# Automatic 2D Floorplan CAD Generation from 3D Point Clouds
## 4 Experiments
### Experiment Results  
During the overall wall detection process, we initially removed the floor and ceiling from the input 3D point clouds. Figure 5 shows the detected floor and ceiling point clouds of the seventh floor of the Robot Convergence Building of Korea University. Then, using Algorithms 1 and 2, we detected lines from point clouds. We proposed an efficient line-detection algorithm by considering missing 3D points and noise from sensor input. The proposed algorithm is more efficient than existing algorithms such as the faster line detection and Hough transform algorithms. Furthermore, the proposed algorithm does not require any post-processing such as the Hough transform. Figure 6 shows the comparison results of line-detection algorithms on the seventh floor of the Robot Convergence Building of Korea University and Table 2 shows the processing time for each algorithm using an Intel i7-7800X and 64 GB of memory.  
\begin{table}
\begin{tabular}{l l c c c c c} \hline \hline
**Dataset** & **Scanner** & **\# of Rooms** & **\# of Points** & \begin{tabular}{c} **Floor** \\ **Size** \\ \end{tabular} & \begin{tabular}{c} **Degree of** \\ **Clutter** \\ \end{tabular} & \begin{tabular}{c} **Degree of** \\ **Missing Points** \\ \end{tabular} \\ \hline \begin{tabular}{l} Seventh floor of the Robot \\ Convergence Building of \\ Korea University \\ \end{tabular} & ZEB-REVO & 7 & 1,969,106 & 300 m\({}^{2}\) & High & Low \\ \hline \begin{tabular}{l} Second floor of residential \\ house \\ \end{tabular} & Velodyne & 5 & 14,756,398 & 66.6 m\({}^{2}\) & High & High \\ \hline \
\begin{tabular}{l} Tango Scan-1 \\ phones \\ \end{tabular} & Google Tango & 4 & 1,000,193 & - & Medium & High \\ \hline
\begin{tabular}{l} Tango Scan-2 \\ phones \\ \end{tabular} & Google Tango & 5 & 1,000,077 & - & Low & High \\ \hline \hline \end{tabular}
\end{table}
Table 1: Details of datasets.  
Figure 5: Result of detected 3D point clouds of the floor and ceiling: (**a**) floor, and (**b**) ceiling. Experimented on the seventh floor of the Robot Convergence Building of Korea University dataset.  
Table 3 shows the final wall detection result. We detected 97% of the ground truth and reconstructed the detected walls in the IFC file format, as shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **3D Reconstruction BIM** & **2D Floorplan CAD [SVG]** \\ \hline Seventh floor of the Robot & & & \\ Convergence Building of & & & \\ Korea University & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Result of the proposed method.  
Figure 6: Comparison result of the line-detection algorithms: (**a**) Hough transform algorithm with post-processing, and (**b**) the proposed algorithm.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**S Seventh Floor of the Robot**} & \multicolumn{2}{c}{**Second Floor of**} & \multicolumn{2}{c}{**Tango**} & \multicolumn{1}{c}{**Tango**} \\  & \multicolumn{2}{c}{**Convergence Building of**} & \multicolumn{2}{c}{**Residential House**} & \multicolumn{1}{c}{**Scan-1**} & \multicolumn{1}{c}{**Scan-2**} \\ \hline TP & 92.8\% & 91\% & 92.2\% & 97.5\% \\ FT & 5.8\% & 7.1\% & 5.5\% & 1.6\% \\ FN & 2\% & 2.3\% & 2.2\% & 0.08\% \\ Precision & 94\% & 92.7\% & 94.3\% & 98.3\% \\ Recall & 97.8\% & 97.5\% & 97.6\% & 99.1\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Wall detection evaluation results.