## 1 Introduction  
An as-built building information model (BIM) is an innovative tool for construction management such as quality control, progress monitoring, inspection, safety monitoring, and so forth [1; 2; 3; 4; 5]. Additionally, as-built BIM is also useful for the control and monitoring of automated construction equipment and robots [6; 7]. In order to create an as-built BIM, it is necessary to collect the actual status of construction sites or an existing building. The most common methods to gather status data are laser scanning or photogrammetry [8], and their output is point cloud data. However, the point cloud data typically includes unnecessary objects, such as people, tools, and materials, which are considered clutter in the point cloud data [9; 10]. These clutter objects can negatively impact the accuracy and speed of automated as-built BIM creation, particularly in terms of point-cloud semantic segmentation of building elements [11; 12]. Therefore, it is critical to remove these indoor clutter objects from the point cloud. Removing outdoor clutter objects from point cloud data can be easily accomplished manually. However, it is relatively difficult to remove indoor clutter objects due to the large number of items and their interconnection with building elements such as floors, walls, and ceilings [13]. Therefore, manual elimination of indoor clutter objects is an inefficient process, which is why automation is necessary to develop efficient as-built BIM creation methods [14]. Removing indoor clutter objects intersects with several research areas, including the automatic creation of as-built BIM, the automation of two-dimensional (2D) floor plan generation, and point cloud semantic segmentation. The authors believe that removing indoor clutter objects could enhance the outcomes of critical processes in Scan-to-BIM, such as point cloud semantic segmentation, line fitting, and plane fitting. The proposed method aimed to obtain an x-y plane from which indoor clutter objects were removed. The obtained x-y plane could be used to remove indoor clutter objects from the point cloud. The most typical method for removing indoor clutter objects is the line-fitting-based method. The line-fitting-based approach identifies outliers using obtained lines or planes that represent the additional elements that need to be preserved [9; 10; 14; 15; 16]. However, the line-fitting-based method often ignores certain types of elements, such as indoor columns or walls, depending on their parameter values defined a priori, and it may not accurately reflect the thickness of inner walls [14; 16]. Furthermore, while an appropriate horizontal x-y plane is essential, related studies have either manually selected this plane or determined it based on a z-axis value. Another category of removal methods are feature-based approaches. However, it is challenging to set an appropriate parameter value in clustering [17; 18], and there is a limitation near the contact area of different objects [19]. In order to overcome these limitations, active research is being conducted on methods that use geometric features such as the normal vectors [18; 20; 21; 22], as well as those that use features extracted from deep learning models, such as pointNet [23]. Recently, deep learning models have been utilized to conduct semantic segmentation into learned classes. Subsequently, these models label any unlearned point cloud as clutter. While these methods classify structural elements based on distinct geometric features, distinguishing elements with similar features, such as walls and columns, remains challenging. Furthermore, removing indoor clutter objects that have geometrical features similar to structural elements poses a significant challenge. This study proposes an indoor clutter object removal method that works even when the geometric features of indoor clutter objects are similar to those of structural elements.