# Rule-Based Scan-to-BIM Mapping Pipeline in the Plumbing System
## 6 Case Study and Performance Analysis
### Pipe Object Mapping Pipeline Performance Test in Imperfect PCD
#### 6.2.3. Pipe Object Mapping Pipeline Performance Test in Massive PCD  
To test the pipe object mapping performance in massive PCD, the factory plant equipment was scanned with LiDAR. The scanned PCD size is 89,449,621 (2 GB) (Figure 11). After calculating the total size of the point group, the grid sizes divided by three on the X-Y-Z axis were used to generate voxels. Voxels were used to improve PCD calculation performance. Down-sampling was performed for LoD processing, and the parameter value was 2 cm. For reference, the pipe radius required was between 20 and 50 cm. The difference in diameter between these pipes was 6 cm. When mapping the extracted geometries to BIM objects, they were converted into BIM objects using Scan-to-BIM mapping ruleset. In addition, the parameters such as grid size or LoD were chosen considering these situations.  
The final data is approximately 85% of pipe objects extracted from the point cloud data.  
This paper checked the object mapping performance between the proposed pipeline method and Company E's software. Company E products are mainly used in the industry for automatic pipe extraction. As shown in Table 7, the pipe object extraction performance of the proposed method is improved by 104.4%, and processing speed is improved by 687.5% compared to the software.  
Pipe extraction performance errors mainly occurred in small and complex pipes. The proposed method can precisely set the parameters of the Scan-to-BIM mapping pipeline, so that it is possible to  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Case**} & \multirow{2}{*}{**PCD**} & **Filtering,** & \multicolumn{2}{c}{**Straight Pipe**} & \multicolumn{2}{c}{**Elbow-Type Pipe**} \\ \cline{3-6}  & & **LOD(MB)** & & **Manual** & **Automatic** & **Manual** & **Automatic** \\ \hline A & 480,220 & 331,595(5.05) & 97 & 60 & 51(85.0\%) & 40 & 29(72.5\%) \\ B & 364,340 & 122,166(1.86) & 35 & 60 & 55(91.7\%) & 41 & 27(65.9\%) \\ C & 807,509 & 343,056(5.23) & 175 & 90 & 78(86.7\%) & 59 & 52(88.1\%) \\ Average & 550,690 & 265,606(4.05) & 102.3 & 70.0 & 61.3(87.8\%) & 46.7 & 36.0(75.5\%) \\ STD & 229,835 & 124,355(1.90) & 70.2 & 17.3 & 14.6(3.48\%) & 10.7 & 13.9(11.4\%) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Scan-to-BIM mapping pipeline performance (count, %).  
Figure 11: Massive PCD Processing Results (Input PCD, Proposed Scan-to-BIM Pipeline, Compared Software).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Program**} & **PCD (GB)** & **Filtering (MB)** & **Pipe (A/B\%)** & **Speed (min)(B/A\%)** \\ \hline A. Proposed & 89,449,621 (2 GB) & 8,994,608 (137 MB) & 1099 & 8 \\ \hline B. Compared & 89,449,621 (2 GB) & - & 1053 & 55 \\ \hline Difference (A\(-\)B) & - & - & 46 (104.4\%) & 47 (687.5\%) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Pipe object mapping performance results.  
obtain precision that is difficult to set in the comparison software. For reference, this study did not compare the overall functionality of the comparison software that is not related to the scope of this study. Based on the previously obtained data, the time required for work was compared as shown in Table 8. It is assumed that the scan-to-BIM mapping work of the same data size was performed with eight projects. In this case, 376 min per day and 7520 min per month are saved when measured against the comparison software.