## 5 Discussion
Appendix A Figures A1-A4 presents the best and the worst distribution of natural check and control points for particular scanner stations on test sites I and III. When the distribution of points presented in Figure A1 (Test site I) is analysed, it may be noticed that the highest number of control and check points was detected for the orthoimage and the FAST detector. For remaining cartographic projections the number and distribution of control and check points also guarantee the correct implementation of the orientation process. When results presented in Figure A2 are analysed, it may be noticed that the worst distribution and the smallest number of points were obtained for the Mercator projection and for the SURF detector. In the case of Test site III (Figure A3), which is an office room, the best distribution and the best number of points were obtained for spherical projections and for the Mercator projection for both detectors (FAST and SURF). However, when the distribution of points from the orthoimage is analysed, they may also be considered correct. When results presented in Figure A4 are analysed, the smallest number of points and the worst distribution were obtained for points detected in raster images in the Mercator \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{3}{*}{**Statistics**} & \multirow{3}{*}{**Detector**} & \multicolumn{3}{c}{**The Statistic of The Rmse on Detected Natural and Marked Check Points [mm]**} \\ \cline{3-6}  & & \multicolumn{3}{c}{Test site I-marked check points} & \multicolumn{3}{c}{Test site I-marked check points} \\  & & \multicolumn{3}{c}{(our approach)} & \multicolumn{3}{c}{(target-based registration)} \\ \cline{3-6}  & & & \multicolumn{3}{c}{Cartographic transformation} \\ \cline{3-6}  & & \multicolumn{3}{c}{Cartographic transformation} \\ \cline{3-6}  & & Spherical & Orthoimage & Mercator & Spherical \\ \hline \multirow{2}{*}{**Avg**} & **FAST** & 4.2 & 6.7 & 5.8 & \\  & **SURF** & 4.2 & 6.8 & 4.8 & 5.7 \\ \hline \multirow{2}{*}{**Min**} & **FAST** & 0.3 & 1.4 & 1.1 & \\  & **SURF** & 3.9 & 0.9 & 0.3 & \\ \hline \multirow{2}{*}{**Max**} & **FAST** & 9.2 & 14.6 & 13.1 & \\  & **SURF** & 4.6 & 14.6 & 10.3 & 29.5 \\ \hline \multirow{2}{*}{**Statistics**} & \multirow{2}{*}{**Detector**} & \multicolumn{3}{c}{Test site III-marked check points} & \multicolumn{3}{c}{Test site III-marked check points} \\  & & \multicolumn{3}{c}{(our approach)} & \multicolumn{3}{c}{(target-based registration)} \\ \cline{3-6}  & & \multicolumn{3}{c}{Cartographic transformation} \\ \cline{3-6}  & & Spherical & Orthoimage & Mercator & Spherical \\ \hline \multirow{2}{*}{**Avg**} & **FAST** & 3.0 & 3.9 & 4.3 & \\  & **SURF** & 4.1 & 5.9 & 4.7 & 1.3 \\ \hline \multirow{2}{*}{**Min**} & **FAST** & 1.6 & 2.0 & 1.6 & - \\  & **SURF** & 1.5 & 2.1 & 1.8 & \\ \hline \multirow{2}{*}{**Max**} & **FAST** & 6.5 & 9.1 & 13.3 & \\  & **SURF** & 10.0 & 15.1 & 14.7 & 4.6 \\ \hline \multirow{2}{*}{**Statistic**} & \multirow{2}{*}{**Detector**} & \multicolumn{3}{c}{Test site IV-marked check points} & \multicolumn{3}{c}{Test site IV marked check points} \\  & & \multicolumn{3}{c}{(our approach)} & \multicolumn{3}{c}{(target-based registration)} \\ \cline{3-6}  & & \multicolumn{3}{c}{Cartographic transformation} \\ \cline{3-6}  & & Spherical & Orthoimage & Mercator & Spherical \\ \hline \multirow{2}{*}{**Avg**} & **FAST** & 29.3 & 13.3 & & 3.8 \\  & **SURF** & 24.7 & 8.4 & 39.0 & \\ \hline \multirow{2}{*}{**Min**} & **FAST** & 3.0 & - & & - \\  & **SURF** & 3.0 & 8.0 & - & \\ \hline \multirow{2}{*}{**Max**} & **FAST** & 29.3 & - & & 14.0 \\ \cline{3-6}  & **SURF** & 54.9 & 8.8 & - & \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Comparison of results of TLS reaster images orientation and the target-based registration method. projection. To summarize, an important aspect related to data orientation is not only the number of tie points but also the distribution of points which influences on the quality of TLS data registration.