# Using Dynamo for Automatic Reconstruction of BIM Elements from Point Clouds
## 5 Discussion  
The pursuit of optimizing scan-to-BIM processes does not always involve reducing the time spent on modeling. Although speed is important, the optimization objectives also include representing the captured elements with high geometric fidelity, structural coherence, functionality, and performance; all these objectives must be achieved while maintaining a tolerable level of accuracy, approximation, and simplification of the model. The high complexity of buildings, especially when it comes to historical heritage buildings, makes it difficult to expand the use of BIM for building surveys and projects with existing buildings; in a recent survey, only 40% of the professionals interviewed indicated using BIM in more than three-fifths of their projects, and this number drops to 25% when it comes to heritage buildings [19]. The Old Lifeguard Station of Fuseta building showed different challenges in each element's modeling, reflected in different solutions and approach strategies in each case. Despite being a small building with a low complexity of details, it is rich in terms of various shapes and structural solutions. The characteristics found in this case, such as levels with different elevations and steps, deformities, and inclinations in the slabs, building structures with spots of corrosion and abnormalities, and deviations and irregularities in the beams  
Figure 23: Charts comparing the accuracy between models created by automatic and manual modeling approaches. (**a**) Percentage of points for accuracy of up to 12 mm. (**b**) Percentage of points for accuracy of up to 25 mm. (**c**) The average accuracy of all elements. and pillars, brought a problematic complexity to the research, but, at the same time, they were decisive for a better and more complete development of the algorithms. The algorithm's performance depends on the computer's data processing capacity, the point cloud size, the number of points to be processed, and the number of elements that must be created simultaneously. Thus, a segmented point cloud with unwanted points removed is easier for the algorithms to work and benefits the 3D reconstruction process. However, cleaning and segmenting the point clouds can consume much time in the process, and usually many companies decide not to perform this step. In a recent survey, 64.5% of professionals said they rarely segment the point clouds by building elements, 48.9% said they rarely segment by building floor or levels, and 22% rarely perform any cleaning procedure to remove unwanted points [19]. For this reason and to perform a stress test with the worst possible scenario, we used the algorithms with the complete point cloud, without any editing, cleaning, or segmentation, except for the topography modeling. After creating the models, the analysis of the results began, and a phase was foreseen in the investigation to validate the developed methodology and measure the efficiency of the algorithms. As mentioned in Section 2 of this article, the validation was carried out based on three main performance concepts, which were the time consumed, geometric fidelity, and element functionality, through variables selected from the conceptual and methodological references used in this study, which were the element type [18; 28; 46], modeling/approach type [7; 15; 18; 47], point cloud classification and segmentation [18; 45], Level of Development (LOD) [18; 45; 46], Level of Accuracy (LOA) [25; 28; 29; 48; 49; 50], BIM element, parameterization/parameter assignment [45; 46; 52; 53], and time saved [45; 51]. The elements were analyzed individually by their categories and their function in the building. They were also classified according to the approach and point cloud type. The type of approach was divided into automatic or semi-automatic; the elements made with the manual approach were not objects of analysis because the objective was to validate the performance of the process automation algorithms.