## 3 Methodology
### Spaces Division and Extraction
#### 3.3.2 Generation of Wall Candidates
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Case ID**} & \multirow{2}{*}{**Components**} & \multirow{2}{*}{\begin{tabular}{c} **Time** \\ **(s)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Recognized Area Dimension** \\ **(m\({}^{2}\))** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Measured Area** \\ **Dimension (m\({}^{2}\))** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Accuracy** \\ **(m\({}^{2}\))** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Accuracy** \\ **(m\({}^{2}\))** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Average Accuracy** \\ **(\%)** \\ \end{tabular} } \\ \hline \multirow{4}{*}{1} & Walls & & 42.72 & 41.93 & 0.79 & 98.13 & \multirow{4}{*}{97.23} \\  & Door & 2.03 & 2.01 & 0.019 & 99.04 & & \\  & Ceiling & 14.28 & 14.06 & 0.22 & 98.39 & & \\  & Floor & & 14.28 & 14.06 & 0.22 & 98.39 & & \\ \hline \multirow{4}{*}{2} & Walls & & 101.33 & 99.76 & 1.57 & 98.43 & \multirow{4}{*}{98.68} \\  & Door & 4.22 & 4.19 & 0.03 & 99.20 & & \\  & Ceiling & 67.5 & 66.54 & 0.96 & 98.55 & & \\  & Floor & & 67.5 & 66.54 & 0.96 & 98.55 & \\ \hline \multirow{4}{*}{3} & Walls & & 468.14 & 455.36 & 12.78 & 97.19 & \multirow{4}{*}{98.21} \\  & Door & 46.04 & 45.56 & 0.48 & 98.95 & & \\ \cline{1-1}  & Windows & 84.8 & 15.56 & 15.35 & 0.21 & 98.63 & 98.21 \\ \cline{1-1}  & Ceiling & & 266.16 & 261.35 & 4.81 & 98.16 & \\ \cline{1-1}  & Floor & & 266.16 & 261.35 & 4.81 & 98.16 & \\ \hline \multirow{4}{*}{4} & Walls & & 256.78 & 249.44 & 7.34 & 97.02 & \multirow{4}{*}{97.06} \\  & Door & 12.09 & 12.45 & 0.36 & 97.11 & \\ \cline{1-1}  & Ceiling & 145.97 & 141.82 & 4.15 & 97.07 & & \\ \cline{1-1}  & Floor & & 145.97 & 141.82 & 4.15 & 97.07 & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Time consumption and the measurement accuracy of reconstructed components. Figure 14: Reconstruction results for case study (3), (**a**) reconstruction results in different stages during indoor reconstruction of the whole model, (**b**) reconstruction results of each functional space in case study (3). Figure 16: Average error for each component. Figure 15: Reconstruction results for case study (4), (**a**) reconstruction results in different stages during indoor reconstruction of the whole model, (**b**) reconstruction results of each functional space in case study (4). ## 5 Conclusions

In this paper, we propose an automatic indoor reconstruction methodology using low-quality RGB-D sequences. Our approach allows for the robust generation of semantically rich 3D indoor models and demonstrates applicability and reconstruction power for complex real-world datasets. From our theoretical analysis and experimental validation, the following conclusions can be drawn:

1. Benefiting from the multiple types of data set and the advantage of interactive data collection of the RGB-D mapping system, the proposed method provides new opportunities to use low-quality RGB-D sequences to reconstruct semantically rich 3D indoor models that include wall, opening, ceiling, and floor components. 2. For point cloud data with significant occlusion, most components can be recognized correctly to achieve an average accuracy of 97.73%. Some components in case study (2) and case study (3) that are absent from the point cloud can be recalled based on the layout determination algorithm. The reconstruction results indicate the robustness of the proposed methodology for low-quality point clouds. 3. The proposed reconstruction method produces an area dimension error within 3% for all cases. The measurement results indicate that modeling accuracy can be affected by the range sizes of the components. Higher range sizes result in lower accuracy. The automatic reconstruction method based on low-quality RGB-D sequences discussed here enables one to take full advantage of the information and the mode of data scanning provided by the RGB-D mapping system. This provides a fast, more convenient, and lower-cost solution for semantically rich 3D indoor mapping.