## 2 Related Work  
### From Point Clouds to Building Information Model  
The scan-to-BIM process involves three tasks [9]: modelling the geometry of components, assigning an object category and material properties to a component, and establishing of relationships between components. [PERSON] et al. [10] identified several characteristics induced by the use of laser scanner data for as-is BIM creation. First of all, the point cloud density, which describes the average spacing between points, conditions the size of the smallest object that can be modelled. The density of point clouds has to be chosen regarding to the level of details defined in the requirements of the project. Secondly, since point clouds are the source of information for the generation of the model, measurement uncertainties may affect the derived geometry. Measurement uncertainties are notably due to the scanned object properties [11; 12] and the geometry of the acquisition namely the distance to the object and the incidence angle formed by the laser beam and the scanned surface [13]. Thirdly, laser scanner allows acquiring large spaces but, because of occluding objects, it is difficult to acquire the whole environment. Even if some masks can be circumvent by optimising the successive positions of the laser scanner, in very congested spaces, occlusions are inexorably present in point clouds. It is particularly the case for indoor spaces where equipment and furniture are encountered [14; 15]. [PERSON]\(\acute{\text{a}}\)ucean et al. [16] also highlight that besides partially occluded objects, there are also objects which are invisible such as pipes located inside walls. Finally, since building geometry is imperfect, simplification assumptions are often considered and constraints are commonly introduced. In order to keep the true geometry and to assess the quality of the as-built model, it is essential to integrate information about deviations of the model from the laser scan data. One should also add to the above mentioned characteristics the absence of semantic information in point clouds. Indeed, point clouds provide information about the geometry of objects but don't contain semantic information such as the categories of objects or the materials composing construction elements. In that sense, [PERSON] et al. [16] explain that the scan-to-BIM process is limited. The best possible as-built modelling method cannot be expected to output an as-built BIM as rich as an as-designed BIM. An as-built model represents a reduced version of a complete BIM. Currently, the scan-to-BIM process remains largely manual and is recognized by many as time-consuming, tedious, subjective and requiring skills [9; 17; 18]. As explained by [PERSON] et al. [19], even with training, the result produced by one modeller may differ significantly from that produced by another person. The automation of the scan-to-BIM process is thus a very active research area. In order to model a building from point clouds manually, either the point clouds can be used as a visual support (usually in a top view) or sections can be generated. The manual modelling causes some errors since the modelling is achieved thanks to 2D views or restricted to specific locations. Semi-automatic tools were developed to avoid potential errors. Software dedicated to point cloud processing such as Realworks (Trimble), CloudCompare (EDF R&D) or 3D Reshaper (Technodigit) propose tools to create geometric primitives or meshes directly from 3D data. Unfortunately such a modelling does not generate objects which can be integrated directly in a BIM software such as Revit (Autodesk), ArchiCAD (Graphisoft) or Tekla Structures (Trimble). A conversion step, which often involves several software, is necessary. Moreover, this conversion can cause problems of data interoperability. Thus, semi-automatic software (e.g., EdgeWise, Trimble) or plugins integrated to BIM software (e.g., Scan-to-BIM, ImaginIT and PointSense, FARO) were developed specifically for the scan-to-BIM process.