## 4 The M_HERACLES Toolbox
### Step 2 (1): Automatic Segmentation and Classification of Structural Supports
#### 4.2.2 Results and Analysis  
A substantial test regarding the results of this section was presented in [67]. In this paper, two new datasets were introduced: the Central Pavilion of the Kasepuhan dataset and the Valentino dataset, while the St-Pierre dataset was presented with new statistics. The results can be consulted in Figure 10. The Kasepuhan dataset is small in size, with a little over 333K points while the St-Pierre and Valentino datasets presented a much larger point cloud with over 1.8M and 3.5M points, respectively. It is also interesting to note that the three tested datasets possess different styles of architecture; the Kasepuhan dataset consists mainly of open pavilions with many free-standing columns while the Valentino presents an example of an interior point cloud case. The St-Pierre church choir was chosen due to its particularity in that it possesses twin pillars instead of the usual free-standing ones. As can be seen in Figure 10, in terms of the segmentation process, the algorithm managed to detect eight structural supports in the Kasepuhan dataset. This corresponds exactly with the ground truth data. In the case of the St-Pierre, it managed to correctly detect the eight pillars individually despite the twin pillar nature. For the Valentino, 20 structures were detected in lieu of the actual 19 as can be found in the ground truth. The Valentino data presented a particular challenge since 13 out of the 19 pillars present in the dataset are in the form of engaged pillars, i.e., semi-pillars or columns which are part of the wall. As can be seen from the results, the algorithm had difficulties in segmenting these kinds of structural supports, while having no problem for free-standing pillars. For the Kasepuhan dataset, a preliminary segmentation was performed to divide it into the building body and attic, with the body used as input for the function supportdetect.m as described in Algorithm 2. Some points mainly at the top of the pillars remained unclassified. This is due to the fact that, in the pre-segmentation of the building body and attic, the algorithm considered the change in the surface of the cross-sections of the building to determine the two parts. These cross-section surfaces being calculated from the surface of the bounding-box, only the exterior point cloud was considered instead of the interior. This is reflected numerically in Table 4, where the recall value for this dataset is visibly low despite a very high precision. The inverse is seen in the Valentino dataset, where the non-planar ceiling created a case of oversegmentation. Indeed, the statistics as for Valentino show a high rate of recall but lower precision. The visual results for the St-Pierre dataset had been amply described in [67], including the overclassification of the iron fence attached to the posterior pillar. Statistically speaking, within Tables 4-6, the overclassified column describes the number of points considered as false positives, while the unclassified column denotes the true negative points. False negative points are not shown since the values are negligible due to the cookie-cutter approach of  
Figure 10: Results of the detection and segmentation of structural supports, as well as their subsequent automatic classification. In the segmentation part, each color denotes a different point cloud cluster, while, in the classification part, red clusters are columns and blue clusters are non-columns. taking all points of all elevations of a particular polygon shape. Similar to the analysis conducted in Section 4.2, four statistical values were presented to assess the quality of the algorithm, namely percentage of unclassified points, precision, recall, and F1 score. In terms of the unclassed percentage, Kasepuhan showed a higher rate (median of 34.36%) which is most probably caused by the same reasons as the one established before regarding errors during the pre-segmentation between the body and the attic. The median precision of Kasepuhan is 100%, which is very satisfactory.