# Indoor Building Reconstruction from Occluded Point Clouds Using Graph-Cut and Ray-Tracing
## 2 State-Of-The-Art  
Most of the applications requiring indoor models need semantically-rich representations including not only geometry, but also semantics and topology. Although BIM is the traditional domain encoding all the relevant information of individual buildings, Geographic Information Systems (GIS) have become increasingly detailed and have begun to model 3D cities including indoors. In spite of the fact that GIS and BIM domains overlap in terms of building modelling, each domain still has its own focus and characteristics. BIM considers very detailed and semantically rich information on all the physical elements comprising individual buildings as they are designed or built. On the other hand, 3D GIS representations mainly supported by the CityGML data model are more focused on describing the information about the environment that is captured at different points in time. In this case, representations of indoor scenes are less detailed but included into the broad context of a city. Apart from CityGML, the Open Geospatial Consortium (OGC) published IndoorGML, an application schema of the Geography Markup Language (GML), focusing also on semantics, geometry, and topology of indoor environments. Nevertheless, this scheme does not extensively cover geometric and semantic properties to prevent overlap with other indoor building modelling standards such Industry Foundation Classes (IFC) data model and CityGML. Several methods have been proposed in the recent literature for generation of indoor models. Their performance can hardly be compared since it mostly depends on the level of clutter and occlusions of the input point cloud and on the geometric, topological, and semantic detail of the output model. From this requirement, the ISPRS Benchmark on Indoor Modelling proposes the creation of a common framework for the evaluation and comparison of indoor modelling methods but it is still under development [12]. Recent methods dealing with indoor modelling can be classified into three categories according to the primitives they are reconstructing: linear-primitive, planar-primitive, and volumetric-primitive based [7]. In the first category, indoor modelling is based on floor plan extraction followed by extrusion, assuming walls as planar and vertical surfaces. These methods are generally performed on isolated floor levels and in the absence of clutter. [PERSON] et al. [13] develop a methodology for modelling floor plans by applying cell decomposition after line fitting followed by a graph cut optimization. Ref [14]reconstructing buildings indoors from point clouds already distributed into separate rooms. In this case, the labelling step is also solved as an energy minimization problem. Both works apply to MW and non-MW structures. However, they deal neither with occluded wall surfaces nor with door and window reconstruction. The planar-primitive approach consists of submitting point clouds to a planar primitive detection followed by a classification. [PERSON] et al. [15] use Random Sample Consensus (RANSAC) to plane fitting and calculate their extents by using alpha shapes. Similarly a plane-sweep approach is used in [16; 17] to find planar regions. However, even if these algorithms work well for extracting planar patches from the laser data, they do not consider the complete occlusion problem. These approaches are generally using 'contextual-based' reasoning to distinguish between building elements prior to plane fitting and intersection. Such approaches generate semantic labels of geometric primitives, and test the validities of these labels with a spatial relationship knowledge base [18]. The rule-coding process is generally quite complex and missing data may cause an incorrect application of rules. The volumetric-primitive detection generally imposes a stronger regularity and the developed methods are so far restricted to MW structures. [PERSON] et al. [19] introduced an inverse constructive solid geometry algorithm based on detecting wall segments in 2D sections which are combined to generate cuboids.