# Using Dynamo for Automatic Reconstruction of BIM Elements from Point Clouds
## 2 Materials and Methods
Dynamo allows the creation of programming algorithms in order to perform tasks that are not allowed or that are too complex to be performed with the basic functions of Revit. Autodesk Recap v.22 and CloudCompare 2.12 alpha software [43; 44] were also used to manipulate, clean up, and segment the point cloud and calculate the created model's accuracy. The geometric reconstruction of the captured architecture needs to occur in a pragmatic and organized way, following the concepts and definitions pre-established in the previous item. Linked to these concepts, variables relevant to the methodological development of this investigation were selected from the literature review. These variables guided the research both in the development stage of studies and approaches and assessing and validating the results. The selected variables were divided into process and performance variables. Process variables are used to guide the researcher in their experiments, as they  
Figure 2: Survey of the Old Lifeguard Station of Fuseta building, Portugal. influence how the approach to the object will be made, including the use of techniques and tools. In this group, we had Level of Development (LOD), modeling type, element type, segmentation, and classification of the point cloud. The performance variables are related to the model's efficiency within the established process, with functions of quality control, analysis, and performance measurement. The variables chosen for this step were Level of Accuracy (LOA), time consumed, parameterization, and BIM element (Table 1). Among the process variables used, we have element type, a variable that classifies the existing elements in the building so that the modeling phase occurs independently of the other elements. Within BIM, a 3D model is not just a solid; it is linked to information, data, parameters, and geometric and non-geometric characteristics of the element it represents [26; 38]. Thus, the function that a door represents in the BIM environment is different from the function of a wall, and this applies to all existing elements in the model. The modeling task is directly linked to the Level of Development (LOD) variable, as it is through the prior definition of the LOD to be achieved that the elements to be modeled are defined, and how this process should take place. Each building element has a specific LOD and the level of accuracy that is measured in each of them independently [18; 28; 46]. The modeling type variable classifies how modeling takes place [7; 15; 18; 47]. It can occur manually, automatically, and semi-automatically; it can use a single tool or external tools to bring the desired result. The segmentation and classification of the point cloud variable refers to a preliminary moment in the modeling phase; this is a stage of active interaction of the modeler with the survey product, and the variables of the first phase also influence that. Here, the objective was to perform the necessary procedures to prepare the data to be used in the 3D modeling. The segmentation, cleaning up, and classification of the point clouds can happen manually or automatically [18; 45]; this influences the time spent at the end of the process, and it also increases the performance of automatic approaches, as unwanted points and objects will not be processed by the algorithms. The next step after modeling is validating the model through measurements and analysis with the application of the performance variables. This validation goes through three main concepts, which are (i) time consumed, (ii) geometric fidelity, and (iii) element functionality. The first variable used is the Level of Accuracy (LOA) [30; 31; 32; 33; 34], which measures the model's accuracy to the captured object. The LOA is not a fixed value, like the LOD; the definition of which LOA to be used varies according to the type of element being modeled, the type of building, and the needs and objectives for which the model is intended.