## 2 Related Work  
In recent years, the automatic reconstruction of structured 3D models from point cloud is one of the core topics in surveying and mapping. The tasks of building indoor 3D maps through point cloud generally involve indoor boundary extraction, room segmentation and opening detection. Since indoor spaces contain not only the main structure of the building, a large number of factors that affect data collection, such as: furniture, pedestrians, windows, and other data, missing data and a large amount of clutter during data collection can be caused. For this reason, a large number of a priori constraints are usually used to extract information about the building components. For example, the Manhattan world hypothesis, which assumes an environment with horizontal floors and ceilings, and vertical walls that intersect at right angles [13]. However, many architectural elements of the real-world deviate from the Manhattan world assumptions. Much of the work began to introduce the augmented Manhattan hypothesis, the Atlanta world [14], into the work, where there is no restriction of walls intersecting angles compared to the Manhattan world. In the work of indoor boundary extraction, almost all methods processed the point cloud by projecting them onto the XOY plane and then extending it to three dimensions using several techniques [15; 16]. The techniques of extracting indoor boundary information by projection can be roughly divided into two categories: one is to obtain room boundaries by projecting point cloud onto a horizontal plane, which can effectively avoid the influence of indoor occluding objects [17; 18]. However, there are usually openings such as doors and windows in the house boundary area, and the data dispersed from the openings during the scanning process will lead to local boundaries being different from the real situation to a large extent. The other is to form the indoor boundary by directly projecting the point cloud of wall surfaces [19; 20; 21], which effectively avoid the influence of the dispersed point cloud in the boundary area. However, influenced by the occluded objects, all wall surfaces will not be completely obtained. [PERSON] et al. [7] generated planar partition maps by extending the centerline of all candidate walls, and then assigned all partitioned planes to rooms or external areas. After determining a suitable label, connected components of identically labeled cells are rooms, and edges separating differently labeled regions are wall elements. However, this method is subject to local density of point cloud, resulting in room boundary information being incorrectly labeled. [PERSON] et al. [21] complemented the wall surface by extending the local plane to the intersection line, but their method is only applicable to the locally missing wall surfaces. [PERSON] et al. presented four types of wall segment connections, which were obtained by user specification or data training to obtain the best wall segment connection [22]. [PERSON] et al. [23] addressed the problem of obscured wall information by projecting non-building points onto a suitable building plane through scanner scan trajectories and poses. Some studies segmented rooms by scanner trajectory information and viewpoint information, and their methods are effective not only for room segmentation, but also for architectural elements such as stairs, floors, and doors [24]. However, in practice, not all point clouds have scanner position and pose information. Therefore, some scholars study the problem of indoor room segmentation without scanner position and pose. Most of these studies project the point cloud to the XOY plane to form a floor plan, and then floor plans can be segmented by some methods, such as morphological methods [25]. Unlike methods based on floor plan segmentation, some methods divide the interior environment through space division and unit labeling methods.