# A Layer-Wise Strategy for Indoor As-Built Modeling Using Point Clouds
## 4 Experiments
### Comparisons  
In order to evaluate the performance, we conducted comparisons with two state-of-the-art methods, namely structured indoor modeling [8] and Manhattan-World urban reconstruction [10] (see Figure 27). Compared with the structured indoor modeling method, the proposed method generated more elaborate models, especially when recovering the complicated structures on the ceilings, and overhang building components (e.g., bay windows, windowsills). The method of structured indoor modeling required line-of-sight information to compute a score of free space to distinguish the indoor-outdoor spaces, which limited the capability of extension. Nevertheless, the proposed method from 3D primitives required nothing except the point clouds, which meant it could be applied to various types of point clouds.  
With respect to another method named Manhattan-World urban reconstruction, our method had two advantages. As shown in Figure 27c,d, the reconstruction results of these two methods are comparable. However, our method could produce more compact models with fewer polygons. This is mainly because [PERSON] et al.'s method relied on the process of axis-aligned box selection, thus, the final model consisted of a stack of closed cuboids, which introduced redundant polygons. The second advantage of the proposed method was the capability of expressing the interior spaces of the building, [PERSON] et al.'s solution could recover the exterior of the building delicately, but the inner space was blocked by enclosed boxes (see the transparent view of Figure 27c). In contrast to their implementation, our method could represent the interior spaces with connections between different rooms. It is worth noting that the other two methods were based on the Manhattan-World assumption, which cannot  
Figure 26: Sparse point clouds (with different downsampling rates) and corresponding output models: (**a**) sampling rate 60%; (**b**) sampling rate 30%; (**c**) sampling rate 5%.  
reconstruct the non-perpendicular structures such as the slant walls indicated in the red rectangles of Figure 27.  
Because the aforementioned two methods are designed merely for the terrestrial point clouds or cannot represent the inner space, we conducted another comparison with method proposed by [PERSON] et al. [17]. We used the mobile laser scanning (MLS) point clouds to evaluate the extension capability of the 3D primitive reconstruction method. In practice, the large number of point clouds lead to a crash in 3D primitive methods because of the deficiency of memory. The bottle-neck of processing large-scale point clouds for planar primitive detection was the insufficient memory due to huge computational costs. According to our experiment, it took 30 min for 10 million points on a desktop computer (Intel i7-4770, 10 GB Ram). Therefore, we downsampled this dataset to approximate 10 million points for the experiment.  
In contrast to the previous method, the proposed method possesses the advantages that it can generates more detailed models in the following aspects: (1) It can represent the actual thickness of walls, and (2) the planar primitives improved the capability of recovering small corners of buildings. Although our method provides more detail models, it gets stuck when encountering incomplete or low density point clouds (also shown in Figure 20). As illustrated in Figure 28, the proposed method fails in recovering some ceilings because the density of points decreases with the increasing level of architectures.  
Figure 27: Comparisons with different methods: (**a**) the original point clouds; (**b**) the results from structured indoor modeling method ([PERSON] et al. [8]); (**c**) the results from Manhattan-World urban reconstruction method ([PERSON] et al. [10]); (**d**) the results from the proposed method. From left to right: Side view, front view, and transparent view.