## 2 Literature Review
### Feature-Based Method  
The feature-based method was primarily developed for semantic segmentation, and several studies have applied this approach to segment indoor point clouds. These methods can be categorized into clustering-based methods and deep learning-based methods.  
The clustering-based method segments targets, such as the structural elements and the indoor object, and accounts for the indoor clutter objects using an approach that determines the point cloud of the clutter aside from the target. For example, [PERSON] and [PERSON] [22] used pointNet features to perform clustering-based segmentation of point clouds by applying DBSCAN to two selected features. [PERSON] et al. [29] applied supervoxel and DBSCAN to remove the point cloud of the floor, while [PERSON] et al. [19] proposed a new density-based clustering method to segment indoor objects. [PERSON] et al. [18] determined the normal vector from the point cloud and applied DBSCAN to the generated sparse normal space to preserve indoor objects and remove planar elements. [PERSON] et al. [12] segmented a point cloud based on the z-axis into three segments for the construction of an as-built BIM and used the point cloud of the middle segment to create the floor plan. They applied k-means clustering to the x-y plane to extract the structural elements. [PERSON] and [PERSON] [30] proposed an automatic segmentation and classification method based on geometric feature clustering. The indoor clutter was categorized into virtual other objects, virtual objects on the floor, and virtual objects on the ceiling. The main approach of this study was based on the fact that indoor clutter objects have non-planar characteristics.  
The deep learning-based method is widely used for semantic segmentation of point clouds. In general, these methods identify and learn the key structural elements or primary objects of interest. They can also classify any unlearned point cloud as clutter. [PERSON] et al. [2] applied pointNet [23] for semantic segmentation, and [PERSON] and [PERSON] [31] applied DGCNN [32]. [PERSON] et al. [33] developed their own model to apply scan-to-BIM. Besides, other deep learning models such as RandLa-Net [34] and pointNet++ [35] have recently been actively developed and applied to Scan-to-BIM research [36; 37], segmenting both structural elements and indoor clutter objects. However, the classification performance between objects with similar geometric features, such as walls and columns, is still unsatisfactory. Therefore, if certain indoor clutter objects have similar geometric features with structural elements, it would be difficult to clearly identify those indoor clutter objects as clutter.  
As the literature above mentions, the feature-based method is effective in utilizing the geometrical features of the object to be preserved. However, they have limitations in accurately determining indoor clutter objects that have similar geometrical features to the structural elements. Also, they tend to create errors when the clutter objects are close to the structural elements. Therefore, the authors believe that if the indoor clutter objects are removed before the semantic segmentation task, it may lead to more accurate as-built BIM modeling.