## 1 Introduction  
Three-dimensional (3D) reconstruction of the indoor environment has received significant attention due to the development of smart cities. However, the automation of generating high-quality models remains to be a challenging issue due to the complexities of the indoor environment. The industry foundation classes (IFC) defines building information modelling (BIM) as having rich semantic information, 3D structural information, spatial relationships, and interoperable geometry [1]. BIM has been used in a number of applications, such as indoor navigation [2, 3], space management [4, 5], energy simulation [6] and real-time emergency response [7, 8]. Primary indoor locations utilizing BIMinclude indoor offices, parking lots, and commercial establishments, which are commonly comprised of basic elements, such as the ceilings, floors, walls, windows, doors, and pillars, and not objects, such as furniture. 3D Indoor models are often generated manually by creating geometric representations using point cloud data and commercial software. This often requires significant investment in time and in training personnel [9]. To accelerate the efficiency of data acquisition and improve the automation of reconstructed models, various laser scanning technologies and automated modelling methods have been developed. Over the past few years, terrestrial laser scanning (TLS) has been used to obtain high-quality data in the indoor scene, but often suffers from low mapping efficiency due to laborious scan station resetting, protracted registration procedures, and high costs. Thus, its application has excluded large-scale indoor data acquisition. RGBD panorama is acquired by a camera and a depth sensor [10], with added advantages of affordability and convenience of use. However, the image distorted and noisy that lead to difficultly build accurate models in large scale scenes. With the development of the simultaneous localization and mapping (SLAM), various types of mobile laser scanning (MLS) devices have been used for data acquisition, such as handheld, backpack, push-cart and robot mobile laser scanners. MLS systems can obtain point clouds by moving from different spaces and measure from different locations. The easy-to-use and affordable indoor MLS systems are mostly used for data acquisition of large indoor scenes [11]. While MLS ensures good coverage for indoor environment mapping, the data can be affected by a number of factors (e.g., moving objects, multiple reflections, and dynamic occlusions) resulting in quality losses, which present serious challenges in model reconstruction. Recently, numerous studies have focused on modelling the indoor environment. For example, some research [12; 10; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22] segmented the unstructured points clouds into individual rooms to provide a prior knowledge in building the indoor model. The over-segmentation often occurs in the spatial partition of long corridors [13; 14], creating a substantial challenge when segmenting long corridors. Neoteric methods [9; 15; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31] have focused on extracting piecewise planar surfaces to construct the model. Although the results of room segmentation have been satisfactory, indoor models still are difficult to accurately reconstruct because of high levels of occlusion and noisy point cloud data, while still requiring interaction [22]. Large scale scene modelling based on surface elements has significant computational challenges. In order to increase computational efficiency, many line-based reconstructed methods have been proposed [32; 33; 34; 35; 36; 37; 38; 39; 40]. Their results have shown that line-based methods are efficient and effective, which can produce accurate and complete line segments. However, the reconstructed models are only represented in vector line structure without room semantic information, even when these elements are unconnected.