From Point Clouds to Building Information Models: 3D Semi-Automatic Reconstruction of Indoors of Existing Buildings

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This work proposes a semi-automatic approach for the 3D reconstruction of existing buildings from point clouds in the context of BIM. This process remains currently largely manual and its automation will allow reducing time required for the manual modelling and errors induced by the user.

Abstract

The creation of as-built Building Information Models requires the acquisition of the as-is state of existing buildings. Laser scanners are widely used to achieve this goal since they permit to collect information about object geometry in form of point clouds and provide a large amount of accurate data in a very fast way and with a high level of details. Unfortunately, the scan-to-BIM (Building Information Model) process remains currently largely a manual process which is time consuming and error-prone. In this paper, a semi-automatic approach is presented for the 3D reconstruction of indoors of existing buildings from point clouds. Several segmentations are performed so that point clouds corresponding to grounds, ceilings and walls are extracted. Based on these point clouds, walls and slabs of buildings are reconstructed and described in the IFC format in order to be integrated into BIM software. The assessment of the approach is proposed thanks to two datasets. The evaluation items are the degree of automation, the transferability of the approach and the geometric quality of results of the 3D reconstruction. Additionally, quality indexes are introduced to inspect the results in order to be able to detect potential errors of reconstruction.

1. Introduction

The modelling of indoor areas of existing buildings is a huge issue since the emergence of Building Information Modelling (BIM) in Architecture, Engineering and Construction (AEC) industry. Volk et al. [1] highlight the fact that despite well established BIM processes for new buildings, the majority of existing buildings is not maintained, refurbished or deconstructed with BIM yet. According to Giudice and Osello [2], the need to refurbish the cultural heritage is becoming more important than the construction of new buildings. The potential benefits of BIM implementation in existing buildings seem to be significant, for among them, restoration, documentation, maintenance, quality control or energy and space management [1,3]. The creation of a Building Information Model of an existing building commonly named as-built BIM requires the acquisition of the as-is state of the building. Terrestrial Laser Scanning (TLS) is widely used to achieve this goal. Indeed, laser scanners permit to collect information about object geometry in form of point clouds. They provide a large amount of accurate data in a very fast way and with a high level of details [4,5,6]. Some governments such as the British and the American governments recommend the use of this capture technology in the BIM process [7,8].

Unfortunately, the scan-to-BIM process remains largely a manual process because of the huge amount of data, the challenge that constitutes the reconstruction of occluded parts of buildings and the absence of semantic information in point clouds. This is time consuming and requires skills. A key challenge today is thus to automate the process leading to as-built BIM from point clouds. The aim of our project is to develop a processing chain allowing to extract automatically the maximum of information from point clouds in order to integrate easily the result in BIM software. Three items were considered for the development of the approach. The first item is the automation. A 3D semi-automatic reconstruction is proposed and tasks which can be automated have to be identified. Secondly, in order to guaranty the generalisation of the approach, a transferability item is considered. It involves the definition of characteristics of studied buildings. The last but not least item is the geometric quality of the results. Quality criteria are intended to be integrated in the approach and quality indexes have to be proposed for the inspection of reconstruction results.

In this article, a state of the art of the process which consists in the creation of as-built BIM from point clouds is first proposed (Section 2). The Section 3 is dedicated to the full description of the developed approach which considers indoor point clouds as input. The project framework is presented and the two parts of the approach, namely the segmentation of building elements point clouds and the 3D reconstruction of walls and slabs of buildings, are detailed. The assessment of the developed approach is then considered in Section 4. Datasets and thresholds used for the assessment are presented and results of both parts of the approach are shown. Moreover, quality indexes are introduced for the inspection of results. Finally, some future works are proposed and new trends for as-built BIM creation are discussed (Section 5).

2. Related Work

2.1. 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.

Anil 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]. Pătrăucean 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, Pătrăucean 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 Xiong 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. In such software, structural elements are adjusted to point clouds from information given by the user.

2.2. Modelling of Building Geometry

2.2.1. Types of Models

Different types of models can be constructed from point clouds: models based on geometric primitives, mesh based models and hybrid models [20]. Models based on geometric primitives consist of the segmentation of point clouds into geometric shapes namely planes, cylinders, cones and spheres [21,22,23]. Mesh based models are considered when surfaces under study are complex or when a high level of details is required [24]. They are composed of sets of facets and require unlike models based on geometric primitives a large storage volume. Geometric shapes and meshes can also be combined into hybrid models when different levels of details have to be reached in a same model [25].

The choice of the type of model depends on the type of building under study. Historical buildings combine geometric shapes as well as distorted areas, architectural details such as mouldings and ornaments, that is why hybrid models are often considered. Industrial buildings present Mechanical, Electrical and Plumbing (MEP) objects generally cylindrical which have to be modelled. The modelling of such buildings often consists firstly in dissociating building architecture from objects of interest [26]. Most buildings are more ordinary and composed mostly of planes. Thus, in order to characterize the geometry of these buildings, models based on geometric primitives limited to planes are frequently used. Moreover, some assumptions can be integrated to the modelling process as for example the parallelism or the perpendicularity between two walls. The choice of the type of model is oriented by the type of building, but also by the precision defined in the project requirements. Runne et al. [27] study the link between modelling precisions and different applications. For example, for retro-engineering a model with a higher precision is required than for space planning and area management.

2.2.2. Modelling of Indoor Environments

The modelling of building façades from laser scan data was already the subject of many works (e.g., [21,28,29]). Our work focus on the modelling of indoor environments from point clouds. It requires other methods and involves more challenges to overcome. Indeed, an indoor space is not limited to the study of the elements belonging to a plane. Moreover, existing buildings can present numerous occlusions which disturb the modelling.

A building is usually composed of sub-spaces namely floors and rooms. Several approaches are developed in order to segment or identify these sub-spaces in a building point cloud. Huber et al. [30], Khoshelham and Díaz-Vilariño [31] and Oeseau et al. [32] use an histogram of the repartition of points along z axis to identify the different floors of a building. The study of the histogram allows determining ground and ceiling altitudes of the building. Ochmann et al. [33] propose a room segmentation algorithm based on the computation of the probability of each point to belong to a room. This algorithm requires the positions of the scanner as input. Budroni and Boehm [34] and Mura et al. [35] detect the major planes of a floor and use a cell decomposition in the X-Y plane in order to segment rooms. Lastly other methods of room segmentation are based on shape or structure grammars [31,36].

Rather than considering a decomposition into sub-spaces, an other approach consists in looking for structural elements of a building and notably walls and slabs. A plane segmentation is usually performed for the identification of planes composing the structural elements. The RANdom SAmple Consensus (RANSAC) paradigm is frequently preferred for this purpose [37,38,39,40] instead of region growing algorithms. Indeed, it is robust and efficient regarding processing time and noise management [41].Two methods are commonly used to segment point clouds of indoor environments. The first one consists in carrying out a plane segmentation and then in identifying groups of planes (gather together) which constitute building elements by exploiting the properties of planes such as their normals or their positions [37,39,42]. The second method executes several successive segmentations. Thomson and Boehm [40] first perform a segmentation into horizontal planes in order to extract planes corresponding to ground and ceiling and then a segmentation into vertical planes to identify wall planes. Hong et al. [38] and Valero et al. [43] also begin their processes by segmenting ceiling and ground. Then, the remaining point cloud is projected onto the X-Y plane and planes corresponding to walls are extracted by considering room boundaries in the 2D plane. Once planes are extracted from point clouds and identified as a part of a wall, a ceiling or a ground, different modelling strategies can be carried out. Wire frame models [19] or surface models composed of planes [43] are obtained by considering intersections between planes. Boulch et al. [44] propose a piecewise-planar 3D reconstruction. The reconstructed surface is in this case a polygonal mesh of the scene. Lastly Anagnostopoulos et al. [45] assemble wall planes and create a volumetric model of walls. This type of model is the one that is the closest to Building Information Model since wall objects are described by volumes.

A key challenge in the reconstruction of existing buildings is to manage masks in point clouds. Besides adding elements which do not have to be modelled, occluding objects also cause some missing parts of structural elements of a building. Some works deal with the reconstruction of missing parts of walls which is particularly interesting for the identification of openings (doors and windows). Huber et al. [30], Previtali et al. [15] and Xiong et al. [19] use a ray tracing labelling for that purpose. The ray tracing algorithm exploits the position of scan stations. For a given surface, a voxel space is created for each scan station. A voxel is labelled as either occupied, occluded or empty by studying the position of points in relation to the scanner and the surface. Based on the occlusion labelling results, Xiong et al. [19] use a learning-based method to recognize and model openings in the surface. They finally apply an inpainting algorithm to fill occluded regions of walls. Stambler and Huber [46] also use a ray tracing algorithm but they consider point clouds describing surfaces of both sides of a wall.

2.3. Object Recognition and Relationship Establishment

As highlighted by Tang et al. [9] and Pătrăucean et al. [16], object recognition is a core task in as-built BIM modelling. Beyond geometry, elements extracted from point clouds have to be assigned to several object classes. The attribution of semantic information can be done at various steps in the processing chain.

Semantic information can be integrated directly during the segmentation step and/or the geometric modelling of elements so that the search of building elements is guided. As shown above, some approaches integrate knowledge about building structure in order to identify specific elements in point clouds. Hong et al. [38], Thomson and Boehm [40] and Valero et al. [43] first extract only horizontal planes with the aim of identifying grounds and ceilings. Then, Hong et al. [38] and Valero et al. [43] exploit the fact that walls are located in borders of rooms to extract them. Whereas Thomson and Boehm [40] use a verticality constraint for the extraction of walls. Buildings generally follow standards and architectural codes linked to the type of building and the construction date. Plenty of buildings, notably office buildings, verify the Manhattan-World (MW) assumption originally defined by [47]. As explained by Anagnostopoulos et al. [37] these buildings have three mutually orthogonal directions. Grounds and ceilings are horizontal (X-Y plane), whereas the walls are vertical and either parallel to the Y-Z or the X-Z planes. Budroni and Boehm [34] consider the MW assumption in their approach. They use a sweeping method along Y-Z and X-Z planes to extract walls. Anagnostopoulos et al. [37] also take advantage of MW assumption in their processing chain in particular during the fusion of wall planes. Methods based on shape grammars, used more specifically for MW buildings, are studied by [31,48,49]. Ikehata et al. [36] developed an approach in which a scene graph is modified thanks to a structure grammar.

When the attribution of semantic information is not coupled with the modelling of the geometry, an a posteriori object recognition has to be performed. The chosen approach depends on the considered elements which are either parametric representations or non-parametric representations [9]. In the context of as-built BIM, one of the challenges for object recognition is that many objects are similar. Several works focus on the use of contextual characteristics to differentiate very close objects. A constraint network is used by [50] to represent a knowledge model. This type of network specifies the relationships between entities. For example, considering a building, this consists in saying that a wall is parallel or orthogonal to another wall and orthogonal to ceiling and floor. Bassier et al. [51] combine simultaneously contextual and geometrical characteristics in order to assign an object category to extracted elements. Pu and Vosselman [21] also suggest to distinguish segmented planar entities based on their characteristics. The characteristics taken into account are the size, the position, the orientation, the topology and the density of points. Xiong et al. [19] propose a machine learning algorithm to automatically label planar patches previously extracted as walls, ceilings, or floors. Features of different types of surfaces and the contextual relationships between them are learned to perform the labelling.

For non-parametric models, 3D object recognition algorithms may label object instances or recognized classes of objects. They are used whether in urban environments [52], industrial environments [53] or in indoor areas of buildings [54]. The recognition is based on a database of objects which have to be recognized. Shape descriptors are computed for both objects of the database, and the scene containing these objects. Then, a matching step allows to identify objects in the scene. Recognition of object instances is usually performed when objects with known shape and which are repeated in a scene have to be identified. For example, Johnson et al. [55] use this method in power plants for recognizing objects such as machinery, pipes and valves. Günther et al. [54] are interested in furniture recognition in an indoor environment. Based on a semantic model of furniture objects, they generate hypotheses to locate object instances and they verify them by matching a geometric model of the object into the point cloud. Recognition of object classes is more adapted to handle variations in object shape. Global descriptors are frequently used in this case but one disadvantage of using an approach with such descriptors is that it cannot handle partial data, ie. data in which occlusions are encountered. As highlighted by Tang et al. [9], research about recognition of BIM specific components (walls, windows, doors, etc.) is still in its early stages. Currently approaches developed for as-built BIM modelling usually consider a prior segmentation of the scene into planes and then features of planes are used to recognize objects.

The third task necessary for as-built BIM creation is the establishment of relationships between elements. Indeed, besides their geometry, building elements are also described by their topology. Geometrical data represent the dimensions and the location of elements whereas topological data refer to information about their spatial relations [56]. There is obviously a link between the type of element and the types of relations with other elements. As explained above, spatial relations between objects can by the way be used to identify the nature of objects. Several models were developed to automatically determine topological relations between objects. Nguyen et al. [56] present an approach which is able to automatically deduce topological relationships between building elements. Belsky et al. [57] implemented a prototype system for the semantic enrichment of precast concrete model files. Joint and slab aggregation concepts are considered. The composition of rule-sets uses topological, geometric and other generic operators. Anagnostopoulos et al. [45] present a semi-automatic algorithm which estimates the boundaries and the adjacency of objects in point clouds and creates a representation of objects in IFC format.

3. Full Description of the Approach

3.1. Project Framework

3.1.1. Buildings under Study

The French real-estate patrimony is rich and all types of buildings can not be taken into account during this project. Hence, it is important to define what type of buildings can be considered. A building is described thanks to several characteristics such as its size, its function, its topology, its date of construction, materials composing it and its aesthetic.

Buildings taken into account in the developed approach are ordinary buildings both by their function and their aesthetic. The modelling of industrial buildings or historical buildings is not envisaged since these types of buildings require specific processes. Moreover, in the developed approach, a building is described by planar shapes. Complex architectures composed of non-planar shapes are thus set aside. Also, some buildings composed of materials which are difficult to acquire by laser scanning could obviously not be considered. It is notably the case for buildings with façades made entirely of glass. The developed approach deals with buildings of variable sizes and dates of construction. These buildings are composed of one or several floors divided into several rooms. This organisation is followed by the majority of buildings as for example individual houses, public buildings or office buildings. Finally, developed algorithms handle only horizontal ceilings that is why rooms with sloped ceilings are not processed.

3.1.2. Input Data

The developed approach considers as input data acquired by laser scanners namely registered point clouds. Our project focuses on how to process these data even if it must be noticed that the acquisition and the post-processing steps have obviously an impact on the quality of the data and consequently on the quality of models created from point clouds. It is essential to adopt the best possible acquisition protocol to avoid troubles during the modelling step. In order to ensure the quality of input data, the following recommendations have to be followed by the operator:

• Scan stations:

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  optimize the placement of scan stations in order to acquire the whole environment without causing redundant data by multiplying scan stations

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  plan a consequent overlapping between point clouds in case of indirect georeferencing

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  if possible perform loops to improve the quality of the registration of point clouds (global adjustment) and to limit the risks of deviation

• Targets:

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  use at least 4 targets in common between two scans: 3 targets are sufficient to register point clouds issued from 2 successive scans but it is recommended to take more than 3 and to compensate the whole network of targets at once

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  prioritize targets placed in more than 2 scans. It should be ensured that targets are well-distributed in the space, that is to say placed at various ranges from the scanner, not in the same plane or along one line and locared at different heights

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  the use of natural targets such as edges, planes and cylinders can reduce and simplify data collection process; indeed, it can be difficult and time consuming to place targets throughout all the building

• Point spacing:

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  consider upstream the desired point density according to project specifications

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  adapt point spacing depending on space configuration and the positioning of targets in relation to the laser scanner

• Georeferencing after registration:

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  georeferencing can be either direct when positions and orientations of the successive scanner stations are known within a geodetic network, or indirect when the georeferencing is performed after the registration

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  in case of indirect georeferencing, the coordinates of targets or characteristic points well distributed in the scene are measured and are used to compute the transformation from the local system to the global system

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  georeferencing can improve the quality of the registration notably for a linear project

Considering the developed approach, several requirements have to be taken into account for the acquisition with the laser scanner. A minimum of one point per centimetre is required by the approach. Moreover, a scan has to be performed whenever the ceiling height changes. It shall also be ensured that the ceilings are not occluded at the scanner point of views since the approach largely exploits slices of point clouds at their levels.

The data considered as input of the algorithms are supposed to be correctly registered indoor point clouds. In order to save time during the process and to gain more insight into the number of points of extracted elements, point clouds are spatially resampled at 1 cm. Point clouds are also rotated to follow the orientation of one of the major façade plane obtained using a Principal Component Analysis (PCA) to accelerate processing time. Finally, a global shift to a local coordinate system is temporarily performed so that the original precision is kept.

3.1.3. Overview of the Approach

Figure 1 proposes an overview of the developed approach. The approach is composed of two main parts. In the first part, aim is to segment point clouds so that structural elements of the building are identified. Indoor point clouds are first segmented into sub-spaces namely floors and rooms. Then, for each room point cloud, several segmentations into planes are performed. These segmentations are combined with the classification of points into several categories (grounds, ceilings and walls). Finally, wall point clouds are obtained by assembling planes classified as walls.

In the second part of the approach, walls and slabs of the building are reconstructed in 3D based on wall point clouds and ground and ceiling point clouds. In order to do that, point clouds describing these structural elements are adjusted either by volumes or surfaces. The output of the approach is a file in the IFC format (BIM format) where walls and slabs are described.

The entire approach was carried out thanks to MATLAB (MathWorks) software. In the remainder of this section the different steps of the developed approach are detailed and illustrated based on indoor point clouds acquired in a public building namely a building of the National Institute of Applied Sciences (INSA) in Strasbourg (Figure 2). The ground floor, the first floor and a part of the second floor of the building which represent approximately 2500 m² were scanned. The acquisition and the post-processing of this dataset have already been presented in [58].

3.2. Segmentation into Structural Elements

The aim of the first part of the approach, structural element point clouds are segmented. It begins with the segmentation of the building point clouds into sub-spaces to move from scan point clouds to room point clouds. Then, based on each room point cloud, several plane segmentations are performed and points are classified as walls, ceilings or grounds. Finally, wall point clouds are obtained by assembling planes. This part of the approach was already described in [58,59].

3.2.1. Segmentation into Sub-Spaces

First, a floor segmentation is performed for scans resampled at 1 cm. For each scan, an histogram describing the distribution of points along z axis is created and analysed in order to determine the ground and ceiling altitudes. Thanks to ground altitudes, scans are grouped into several clusters representing the different floors. The floor segmentation result obtained for indoor point clouds presented in Figure 2b is shown in Figure 3.

Then for each floor, a room segmentation is performed. At this stage, a binary image is generated based on the projection onto an horizontal plane of a slice of point clouds at ceiling levels. The advantage of considering the ceilings is that at this location the point clouds of different rooms are not linked together. A slice of 30 cm was chosen. The distances between the ceilings and interior contours of doors are generally higher than this value. One should note that if the distances are smaller than the defined value, the link between rooms can be removed by considering the standard deviation of points along z axis (explanation below).

The generated binary image for the first floor of INSA building is presented Figure 4a. A pixel is coloured in white if it contains at least one point and in black otherwise. A pixel size of three times the spatial resampling namely 3 cm is chosen. The choice of the image pixel size is induced on one hand by the thinnest wall that can be encountered. A higher pixel size can cause the disappearance of thin walls and can consequently create links between rooms. On the other hand, by considering a spatial resampling of 1 cm the binary image does not contain “holes”. During the creation of the binary image, the standard deviation of points along z axis is also calculated for each pixel. The pixels corresponding to walls have a higher standard deviation and are temporarily coloured in black in the room segmentation process to ensure that there are no links between rooms.

A region growing is then applied in the image to identify 2D regions representing rooms. During this process, 2D regions are removed if their surfaces are lesser than a threshold ($s_{min}=$ 1 square metre). The result of the region growing applied to the image Figure 4a is presented in Figure 4b. It is superimposed with an existing floor map. The result is satisfying since the rooms are correctly detected.

Finally, to move from 2D to 3D regions representing the rooms, 2D regions are considered from ground height to ceiling height of the floor. A filter is also considered to eliminate 3D regions which are too small or which do not describe rooms. For this purpose, a minimum height $h_{min}$ of 1.8 m was chosen. This choice was induced by the fact that regarding to the French “Carrez” law, parts of an enclosed area with a ceiling height of less than 1.8 m are excluded from the calculation of the effective usable surface area of an accommodation. Moreover, by considering that a room measures at least one square metre of surface, a minimal volume $v_{min}$ of 1.8 cubic metres (1 m²× 1.8 m) was fixed. At the end of room segmentation, points which are not assigned to a region are kept aside for a further use.

3.2.2. Segmentations into Planes and Point Classification

Once room point clouds are identified, the altitudes of ceiling and floor of each room are determined. A plane segmentation is performed at these altitudes and points corresponding to ceilings and floors are extracted. A criterion of horizontality (${\alpha }_{hz}$) is considered. A robust estimator namely the Maximum Likelihood Estimation SAmple Consensus (MLESAC) is used for plane extraction. This estimator uses the same sampling strategy as RANSAC. However, it presents the benefit that the retained solution maximizes the likelihood rather than just the highest number of inliers as it is done by RANSAC [60]. A tolerance t defining the maximum distance between points and the corresponding plane has to be defined. In our case, it has been fixed to 2 cm for ceiling and floor extraction.

After removing ceilings and floors, room point clouds still contain points belonging to walls but also points belonging to occluding objects such as furniture for example desks, computer equipment, chairs and cabinets (see Figure 5a). At this stage, two assumptions are done: on one hand, points located in the borders of rooms are presumably points belonging to walls, and on the other hand, occluding objects generally do not reach the ceilings. Thus based on both room boundaries determined during the room segmentation, and the higher part of the remaining room point cloud, top of walls are identified and then points belonging to walls can be isolated. Figure 5b shows the classification result obtained for a computer room. Points classified as walls are kept in real colours whereas points belonging to occluding objects are presented in red. Points belonging to occluding objects are removed and stored in a remaining point cloud. A reference point cloud containing points belonging to walls of the first floor of INSA building was segmented manually and compared to the result obtained automatically in order to determine the percentage of points correctly assigned to the wall class (true positives). The result is satisfying since about 93 percent of the points are correctly assigned to the wall class.

A plane segmentation is then performed for points classified as wall points. For that purpose, lines are first extracted in a slice of room point cloud at ceiling level projected in the horizontal plane. A buffer zone is considered around a line and a plane is determined based on points located in this zone. The extracted plane has to be composed of a minimum number of 1800 points ($n{b}_{min}$). This value was established by considering a plane of 1.8 m height ($h_{min}$) and 10 cm width as well as the spatial sampling of 1 cm applied to input point clouds. Once again MLESAC is used for plane extraction. The tolerance t defining the maximum distance between points and plane is fixed to 5 cm for wall plane extraction. Considering that portions of walls have to be extracted, a criterion of verticality is imposed. A maximum angular value related to the vertical ${\alpha }_{vert}$ of one degree is defined. Moreover, a filter based on the Root Mean Square Error (RMSE) associated to the extracted planes is considered. Indeed, the extraction of planes can be disturbed by some objects fixed on the walls as for example black boards. A threshold ${\sigma }_p$ of 2 cm was fixed so that extracted planes which have a RMSE higher than this threshold are not considered.

Given our processing chain, the probability that the first segmentation extracts small planes is low, which is why several segmentations are performed sequentially. For each room point cloud, the number of segmentations to perform is determined automatically by thresholding both the percentage and the number of remaining points in the slice of points ($\%pt{s}_{remaining}$ > 5% and $nbpt{s}_{remaining}$ > 400). Moreover, the last segmentation must have extracted at least one plane of 1800 points minimum. At the end of the successive wall planes segmentations, unsegmented points are stored in a remaining point cloud.

Figure 6 presents the results of plane segmentations for several rooms of the 1st floor of the INSA building and the associated numbers of segmentations and extracted planes. The number of segmentations depends on the size of the room and the complexity of the space. One segmentation is required for the segmentation of a small storeroom (Figure 6c) whereas 9 successive segmentations are required for a corridor of about 20 m composed by a plenty of planes.

Finally, all extracted planes of a floor are grouped into walls by using two criteria that are the parallelism and distances between wall planes. Planes which are parallel considering a maximal angular value ${\alpha }_{⫽}$ of 5 degrees between them are selected. Then, they are grouped if they are close enough from each others. A maximal thickness $t{h}_{max}$ of 50 cm is defined for a wall. Figure 7 shows the result obtained for the first floor of INSA building. Each colour corresponds to one wall. Planes belonging to indoor parts of façade walls are identified by considering the borders of the floor. They are presented in grey in the figure.

Thus, the first part of the developed approach allows identifying wall point clouds. Each wall is composed of 1 to p planes. At this stage, a floor map can be automatically generated by considering the intersection of the extracted planes with an horizontal plane. Moreover, ceiling heights can be deduced from the altitudes of planes corresponding to ceilings and floors.

3.3. Reconstruction of Structural Elements

Planes composing walls, ceilings and floors were identified thanks to successive segmentations. These elements are still in form of point clouds. At this stage, a 3D reconstruction of the structural elements of the building (walls and slabs) must be performed. To reconstruct slabs and walls and to export the result into a BIM format, two steps are necessary. The 3D geometry of the elements is first described into the obj format [61] developed by Wavefront Technologies. This format is a simple data-format which is open and which is used by a plenty of 3D graphics applications. It has been chosen as a transition format towards IFC format [62]. The second step consists in translating the 3D geometry of elements into an IFC file in order to be able to open results of the developed approach in BIM software. For this purpose, the FreeCAD software is used. This software has the ability of importing obj file and of creating, from imported objects, building entities in IFC format.

3.3.1. Element Description into Obj Format

Based on point clouds extracted in the first part of the approach, slabs and walls are reconstructed with either volumes or surfaces. Several assumptions are taken into account for the reconstruction: slabs are horizontal, walls follow the vertical and the two sides of a wall are parallel.

For the description of slabs of the building, the altitudes of ceilings and grounds are used. The ground level of a floor describes the upper part of a slab whereas the ceiling level of the floor below describes the lower part of the slab. One should note that if several ceiling levels are encountered for a floor, the highest ceiling level is taken into account for the definition of the slab. A slab is described by a rectangular solid. In the vertical plane the level of the upper and the lower part of the slab is used and in the horizontal plane slab limits are determined considering the contours of the floor. For the two slabs located at the ends, a default value of thickness is used.

The walls identified in the previous part of the approach are composed of 1 to p planes. Their reconstruction consists of adjusting either volumes or surfaces to the wall point clouds. A maximal thickness $t{h}_{max}$ and a minimal thickness $t{h}_{min}$ are defined for this purpose. Depending on the number of planes which compose a wall, different cases appear:

• Wall composed of one plane: It can be encountered when only one side of a wall is determined or for elements such as pillars. In this case a line is adjusted to the plane point cloud projected in an horizontal plane. The wall portion is described by two points in the X-Y plane.
• Wall composed of two planes: If the two planes do not describe the two sides of a wall, the method of the case 1 is used. Otherwise, two lines are adjusted to the two plane point clouds projected in the horizontal plane. The wall axis is determined by the average of the two lines and the half-thickness is calculated by the average of distances between the points of both planes and the axis of the wall. Based on the axis and the thickness of the wall, four points are constructed to describe the wall in the X-Y plane.
• Wall composed of more than two planes: In this case, planes located in each side of the wall are grouped. If only one side is identified, the method of case 1 is used. If two sides are identified, the method proposed in case 2 is considered.

A slab is described by a volume composed of eight points and six facets. A wall is described either by a volume composed of eight points and six facets or by a surface composed of four points and one facet. Based on these information, a file in obj format is created. Figure 8 shows the result of the reconstruction for the 1st floor of the INSA building. In this figure the slab is in grey, the walls reconstructed by volumes are in orange-red and portions of walls reconstructed by surfaces are in yellow.

3.3.2. File Generation into IFC Format

The obj format is not a BIM format. Indeed, only the geometry of structural elements is described in a such a file. Neither object class is assigned to elements contrarily to what it is expected in a building information model. A conversion phase is thus necessary to be able to integrate results in BIM software. The Industry Foundation Classes (IFC) format was chosen as format for the output of the approach. The IFC format is a standardized object-based file format used by the AEC industry to facilitate the interoperability between the building actors. Both elements and relationships between elements are described in this type of file. It has the advantage not to be a proprietary format.

In order to generate a file in IFC format from a file created in obj format, the open-source parametric 3D CAD modeller FreeCAD was used. The Architecture module is particularly interesting since it provides BIM workflow to FreeCAD, with support for features like IFC support (thanks to IfcOpenShell, an open source library), fully parametric architectural entities such as walls, structural elements or windows.

The FreeCAD software allows importing the created obj file. Once the file is imported, each object can be selected separately. The objects describing slabs are first selected. A tool provided in the Arch workbench is used to transform these objects into parametric structural entities for which a role property is set (beam, column, slab, roof, foundation, etc.). Similarly, objects describing walls are selected and transformed into parametric wall entities. During the creation of wall entities, walls which are close from each other are joined automatically. Figure 9 shows structural and wall entities created in FreeCAD. One should note that the creation of entities is based on volumes and therefore walls described only by one surface disappear.

Finally, structural and wall entities are grouped into a building entity and a file in IFC format is exported. The whole steps carried out in the FreeCAD software (import, creation of structural, wall and building entities and export) can be automated thanks to a script in Python. To check if the export in IFC format works properly, the generated file for the 1st floor of the INSA building was imported into several viewers and several BIM software. The IFC file opened in Revit (Autodesk), a leading BIM software in the market, is presented Figure 10. At this stage, one can imagine to enrich semantically the created model by adding for example information about materials composing walls and slabs.

4. Assessment of the Developed Approach

As depicted in the introduction of this paper, three items were considered for the development of the approach: the degree of automation, the transferability and the geometric quality. The assessment of the developed approach will consider these items. The developed approach is applied to two datasets with the same thresholds in order to conclude about the degree of automation and the transferability of the approach. The assessment of the approach is divided in two parts. First, results obtained for the segmentations into sub-spaces and planes and the classifications of points into several categories are studied. Then, results of wall and slab reconstructions are presented. The geometric quality of generated models of both buildings under study are analysed. A complete automation of the reconstruction seems utopian. It is essential to be able to locate possible errors in order to proceed to a manual reconstruction in this case. Two quality indexes are proposed to visualise and locate these errors.

4.1. Datasets and Thresholds Used for the Assessment

The approach was developed based on two datasets namely an individual house and the INSA building presented above. Despite two buildings with different characteristics, the developments may have been influenced by particularities of these datasets. Thus, the assessment of the approach has been performed with two other datasets not involved in the development of the approach. In this sub-section, the datasets used for the approach assessment are first presented. Then, a review of thresholds involved in the approach is proposed and default values used for the assessment are listed.

4.1.1. Datasets for the Approach Assessment

During our project, a call for point clouds has been launched. Consecutively to it, four datasets were provided by French Land Surveyors Offices and a French company. It deals with indoor point clouds issued from laser scanning acquisitions. Among these datasets, two datasets were more specifically chosen for the assessment of the approach. They present the advantage of being relatively complete compared to the two others and they concern buildings with different functions and surfaces. Figure 11 presents the two selected datasets. The first dataset deals with an individual house composed of a garden level and a first floor for a total surface of about 200 m². Indoor point clouds of this house were acquired with a FARO Focus3D S120 laser scanner. The second dataset concerns an office building composed of two blocks and four floors. Indoor point cloud of the biggest block of about 1000 m² was considered. The laser scanner used for the acquisition was a Leica ScanStation C10. For both datasets, deliverables created manually from point clouds were also provided by the professionals.

4.1.2. Review of Involved Thresholds

The developed approach involves several thresholds which can be classified into the following categories:

• Thresholds related to spatial resampling of point clouds
• Thresholds related to space dimensions
• Thresholds related to constraints and quality criteria

A category of thresholds can be associated to several steps of the approach. Moreover, some thresholds are a combination of categories. Thresholds are often related to both space dimensions and spatial resampling. It is for example the case for the threshold defining the number of points of the smallest 3D region to keep. A synthesis of the main thresholds encountered in the developed approach is proposed hereafter in

Table 1. Main thresholds involved in the developed approach and default values.

Process	Threshold value
Spatial resampling and/or space dimensions
3D region growing in point clouds	voxel size = multiple of resampling
Filter for 2D regions removal in binary image	$s_{min}=$ 1 square metre (∼1100 pixels)
Filter for 3D regions removal	$h_{min}=$ 1.8 m (French “Carrez” law)
	$v_{min}=$1.8 m3 (92,000 points for a spatial resampling of 1 cm)
Minimal number of points for a plane	$n{b}_{min}=$ 1800 for a spatial resampling of 1 cm (10 cm × 1.80 m)
Conditions to perform an additional plane segmentation (while loop)	number of remaining points: >5% ($\%pt{s}_{remaining}$) of initial point cloud OR > 400 ($nbpt{s}_{remaining}$) for a spatial resampling of 1 cm (20 cm × 20 cm)
	the last segmentation must have extracted at least one plane of 1800 points minimum ($nbpextrac{t}_{segm-1}$≥1)
Constraints and quality criteria
Maximum distance between inliers and plane	$t=$ 2 cm for grounds and ceilings; $t=$ 5 cm for walls
RMSE of planes	${\sigma }_p<$ 2 cm
Horizontal and vertical constraints	maximal angular value of 1 degree (${\alpha }_{hz}$ et ${\alpha }_{vert}$)
Criteria for the identification and the reconstruction of walls	maximal distance between planes between 5 cm ($t{h}_{min}$) and 50 cm ($t{h}_{max}$)
	parallelism: maximal angular value of ${\alpha }_{⫽}=$ 5 degrees

. Some of default values of those thresholds are also indicated. In addition, Figure 12 shows where thresholds are used in the approach.

Some thresholds are defined manually. Thresholds related to constraints and quality criteria have to be defined regarding the project specifications and thus they cannot be automated. These thresholds are notably involved in plane segmentations: the maximum distance between a plane and its inliers, the horizontal and vertical constraints and the threshold related to the RMSE of planes. Default values are proposed to the user for such thresholds. Other thresholds are defined manually and are intended to be applied to buildings in general as for example the smallest volume or the smallest plane which have to be taken into account. Default values of those thresholds are also proposed and should be adapted to several datasets of various buildings.

By considering all information given by the user, some thresholds are defined automatically as for example for the minimum number of points of a 3D region. Considering a spatial resampling of 1 cm and a minimum volume of 1.8 cubic metres (1 metre by 1.8 m height), the smallest 3D region would count 92,000 points.

The approach was obviously developed so that it can be applied to all datasets having characteristics described in project framework. The approach was applied to the two presented datasets with the proposed default values of thresholds. The results will allow concluding about the generalisation of the use of thresholds defined manually but also about the adaptation of thresholds established automatically. In this way, the transferability of the approach is investigated.

4.1.3. Processing of Datasets

The developed approach was applied to the two presented datasets with the default threshold values proposed Table 1. The automatic 3D reconstruction of walls and slabs required a processing time of 8 min for the individual house and 19 min for the office building. Figure 13 shows for each dataset the repartition of processing time regarding the main steps of the approach namely the loading of point clouds, the segmentation into sub-spaces, the segmentations into planes and the point classification and finally the description of walls and slabs into obj format. One can observe that the repartition is quite similar for the two datasets. The most time consuming step is the segmentations into planes and the point classification which represents almost 50% of the processing time. The percentage of time required for the segmentation into sub-spaces is higher for the office building since the considered surface is five times higher than the surface of the individual house.

4.2. Assessment of Segmentations and Point Classification

In this sub-section, the results of the first part of the approach are investigated. Results of segmentations into sub-spaces and planes are assessed. The semantic aspect is also considered since the classification of points into several categories is analysed.

4.2.1. Segmentation into Sub-Spaces

Table 2. Results of the segmentations into sub-spaces (one colour per sub-space).

Data	Individual House	Office Building
Results of floor segmentations
Results of room segmentations (top view)	Garden level	2nd floor

presents the results of floor segmentation for the two datasets and the results of room segmentation for one floor of each building. For both datasets floors are correctly identified. Results of room segmentation are also very satisfying. Indeed, 3D regions describing rooms are distinct from each other. Considering all the rooms of the two buildings, only two of them were falsely fused. This fusion is due to the fact that one of the room was not complete in the initial point cloud and a part of the wall between the rooms was missing.

The analysis of remaining point clouds of segmentations into sub-spaces is also instructive. For the individual house, all rooms were extracted, whereas for the office building, two rooms out of forty one were not extracted and are in the remaining point cloud. It deals with small rooms partially scanned and for which the minimal density of points required by the approach is not reached. One can also notice that small parts of three rooms are present in the remaining point cloud also because of a too small density of points in the input point clouds.

4.2.2. Classification of Points into Several Categories

During the successive segmentations of room point clouds, points composing each room are classified as ceilings and floors, walls or occluding objects. In order to assess this classification, reference point clouds were segmented manually from room point clouds obtained after the segmentation into sub-spaces. Regarding a floor, points corresponding to ceilings and grounds are first segmented. Then, in a top view, points corresponding to walls and occluding objects are separated. In case of doubt on the nature of points, provided deliverables were examined carefully. Comparisons between the point clouds obtained automatically and the reference point clouds segmented manually allows determining the percentages of true positives, false positives and false negatives for each category and for each building. Moreover, for each building the precision and the recall of each category are calculated based on the following formulas [63], p. 138:

$$\begin{array}{c}\hfill precision=\frac{TP}{TP+FP}\end{array}$$

(1)

$$\begin{array}{c}\hfill recall=\frac{TP}{TP+FN}\end{array}$$

(2)

Results of the assessment of point classification into several categories are presented

Table 3. Assessment of point classification into several categories.

		Ceilings and Grounds	Walls	Objects
Individual house	TP	99%	89%	94%
	FP	1%	11%	6%
	FN	3%	1%	21%
	Precision	99%	89%	94%
	Recall	97%	99%	82%
Office building	TP	99.5%	93%	81%
	FP	0.5%	7%	19%
	FN	4.5%	9%	8%
	Precision	99.5%	93%	81%
	Recall	96%	91%	91%

TP = true positives; FP = false positives; FN = false negatives.

. For both datasets, results obtained for the category “ceilings and grounds” are very satisfying since a high percentage of true positives is combined with a low percentage of false negatives. For ”walls” and “occluding objects” categories, results are satisfying but some exchanges of points between the two categories are observed. These exchanges are mostly due to the fact that a lot of objects are very close or leaned against walls. For the individual house, points belonging to cupboards embedded in walls were falsely classified as walls because they are located in the borders of rooms. This explains that 21% of false negatives are observed for the "occluding objects" category.

4.2.3. Segmentation into Planes

As explained in Section 3, several plane segmentations are necessary and the number of segmentations is determined automatically. In order to validate if this number is correctly determined, remaining point clouds were analysed for both datasets. At the end of plane segmentations of room point clouds, 6% of points classified as walls were not extracted for the individual house and 12% for the office building.

It is interesting to compare remaining points with reference point clouds of “walls” and “occluding objects” categories. Among remaining points, 70% belong in reality to objects for the individual house whereas 30% for the office building. This means that quality criteria integrated in the approach allows rejecting points belonging to objects falsely classified as walls.

4.3. Assessment of Structural Element Reconstruction

After assessing the segmentation of point clouds, the reconstruction of building elements performed in part two of the approach must be evaluated. The geometric quality of reconstructed elements is investigated. The reconstructed elements are first manually inspected. Then analyses, based on both standard deviations associated to walls and distances points-to-models, are carried out.

4.3.1. Inspection of Reconstructed Walls

The results of 3D semi-automatic reconstruction of walls and slabs are shown Figure 14 for both datasets. For visualisation purposes exploded perspective views are used. For the individual house 44 objects were created (12 walls and 32 portions of walls) whereas for office building 127 objects were created (35 walls and 92 portions of walls). For both datasets, about a third of wall portions corresponds to indoor parts of façades since the approach considers indoor point clouds exclusively. A visual inspection of the results superimposed with input indoor point clouds allowed to determine that 93% of acquired objects were reconstructed automatically for the individual house and 94% for the office building.

In order to assess the geometric quality of the reconstruction, deliverables created manually based on indoor point clouds of the buildings were exploited. For each floor of the buildings, a 3D rigid body transformation was performed so that floor plan extracted from the results of the 3D automatic reconstruction can be superposed to floor plan created manually. For that purpose, control points corresponding to points in common between the two plans were selected.

Table 4. Deviations between control points selected in floor plans created manually and automatically.

		Number of Points	Mean of Deviations (cm)	Minimum Deviation (cm)	Maximum Deviation (cm)
Individual house	Garden level	34	1.1 ± 0.8	0.2	4.4
	1st floor	23	0.9 ± 0.6	0.2	2.3
Office building	Ground floor	20	1.1 ± 0.6	0.4	2.7
	1st floor	24	1.2 ± 0.9	0.1	3.7
	2nd floor	32	1.7 ± 1	0.6	4.1
	3rd floor	10	1 ± 0.6	0.1	1.8

presents the minimum, the maximum and the mean of the 2D deviations observed for these control points for each floor of the two buildings. Deviations of one millimetre to several centimetres are observed between control points selected in floor plans created manually and automatically. Moreover, for all floors the mean of deviations is lower than 2 cm.

Based on the superimposition of floor plans created manually and automatically, a visual inspection was performed to check if objects correctly describe the buildings. This inspection shows that 93% of objects are correctly placed for the individual house whereas 88% of objects are correctly placed for the office building. The falsely placed objects are mostly portions of façade walls. Figure 15 presents an example of the inspection of reconstructed walls for the 2nd floor of the office building. The floor plan was extracted from a building information model constructed manually based on point clouds. Reconstructed walls and portions of walls are presented in red in the figure and points used for the calculation of the 3D rigid body transformation are in green.

4.3.2. Attribution of Standard Deviations to Reconstructed Walls

For each created wall, a quality index is calculated. It deals with the standard deviation of the unsigned distances between points and the reconstructed wall. Figure 16 presents wall point clouds colourized with the standard deviations associated to walls for one floor of each dataset.

For the individual house, the mean of standard deviations associated to walls or portions of walls is 9 mm whereas for the office building the mean of standard deviations is 8 mm. This means that on average, the walls are reconstructed with a precision of 1 cm. The visualisation of standard deviations thanks to a colour-bar allows locating rapidly walls or portions of walls for which standard deviations are distinguished from those of the other elements. For example, for the garden level of the individual house, an element point cloud is colourized in red and emerges from the other elements (Figure 16a). It deals with a small portion of wall with a standard deviation of 1.9 cm which was likely not correctly reconstructed or which is not really a portion of wall.

Assuming that there is no significant noise in the data, the factors that influence the standard deviation of a wall are the size of the wall, the number of planes composing it and the amount of points which emerges from the wall (objects leaning against walls). A small wall with a high standard deviation indicates a problem occurred during the reconstruction. However, for a long wall composed of several planes belonging to different rooms (typically the case of a corridor wall or an indoor façade wall) the standard deviation seems not be sufficient to conclude about the quality of the reconstruction. The distances between points and model have to be analysed more specifically.

4.3.3. Analysis of Points-to-Model Distances

A second quality index taken into account is the signed distances between points composing an element and the reconstructed element and more specifically the statistical distribution of those distances. Figure 17 proposes two examples of point clouds of respectively a portion of façade wall (Figure 17a) and an indoor wall (Figure 17b). These point clouds are colourized according to the signed distances between points and the reconstructed element. The distributions of the signed distances are shown in the associated histograms representing the number of points related to the signed distances.

Reconstructed walls or portions of walls are based on a set of points which contains inevitably artefacts or objects. It is for example the case, in Figure 17a, where window awnings and a painting emerge from the wall. It is essential that for points which are effectively on the wall, the distances points-to-wall are close to 0. Figure 17b presents an example of a wall very well reconstructed since the highest occurrence frequencies concern distances around 0 and the most part of distances are between −5 mm and 5 mm. For point cloud of Figure 17a, the reconstruction is less satisfying because the most frequent signed distances are located around −5 mm. This is due to the fact that the surface was reconstructed based on two planes located in two different rooms. For the plane located on the right, one can observed that distances are positives for one side and negatives for the other side. This means that the two planes are not utterly coplanar.

5. Future Work

Several future works are planned for our approach. First, the building will be considered in its whole for the 3D reconstruction. Indeed, the 3D reconstruction is performed for each floor individually but some elements of a building are extended to several floors as for example façade walls. Outdoor point clouds will be used to improve the reconstruction of façade walls and to consider the detection of openings. Moreover, the combination of indoor and outdoor models will enable to integrate the building to models at larger scale such as city models.

In parallel to the development of our approach, the potential use of radiometric information of point clouds (colour and intensity) was investigated for the detection of openings. Satisfying results are obtained in condition to dispose of reliable radiometric information. The developed methodologies are for now not applicable to all point clouds. Works on the detection of openings will be deepened. In particular, the fact that façade walls contain windows and the fact that indoor walls contain doors will be exploited.

As describe in Section 4, two quality indexes were defined and can be used to identify if some elements are not correctly reconstructed. In the future, a tool will be developed to automatically interpret these quality indexes in order to propose to the user a manual reconstruction of elements in case of errors. To do that, it is essential to keep the link between point clouds and the as-built BIM. Additionally, tolerances will have to be defined regarding simplification assumptions.

In the context of BIM, it would also be interesting to define a specific acquisition protocol to integrate information which are not available in point clouds as for example ceiling heights when false ceilings are encountered or materials composing building elements. These information could facilitate the reconstruction and semantically enrich the as-built BIM.

One should also notice that acquisition methods are constantly evolving. In this project, data issued from laser scanners were used but, considering the new techniques in photogrammetry and computer vision, point cloud of a building may be obtained by an automatic dense correlation of thousands images. Beside point cloud, the exploitation of images may allow extracting more semantic informations. Moreover, constraints may be imposed during the densification step to optimize the identification of walls, grounds and ceilings for example.

A new trend for the acquisition of building environments is the combined use of several sensors. Multi-sensors systems are increasingly developed. The synchronized data issued from different sensors constitute a significant contribution for the automation of processes and can provide semantic information. For instance, the combination of laser scanning data and data issued from thermal cameras is notably studied in the context of as-built BIM creation.

6. Conclusions

The aim of this article was to present a processing chain conceived for the 3D semi-automatic reconstruction of indoors of existing buildings from point clouds for their integration in BIM software. The developed approach is composed of two parts. Based on indoor point clouds, the first part consists in several segmentations into spaces and planes and in the classification of points into several categories. The second part of the approach deals with the reconstruction of walls and slabs of buildings from the element point clouds extracted in the first part. At the end of the approach, a file in a BIM format is generated and reconstructed walls and slabs can be opened in BIM software.

The approach was assessed based on two datasets which were not used for the development of the approach. The same thresholds were used for both datasets. The results obtained for the first part of the approach are promising in a geometric and a semantic point of view. Indeed, sub-spaces are almost all segmented and points are well classified into the different categories. The second part of the assessment was looked to the reconstruction of building elements. The results are also very satisfying for both datasets since, for individual house and the office building, more than 90% of objects located in the input point clouds were reconstructed automatically. Additionally, percentages of respectively 93% and 88% of walls and portions of walls detected automatically are correctly positioned and deviations from one millimetre to several centimetres are observed in relation to deliverables created manually.

The assessment of the approach demonstrates that the same default thresholds can be applied to several datasets of quite different buildings, in other words that the approach is transferable. Moreover, the high percentages of elements correctly extracted shows that thresholds determined automatically are well determined. Finally, low deviations indicate that the reconstructed elements accurately described the as-is states of the buildings. Thus, the developed approach responds positively to the three items considered namely the transferability, the degree of automation and the geometric quality.

Tools for the inspection of results were also proposed. Standard deviations associated to reconstructed walls and points-to-model distances were proposed as quality indexes. These tools for the quality assessment are essential since a complete automation of reconstruction seems utopian and reconstruction errors can potentially occur. Despite many scan-to-BIM approaches, currently there are very few tools on the market addressing the quality of generated Building Information Models from point clouds.