## 5 Automatic Classification Model for Architectural Objects in Renovating Aging Buildings
### Automatic Classification of Architectural Objects Based on Point Cloud Data  
To classify architectural objects, it is necessary to establish a training dataset. While a larger dataset generally leads to higher accuracy in results, the abundance of irrelevant data is unnecessary. To address this, we utilized the \"Comprehensive 3D Indoor Data\" collected from the AIHub (aihub.or.kr), operated by the Korea Artificial Intelligence Society for the Promotion of Information and Communications, to gather raw 3D point cloud data. Figure 11: Structure of the FCAF3D Network. This dataset is expected to be valuable for applications such as 3D object recognition in indoor spaces, training AI robot systems, and rendering 3D perspectives for architecture and interior design. The Comprehensive 3D Indoor Data comprises a total of 731 indoor spaces, encompassing various types such as typical residences, single-person dwellings (studios, one-bedroom apartments, etc. ), schools, libraries, hospitals, public offices, offices, factories, farms, art galleries, amusement parks, cafes, restaurants, accommodations, and more. Within each indoor space, there are a total of 120,000 spatial elements. These spatial elements include basic spatial components, open-close spatial components, furniture elements, electronic device elements, facility safety elements, and others, as seen in Figure 13.  
Basic spatial components consist of elements like floors, walls, ceilings, columns, beams, stairs, and roofs. Furniture elements encompass items such as beds, tables, desks, chairs, sofas, storage cabinets, and others. The training data utilized the 'Comprehensive 3D Indoor Data' from AIHub for semantic segmentation labeling. The labeling classes included cupboard, floor, wall, column, beam, window, door, table, chair, sofa, bookshelf, board, and miscellaneous, totaling 13 classes, following the format of the FCAF3D dataset. The aim of this study is the classification and generation of architectural objects, to utilize the generated model in the remodeling process. Consequently, the classified point cloud should align with real-world objects to be effectively employed in practical applications, as seen in Figure 14.  
Figure 12: (a) Result with a Multi-Cell size of 1 cm; (b) Result with a Multi-Cell size of 1.5 cm; (c) Result with a Multi-Cell size of 2 cm; (d) Result with 4 blocks; (e) Result with 5 blocks. Using the FCAF3D network and the training model employed in this study, we conducted classification verification for the three architectural objects among the previously mentioned five (walls, columns, floors, roofs, and others) using architectural data other than the point cloud data used for training. In the case of this experimental building, there were no columns and roofs, and their boundaries were ambiguous, so they were excluded from the classification target. The following Figure 15a-d represent the results classified into each architectural object. Figure 13 represents images classified into walls, floors, and others (chairs, desks), respectively. In Figure 13, when classifying walls, you can see that there is space left above and below the yellow Bounding Box. While the roof and columns of the target building were not clearly distinguished, since the dataset was structured with five categories during data collection (walls, floors, roofs, columns, others), you can see that the upper part corresponding to the roof and the lower part corresponding to the floor were not included in the walls. Figure 13 shows the result of classifying the floor. However, because the dataset consists of point cloud data, it was unable to classify the thickness or volume of the floor. When extracting point cloud data, points are obtained based on factors like the time it takes for the laser to reflect. In this process, the surface of the floor can be obtained through laser reflection. However, the underside of the floor, depending on its thickness, cannot be captured, resulting in the classification of only the floor's surface.