## 5 Conclusions and Future Work
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Object**} & \multicolumn{2}{c}{**Point Number**} & \multicolumn{2}{c}{**Misclassed**} & \multirow{2}{*}{**True Positive**} & \multirow{2}{*}{**\%Unclassed**} & \multirow{2}{*}{**\%P**} & \multirow{2}{*}{**\%R**} & \multirow{2}{*}{**\%F1**} \\ \cline{2-2} \cline{5-10}  & **Manual** & & & & & & & & & \\ \hline Beam1 & 15 036 & 10 960 & 608 & 4 684 & 10 352 & 42.74 & 94.45 & 68.85 & 79.64 \\ \hline Beam2 & 57 986 & 43 826 & 0 & 14 160 & 43 826 & 32.31 & 100.00 & 75.58 & 86.09 \\ \hline Beam3 & 28 789 & 26 141 & 2 355 & 5 003 & 23 786 & 19.14 & 90.99 & 82.62 & 86.60 \\ \hline  & & & & & & **Mean** & **31.40** & **95.15** & **75.68** & **84.11** \\ \hline  & & & & & & & **Median** & **32.31** & **94.45** & **75.58** & **86.09** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Quantitative analysis on the results of step 2 for the detection and classification of beams. %P is precision, %R is recall, and %F1 is the normalized F1 score. the results remain promising and some lessons can be learned from this experiment, to be the subject of further improvement. A comparison with the DL approach presented in [36] for the Valentino also showed a rather favorable outcome with respect to the quality parameters. Furthermore, the fast nature of the segmentation and classification process is in contrast to training-intensive ML/DL and resource-intensive manual segmentation and labeling. It may therefore be used to complement ML/DL algorithms, especially in the generation of training data. The beam detection algorithm described in Section 4.3 is still in its early stages, and more tests must be conducted in order to assess its efficacy. A test with a small dataset yielded a very satisfactory median precision value of 94.45%, but with low median recall value of 75.58%, which is mostly due to the noise filtering function applied in the algorithm. Although these preliminary results are promising, processing time remains an important issue. Most of the processing time was used for the computation of the normals and especially curvatures. More research should be conducted to optimize this part of the algorithm better. Many improvements can be envisaged for this toolbox. For example, the use of CAD files in lieu of shapefiles for the Step 1 segmentation part could be very useful especially for some heritage sites where GIS is not available. Being a common file format in the domain of architecture, the CAD file can be another alternative for the segmentation aid. Another idea is to use CAD files to perform a similar algorithm as described in Algorithm 1, but applied to indoor scenes [75]. Another interesting idea which has been planned to be tested is to use the results from Step 2, whether structural supports or beams, to help generate training data for machine learning and deep learning techniques. As has been previously established, one of the bottlenecks in ML/DL approaches is the creation and labeling of training data which is performed manually. The algorithms proposed in this paper may help to automate (or at least provide an \"approximate value\") this training data generation process, thus rendering the overall 3D processing pipeline more automatic. Lastly, the availability of open point cloud data for heritage sites remains difficult due to various reasons. This applies both to ML/DL techniques (in the sense that it reduces the possible training data candidate) and to algorithmic approaches such as M_HERACLES (in the sense that it limits the available data for testing). The creation of an open data portal for heritage point cloud is therefore one of the intended future objective of the research. M_HERACLES is an open source toolbox written in Matlab(r).