## 1 Introduction  
Three-dimensional (3D) indoor reconstructions have received increasing attention in recent years [1; 2; 3]. In the Architecture, Engineering, and Construction (AEC) domain, blueprints and as-built Building Information Modeling (BIM) have become must-have tools throughout a facility's life cycle [1; 4]. However, the built structure may be significantly different than what was proposed in the original plan [1; 5; 6], and blueprints of facilities may be unavailable [7]. Consequently, the reconstruction of indoor interiors via a precise 3D model has been an emerging challenge. The creation of a 3D model for indoor interiors entails large amounts of time and human resources [8]. To accelerate data acquisition and improve the accuracy of a reconstructed model, many research groups have developed various sensor-based surveying technologies [5; 9]. Laser-scanning technologies are significantly advanced [4; 10; 11; 12] and they can rapidly capture the details of a complex indoor structure's geometry; thus, laser-scanning technologies show promise for certain applications [8]. The high-quality reconstruction of a watertight mesh model from scannedpoint clouds [13] has drawn increasing attention in recent years in many areas, such as computer graphics and non-manifold repair [1; 13; 14; 15; 16]. Various types of input point clouds are suitable for surface reconstruction [17], such as those acquired from aerial Light Detection and Ranging scans (LiDAR), consumer-level color and depth (RGB-D) cameras, mobile laser scanning (MLS), and terrestrial laser scanning (TLS). Aerial LiDAR was proposed for large-scale outdoor environments and it may not be suitable for indoor reconstruction. RGB-D cameras (e.g., Microsoft Kinect) are affordable to the general public [18], although their depth information is noisy and possibly distorted and can have large gaps. MLS sensors (e.g., Zeb-Revo) ensure that the indoor environment has good coverage; however, their precision and density are not as high as those of TLS sensors. TLS sensors (e.g., Faro) have good precision and range, but they occasionally suffer from dynamic occlusion [19], i.e., moving objects, resulting in integrality and quality losses. MLS and RGB-D datasets [20; 21; 22; 23; 24] are tested in this study. Reconstructing indoor interiors from these scanned point clouds is still in the early stage, and this procedure is complicated by restrictions in the data and the complexity of indoor environments, which may exhibit high levels of clutter and occlusion [14]. Despite recent research efforts, a satisfactory solution for indoor interior reconstruction is still undeveloped [14; 15]. Various planar-detection methods have been proposed to rebuild interiors [4; 25; 26; 27; 28; 29; 30; 31; 32]. However, these plane detection-based methods are not robust when missing data, while the indoor point clouds exhibit high levels of missing data and noisy data because of windows and other highly reflective surfaces [15]. More recently, the focus has shifted to floor plane segmentation to address missing data [33; 34; 35; 36; 37; 38]. These methods consider the indoor-reconstruction problem to be a floor map-reconstruction issue and target room segmentation, but fail to consider wall-shape detection and reconstruction. Spatial partitioning along the wall direction, which is integrated with labeling partitioned cells via a graph-cut operation, was proposed to resolve these issues [1; 14; 39; 40; 41]. However, detecting long corridors by graph cut leads to over-segmentation [1; 21; 40]. Thus, reconstructing complete long corridors is still a challenge for these methods. Furthermore, the cited works did not consider the connected area across multiple floors. Although scholars [1; 33; 42] have applied their methods to multistory datasets and other researchers [26; 43] have tested their algorithms on stairs, none of these studies have explored the connected area between two floors.