scholarly journals Automatic 2D Floorplan CAD Generation from 3D Point Clouds

2020 ◽  
Vol 10 (8) ◽  
pp. 2817 ◽  
Author(s):  
Uuganbayar Gankhuyag ◽  
Ji-Hyeong Han
Keyword(s):  
Point Clouds ◽  
Detection Algorithm ◽  
Information Modeling ◽  
Ongoing Research ◽  
Cloud Data ◽  
Industry Foundation Classes ◽  
Automated Method ◽  
3D Point Clouds ◽  
Building Information ◽  
Geometric Quality

In the architecture, engineering, and construction (AEC) industry, creating an indoor model of existing buildings has been a challenging task since the introduction of building information modeling (BIM). Because the process of BIM is primarily manual and implies a high possibility of error, the automated creation of indoor models remains an ongoing research. In this paper, we propose a fully automated method to generate 2D floorplan computer-aided designs (CADs) from 3D point clouds. The proposed method consists of two main parts. The first is to detect planes in buildings, such as walls, floors, and ceilings, from unstructured 3D point clouds and to classify them based on the Manhattan-World (MW) assumption. The second is to generate 3D BIM in the industry foundation classes (IFC) format and a 2D floorplan CAD using the proposed line-detection algorithm. We experimented the proposed method on 3D point cloud data from a university building, residential houses, and apartments and evaluated the geometric quality of a wall reconstruction. We also offer the source code for the proposed method on GitHub.

scholarly journals Clustering of Wall Geometry from Unstructured Point Clouds Using Conditional Random Fields

2019 ◽  
Vol 11 (13) ◽  
pp. 1586 ◽  
Author(s):  
Maarten Bassier ◽  
Maarten Vergauwen
Keyword(s):  
Conditional Random Fields ◽  
Conditional Random Field ◽  
Region Growing ◽  
Point Clouds ◽  
Information Modeling ◽  
Ongoing Research ◽  
Cloud Data ◽  
Building Information ◽  
Wall Geometry ◽  
Balanced Clustering

The automated reconstruction of Building Information Modeling (BIM) objects from point cloud data is still subject of ongoing research. A vital step in the process is identifying the observations for each wall object. Given a set of segmented and classified point clouds, the labeled segments should be clustered according to their respective objects. The current processes to perform this task are sensitive to noise, occlusions, and the associativity between faces of neighboring objects. The proper retrieval of the observed geometry is especially important for wall geometry as it forms the basis for further BIM reconstruction. In this work, a method is presented to automatically group wall segments derived from point clouds according to the proper walls of a building. More specifically, a Conditional Random Field is employed that evaluates the context of each wall segment in order to determine which wall it belongs to. First, a set of classified planar primitives is obtained through algorithms developed in prior work. Next, both local and contextual features are extracted based on the nearest neighbors and a number of seeds that are heuristically determined. The final wall clusters are then computed by decoding the graph. The method is tested on our own data as well as the 2D-3D-Semantics (2D-3D-S) benchmark data of Stanford. Compared to a conventional region growing method, the proposed method reduces the rate of false positives, resulting in better wall clusters. Overall, the method computes a more balanced clustering of the observations. A key advantage of the proposed method is its capability to deal with wall geometry in complex configurations in multi-storey buildings opposed to the presented methods in current literature.

scholarly journals IFCWALL RECONSTRUCTION FROM UNSTRUCTURED POINT CLOUDS

2018 ◽  
Vol IV-2 ◽  
pp. 33-39 ◽  
Author(s):  
M. Bassier ◽  
R. Klein ◽  
B. Van Genechten ◽  
M. Vergauwen
Keyword(s):  
Point Cloud ◽  
Input Data ◽  
Initial Point ◽  
Point Clouds ◽  
Information Modeling ◽  
Point Cloud Data ◽  
Ongoing Research ◽  
Cloud Data ◽  
Building Information ◽  
Wall Geometry

The automated reconstruction of Building Information Modeling (BIM) objects from point cloud data is still ongoing research. A key aspect is the creation of accurate wall geometry as it forms the basis for further reconstruction of objects in a BIM. After segmenting and classifying the initial point cloud, the labelled segments are processed and the wall topology is reconstructed. However, the preocedure is challenging due to noise, occlusions and the complexity of the input data.<br>In this work, a method is presented to automatically reconstruct consistent wall geometry from point clouds. More specifically, the use of room information is proposed to aid the wall topology creation. First, a set of partial walls is constructed based on classified planar primitives. Next, the rooms are identified using the retrieved wall information along with the floors and ceilings. The wall topology is computed by the intersection of the partial walls conditioned on the room information. The final wall geometry is defined by creating IfcWallStandardCase objects conform the IFC4 standard. The result is a set of walls according to the as-built conditions of a building. The experiments prove that the used method is a reliable framework for wall reconstruction from unstructured point cloud data. Also, the implementation of room information reduces the rate of false positives for the wall topology. Given the walls, ceilings and floors, 94% of the rooms is correctly identified. A key advantage of the proposed method is that it deals with complex rooms and is not bound to single storeys.

scholarly journals SEMI-AUTOMATED TOPOLOGY ADJUSTMENT OF PARTIAL WALL GEOMETRY

2019 ◽  
Vol XLII-5/W3 ◽  
pp. 13-19
Author(s):  
M. Bassier ◽  
M. Vergauwen
Keyword(s):  
Information Modeling ◽  
Ongoing Research ◽  
Cloud Data ◽  
Automated Method ◽  
Building Geometry ◽  
Building Information ◽  
Evaluation Algorithm ◽  
Novel Method ◽  
The Subject ◽  
Wall Geometry

Abstract. The reconstruction of Building Information Modeling objects for as-built modeling is currently the subject of ongoing research. A popular method is to extract building information from point cloud data to create a set of parametric objects. The automation of this process is highly desired by the industry but is currently hindered by occlusions, clutter and the complexity of the building geometry. To create an as-built BIM, it is vital to not only accurately reconstruct the building’s structure but also to compute the topology between the objects. More specifically, we target the topology of the reconstructed partial wall geometry as this forms the basis for other objects.In this work, a novel method is presented to automatically adjust the topology of wall geometry in an as-built BIM. We present a semi-automated method that procedurally evaluates the configuration of reconstructed objects and adjusts them to create a more faithful BIM. A wall connection evaluation algorithm is proposed that takes as input the centrelines of partial wall geometry and a set of floor and ceilings mesh segments and outputs the topologically adjusted objects. The method is tested on a variety of scenes and shows promising results to reliably compute the topology of as-built models. The generated geometry is similar to the geometric modification proposed by expert modelers. A key advantage is that the algorithm operates directly in Revit and Rhino and can be used for new models as well as for updating existing models.

scholarly journals BIM Supported Surveying and Imaging Combination for Heritage Conservation

2021 ◽  
Vol 13 (8) ◽  
pp. 1584
Author(s):  
Pedro Martín-Lerones ◽  
David Olmedo ◽  
Ana López-Vidal ◽  
Jaime Gómez-García-Bermejo ◽  
Eduardo Zalama
Keyword(s):  
Laser Scanning ◽  
Point Clouds ◽  
Cloud Data ◽  
Industry Foundation Classes ◽  
3D Point Clouds ◽  
Conservation Actions ◽  
Heritage Building ◽  
2D Imaging ◽  
Digital Survey ◽  
Level 2

As the basis for analysis and management of heritage assets, 3D laser scanning and photogrammetric 3D reconstruction have been probed as adequate techniques for point cloud data acquisition. The European Directive 2014/24/EU imposes BIM Level 2 for government centrally procured projects as a collaborative process of producing federated discipline-specific models. Although BIM software resources are intensified and increasingly growing, distinct specifications for heritage (H-BIM) are essential to driving particular processes and tools to efficiency shifting from point clouds to meaningful information ready to be exchanged using non-proprietary formats, such as Industry Foundation Classes (IFC). This paper details a procedure for processing enriched 3D point clouds into the REVIT software package due to its worldwide popularity and how closely it integrates with the BIM concept. The procedure will be additionally supported by a tailored plug-in to make high-quality 3D digital survey datasets usable together with 2D imaging, enhancing the capability to depict contextualized important graphical data to properly planning conservation actions. As a practical example, a 2D/3D enhanced combination is worked to accurately include into a BIM project, the length, orientation, and width of a big crack on the walls of the Castle of Torrelobatón (Spain) as a representative heritage building.

scholarly journals Topology Reconstruction of BIM Wall Objects from Point Cloud Data

2020 ◽  
Vol 12 (11) ◽  
pp. 1800 ◽  
Author(s):  
Maarten Bassier ◽  
Maarten Vergauwen
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Evaluation Framework ◽  
Information Modeling ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Current State ◽  
Building Information ◽  
Popular Subject ◽  
Best Fit

The processing of remote sensing measurements to Building Information Modeling (BIM) is a popular subject in current literature. An important step in the process is the enrichment of the geometry with the topology of the wall observations to create a logical model. However, this remains an unsolved task as methods struggle to deal with the noise, incompleteness and the complexity of point cloud data of building scenes. Current methods impose severe abstractions such as Manhattan-world assumptions and single-story procedures to overcome these obstacles, but as a result, a general data processing approach is still missing. In this paper, we propose a method that solves these shortcomings and creates a logical BIM model in an unsupervised manner. More specifically, we propose a connection evaluation framework that takes as input a set of preprocessed point clouds of a building’s wall observations and compute the best fit topology between them. We transcend the current state of the art by processing point clouds of both straight, curved and polyline-based walls. Also, we consider multiple connection types in a novel reasoning framework that decides which operations are best fit to reconstruct the topology of the walls. The geometry and topology produced by our method is directly usable by BIM processes as it is structured conform the IFC data structure. The experimental results conducted on the Stanford 2D-3D-Semantics dataset (2D-3D-S) show that the proposed method is a promising framework to reconstruct complex multi-story wall elements in an unsupervised manner.

scholarly journals CLUSTERING OF WALL GEOMETRY FROM UNSTRUCTURED POINT CLOUDS

2019 ◽  
Vol XLII-2/W9 ◽  
pp. 101-108 ◽  
Author(s):  
M. Bassier ◽  
M. Vergauwen
Keyword(s):  
Point Cloud ◽  
Conditional Random Field ◽  
Initial Point ◽  
Point Clouds ◽  
The Body ◽  
Information Modeling ◽  
Point Cloud Data ◽  
Ongoing Research ◽  
Cloud Data ◽  
Wall Geometry

<p><strong>Abstract.</strong> The automated reconstruction of Building Information Modeling (BIM) objects from point cloud data is still ongoing research. A key aspect is retrieving the proper observations for each object. After segmenting and classifying the initial point cloud, the labeled segments should be clustered according to their respective objects. However, this procedure is challenging due to noise, occlusions and the associativity between different objects. This is especially important for wall geometry as it forms the basis for further BIM reconstruction.</p><p> In this work, a method is presented to automatically group wall segments derived from point clouds according to the proper walls of a building. More specifically, a Conditional Random Field is employed that evaluates the context of each observation in order to determine which wall it belongs too. The emphasis is on the clustering of highly associative walls as this topic currently is a gap in the body of knowledge. First a set of classified planar primitives is obtained using algorithms developed in prior work. Next, both local and contextual features are extracted based on the nearest neighbors and a number of seeds that are heuristically determined. The final wall clusters are then computed by decoding the graph and thus the most likely configuration of the observations. The experiments prove that the used method is a promising framework for wall clustering from unstructured point cloud data. Compared to a conventional region growing method, the proposed method significantly reduces the rate of false positives, resulting in better wall clusters. A key advantage of the proposed method is its capability of dealing with complex wall geometry in entire buildings opposed to the presented methods in current literature.</p>

scholarly journals BIM RECONSTRUCTION: AUTOMATED PROCEDURAL MODELING FROM POINT CLOUD DATA

2019 ◽  
Vol XLII-2/W17 ◽  
pp. 53-60
Author(s):  
M. Bassier ◽  
L. Mattheuwsen ◽  
M. Vergauwen
Keyword(s):  
Point Cloud ◽  
Reconstruction Algorithm ◽  
Information Modeling ◽  
Data Sets ◽  
Point Cloud Data ◽  
Ongoing Research ◽  
Cloud Data ◽  
Structure Information ◽  
Wide Range ◽  
Building Information

Abstract. The reconstruction of Building Information Modeling objects for as-built modeling is currently the subject of ongoing research. A popular method is to extract structure information from point cloud data to create a set of parametric objects. This requires the interpretation of the point cloud data which currently is a manual and labor intensive procedure. Automated processes have to cope with excessive occlusions and clutter in the data sets. To create an as-built BIM, it is vital to reconstruct the building’s structure i.e. wall geometry prior to the reconstruction of other objects.In this work, a novel method is presented to automatically reconstruct as-built BIM for generic buildings. We presented an unsupervised method that procedurally models the geometry of the walls based on point cloud data. A bottom-up process is defined where consecutively higher level information is extracted from the point cloud data using pre-trained machine learning models. Prior to the reconstruction, the data is segmented, classified and clustered to retrieve all the available observations of the walls. The resulting geometry is processed by the reconstruction algorithm. First, the necessary information is extracted from the observations for the creation of parametric solid objects. Subsequently, the final walls are created by updating their topology. The method is tested on a variety of scenes and shows promising results to reliably and accurately create as-built models. The accuracy of the generated geometry is similar to the precision of expert modelers. A key advantage is that that the algorithm creates Revit and Rhino native objects which makes the geometry directly applicable to a wide range of applications.

scholarly journals Sensor Data Visualization for Indoor Point Clouds

2019 ◽  
Vol 2 ◽  
pp. 1-8
Author(s):  
Vladeta Stojanovic ◽  
Matthias Trapp ◽  
Benjamin Hagedorn ◽  
Jan Klimke ◽  
Rico Richter ◽  
...  
Keyword(s):  
General System ◽  
Point Clouds ◽  
Facility Management ◽  
Information Modeling ◽  
Sensor Data ◽  
Cloud Data ◽  
Digital Representations ◽  
Automation Systems ◽  
Building Information ◽  
Occupant Comfort

Abstract. Integration and analysis of real-time and historic sensor data provides important insights into the operational status of buildings. There is a need for the integration of sensor data and digital representations of the built environment for furthering stakeholder engagement within the realms of Real Estate 4.0 and Facility Management (FM), especially in a spatial representation context. In this paper, we propose a general system architecture that integrates point cloud data and sensor data for visualization and analysis. We further present a prototypical web-based implementation of that architecture and demonstrate its application for the integration and visualization of sensor data from a typical office building, with the aim to communicate and analyze occupant comfort. The empirical results obtained from our prototypical implementation demonstrate the feasibility of our approach for the provisioning of light-weight software components for the service-oriented integration of Building Information Modeling (BIM), Building Automation Systems (BASs), Integrated Workplace Management Systems (IWMSs), and future Digital Twin (DT) platforms.

scholarly journals 3D Point Cloud to BIM: Semi-Automated Framework to Define IFC Alignment Entities from MLS-Acquired LiDAR Data of Highway Roads

2020 ◽  
Vol 12 (14) ◽  
pp. 2301
Author(s):  
Mario Soilán ◽  
Andrés Justo ◽  
Ana Sánchez-Rodríguez ◽  
Belén Riveiro
Keyword(s):  
Information Exchange ◽  
Point Cloud ◽  
Laser Scanning ◽  
Information Modeling ◽  
Cloud Data ◽  
Industry Foundation Classes ◽  
Cloud Processing ◽  
Building Information ◽  
Definition Of ◽  
Key Steps

Building information modeling (BIM) is a process that has shown great potential in the building industry, but it has not reached the same level of maturity for transportation infrastructure. There is a standardization need for information exchange and management processes in the infrastructure that integrates BIM and Geographic Information Systems (GIS). Currently, the Industry Foundation Classes standard has harmonized different infrastructures under the Industry Foundation Classes (IFC) 4.3 release. Furthermore, the usage of remote sensing technologies such as laser scanning for infrastructure monitoring is becoming more common. This paper presents a semi-automated framework that takes as input a raw point cloud from a mobile mapping system, and outputs an IFC-compliant file that models the alignment and the centreline of each road lane in a highway road. The point cloud processing methodology is validated for two of its key steps, namely road marking processing and alignment and road line extraction, and a UML diagram is designed for the definition of the alignment entity from the point cloud data.

scholarly journals A geometric deep learning approach for checking element-to-entity mappings in infrastructure building information models

2020 ◽  
Author(s):  
Bonsang Koo ◽  
Raekyu Jung ◽  
Youngsu Yu ◽  
Inhan Kim
Keyword(s):  
Deep Learning ◽  
Point Clouds ◽  
3D Models ◽  
Classification Performance ◽  
Information Modeling ◽  
Data Interoperability ◽  
Industry Foundation Classes ◽  
Domain Specific ◽  
Information Models ◽  
Building Information

Abstract Data interoperability between domain-specific applications is a key prerequisite for building information modeling (BIM) to solidify its position as a central medium for collaboration and information sharing in the construction industry. The Industry Foundation Classes (IFC) provides an open and neutral data format to standardize data exchanges in BIM, but is often exposed to data loss and misclassifications. Concretely, errors in mappings between BIM elements and IFC entities may occur due to manual omissions or the lack of awareness of the IFC schema itself, which is broadly defined and highly complex. This study explored the use of geometric deep learning models to classify infrastructure BIM elements, with the ultimate goal of automating the prechecking of BIM-to-IFC mappings. Two models with proven classification performance, Multi-View Convolutional Neural Network (MVCNN) and PointNet, were trained and tested to classify 10 types of commonly used BIM elements in road infrastructure, using a dataset of 1496 3D models. Results revealed MVCNN as the superior model with ACC and F1 score values of 0.98 and 0.98, compared with PointNet's corresponding values of 0.83 and 0.87, respectively. MVCNN, which employs multiple images to learn the features of a 3D artifact, was able to discern subtle differences in their shapes and geometry. PointNet seems to lose the granularity of the shapes, as it uses points partially selected from point clouds.

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