# Indoor Building Reconstruction from Occluded Point Clouds Using Graph-Cut and Ray-Tracing
## 1 Introduction  
Up-to-date 3D indoor models are being increasingly requested for the management of existing buildings. The modelling of as-built constructions can range from mesh and computer-aided design (CAD) models to building information models (BIM). While the two first representations are enough for visualization purposes, the reconstruction of semantically-rich representations such as BIM is required for a variety of applications in which, apart from the detailed geometry, semantics and topology play an important role. These applications are mainly routing and navigation [1], crisis management [2], energy efficiency analysis [3], and structural health monitoring, inter alia.  
In spite of the increasing demand for updated and detailed indoor models, the reconstruction of indoor scenes continues to be in an early stage in comparison with outdoor reconstruction [4]. Dense point clouds, provided by reality-capture technologies such as laser scanner [5] and photogrammetry [6], are typically processed manually or semi-automatically to create 3D models of buildings. Even if the automatic reconstruction of buildings outdoors shares many properties (and problems) with the issues associated with indoor modeling, there are some specific challenges due to complex building layouts and the high presence of objects like furniture and wall-hangings causing clutter and occlusions [7].  
The ongoing advances in the reduction of size and weight of terrestrial laser scanning sensors, together with improvements in indoor positioning techniques, have led to the development of productive Indoor Mobile Mapping Systems (IMMS) [5]. These systems may be cart-based [8],backpack-based [9] or handheld-based [10] depending on the platform in which they are carried. The dynamic nature of the acquisition process has as a consequence a higher availability of 3D point cloud data from indoors, especially in terms of data completeness. In addition, not only the point cloud but also the trajectory followed by the system during the acquisition is usually available. This is because most of these systems are based on simultaneous localization and mapping (SLAM), which is a technique consisting of the construction of an incremental map of the unknown environment and the simultaneous localization within it [11].  
The goal of this research was to develop an automatic method for modelling Manhattan-World (MW) indoor scenes on the basis of laser scanner data. An MW assumes that most man-made structures may be approximated by planar surfaces that are parallel to one of the three principal planes of a common orthogonal coordinate system. The presented methodology specifically copes with the presence of significant amount of clutter and occlusions that may cause a significant lack of data. The main assumption is the consideration of planar surfaces bounding the rooms. The large majority of existing residential buildings in modern cities are in line with this assumption. Doors and windows are also reconstructed. A ray-tracing algorithm was used to identify those regions that are occluded from every viewpoint and to distinguish these regions from openings in the surface.  
The paper is organized as follows. Section 2 reviews the state-of-the-art techniques proposed in the recent literature for indoor modelling. Section 3 describes the methodology to be adopted for indoor reconstruction. Section 4 explains the conducted experiments and respective results and Section 5 is devoted to the conclusion of this work.