clouds allows independent control of the accuracy of matching whole and fragmented point clouds. Such a metric enables the accuracy of the fit to be assessed for objects for which it is not possible to distribute marked reference points. This allows the results to be compared independently with stat-of-the-art approaches, namely iterative closest point and target-based |