## 6 Experiments and Discussion
### Verification of the Proposed Rendering System  
Figure 12 presents the results of the mapping experiment involving an actual private bedroom. During the experiment, the user held the camera, while continuously moving throughout the room. Local loop closure and optimization were performed simultaneously. When the user completed one circuit around the room, global loop closure was performed to prevent the accumulation of errors. In the mapping experiments, the data acquisition and reconstruction time were approximately 1\(\sim\)2 s by using CPU for computation only, and the reconstruction accuracy was within the range of 10 mm. The results demonstrate that the proposed loop closure strategy (Section 3.3) is highly effective in modeling an indoor environment for subsequent object segmentation and shape fitting. Figure 13a presents the test scene used for point cloud segmentation. As previously mentioned, the supervoxels and plane detection used in this study reduce the number of point clouds to decrease computational overhead. As shown in Table 1, we compared the segmentation times resulting from the various procedures to elucidate the influence of these two steps on computation time. Clearly, the use of these two steps can significantly reduce (by approximately 60%) the number of point clouds. This greatly reduces segmentation time and overall computation time, i.e., enhancing the efficiency of point cloud segmentation. Figure 11: Hardware used for system realization. Figure 12: Mapping results (top view). We then conducted an experiment on object segmentation using the simple scene in Figure 13a. Figure 13b-e illustrate the segmentation process using information derived from a single frame. Figure 13b presents the 3D image obtained using a Kinect camera. Figure 13e presents the results after supervoxelization and plane removal. From this, we can clearly see that the proposed method is able to differentiate between a variety of objects. Note that some of the parts show signs of over-segmentation, which could be resolved by integrating smaller groups. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Original Number of Point Clouds**} & \multicolumn{3}{c}{**25023**} \\ \cline{2-4}  & **Test 1** & **Test 2** & **Test 3** \\ \hline Supervoxelization & No & Yes & Yes \\ Time spent & N/A & 1.674 s & 1.674 s \\ \hline Plane detection & Yes & No & Yes \\ Time spent & 4.997 s & N/A & 0.64 s \\ \hline Number of segmentation point clouds & 3284 & 3215 & 952 \\ \hline Segmentation time & 49.674 s & 15.178 s & 2.207 s \\ \hline Total time spent & 54.671 s & 16.852 s & 4.521 s \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of segmentation times. Figure 13: _Cont._  
In the experiment above, the point clouds in each frame were integrated with the previous point clouds after segmentation was completed. This was because the images captured by the camera presented a consecutive sequence, which means that most of the objects in one frame were identical to those in the next frame. This allowed us to treat the overlapping portions or protrusions where two-point groups intersected as the same object suitable for integration. Furthermore, we could not be sure about the size of the environment, and computation time increased with the number of frames. Thus, the system began shape fitting immediately after a threshold number of segmentation frames had been met. Point groups were also checked at that time. In the event that the check results did not indicate a plane and the point groups in the current frame are connected to previously segmented point groups, we could assume that the object was no longer connected to the object in the following frame. Otherwise, we kept the point groups and integrated them with the point clouds in the next frame. Figure 14 illustrates the point cloud segmentation process applied to multiple frames captured in a complex environment.