# Using Dynamo for Automatic Reconstruction of BIM Elements from Point Clouds
## 4 Results
a difference of 10 mm compared to the model created with automatic and semi-automatic algorithms. If we exclude the terrain and only consider the building, the model presents a better accuracy of 10 mm, but it still has a 3 mm difference from the automatic model. The elements that presented the best accuracy were the beams and columns, with an average of 8 mm and 9 mm. The walls and floors presented the same accuracy value of 11 mm, while the topography was the element with the lowest performance, with an average of 24 mm of accuracy. The overall average performance of the model was 73.1% for an accuracy up to 25 mm and 49.5% for up to 12 mm, and an average accuracy of 18 mm; if only the building was considered, excluding the topography, this model presented a performance of 90.8% for an accuracy of up to 25 mm, 70.5% for up to 12 mm, and an average accuracy of 10 mm (Table 3). The total time spent in manual modeling was 6 h and 35 min. This time consumed can increase in cases where it is necessary to have better accuracy and precision. In general, in terms of time consumed to complete the model, the automatic method was 25% faster than the manual method, and 18% faster excluding the topography, (Tables 2 and 3). The element that showed the greatest variation between one method and another was the topography, which took only 12 min in the automatic method, while in the manual method the time consumed was 40 min, and if there is a need to improve the accuracy of the model, this time will increase. Although for some elements the gains in terms of time consumed are not significant, modeling manually requires complete attention and active interaction by the modeler, while with automatic methods much of this  
Figure 22: Level of accuracy of elements modeled with the manual approach compared to the point cloud obtained from the terrestrial 3D laser scanning of the existing building. time consumed is just waiting for computational processing. Thus, the automatic method demands less from the operator in terms of concentration and, consequently, reduces the probability of failures due to a lack of attention. Another factor to note is that the time consumed with automatic methods can be reduced using more powerful computers, while the time consumed with manual methods can increase if the modeler is not so experienced. The overall efficiency of the model created by the algorithm was higher in terms of performance and accuracy. When using an accuracy of 25 mm as a reference, we noticed that the automatic and the manual methods were accurately equivalent. The building model made with the automatic approach (excluding the topography) performed only 3.9% better than the one with the manual approach. When changing the accuracy category to 12 mm, a more significant variation in the model's precision can be seen; for this category, the model from automatic methods was 12.4% better in performance than the model created manually; this value increased to 31.1% if the entire model with the topography is considered (Figure 23). The average accuracy of each element was also calculated individually, and, in all cases, the automatic methods returned better values in terms of accuracy than the models created by the manual method. The structural beams had a minor variation in their average accuracy with a value of 1 mm, and the rest of the elements presented a variation between 2 mm and 7 mm, except for the topography, which had a variation of 16 mm between the two approaches. The element that presented the highest variation was the topography; the automatic approach increased the model accuracy by 66.7%. The floors also had a high variation, in this case, 63.6%. These numbers reflect that modeling organic and irregular shapes in a traditional way, besides being a laborious and time-consuming task, can produce an inaccurate result. The structural columns also present a significant variation between the approaches; for this element, the automatic methods produced a model 33.3% more accurate than the manual approach.