Geotechnical and safety protective equipment planning using range point cloud data and rule checking in building information modeling

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.

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IFCWALL RECONSTRUCTION FROM UNSTRUCTURED POINT CLOUDS

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-2-33-2018 ◽

2018 ◽

Vol IV-2 ◽

pp. 33-39 ◽

Cited By ~ 4

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.

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BIM RECONSTRUCTION: AUTOMATED PROCEDURAL MODELING FROM POINT CLOUD DATA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-w17-53-2019 ◽

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.

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Point Cloud vs. Mesh Features for Building Interior Classification

Remote Sensing ◽

10.3390/rs12142224 ◽

2020 ◽

Vol 12 (14) ◽

pp. 2224 ◽

Cited By ~ 5

Author(s):

Maarten Bassier ◽

Maarten Vergauwen ◽

Florent Poux

Keyword(s):

Point Cloud ◽

Point Clouds ◽

Information Modeling ◽

Point Cloud Data ◽

3D Mesh ◽

Cloud Data ◽

And Topology ◽

Building Information ◽

Building Components ◽

The Impact

Interpreting 3D point cloud data of the interior and exterior of buildings is essential for automated navigation, interaction and 3D reconstruction. However, the direct exploitation of the geometry is challenging due to inherent obstacles such as noise, occlusions, sparsity or variance in the density. Alternatively, 3D mesh geometries derived from point clouds benefit from preprocessing routines that can surmount these obstacles and potentially result in more refined geometry and topology descriptions. In this article, we provide a rigorous comparison of both geometries for scene interpretation. We present an empirical study on the suitability of both geometries for the feature extraction and classification. More specifically, we study the impact for the retrieval of structural building components in a realistic environment which is a major endeavor in Building Information Modeling (BIM) reconstruction. The study runs on segment-based structuration of both geometries and shows that both achieve recognition rates over 75% F1 score when suitable features are used.

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Modeling and Processing of Smart Point Clouds of Cultural Relics with Complex Geometries

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10090617 ◽

2021 ◽

Vol 10 (9) ◽

pp. 617

Author(s):

Su Yang ◽

Miaole Hou ◽

Ahmed Shaker ◽

Songnian Li

Keyword(s):

Point Cloud ◽

Semantic Information ◽

Point Clouds ◽

Information Modeling ◽

Complex Geometries ◽

3D Point Cloud ◽

Point Cloud Data ◽

Modeling Framework ◽

Cloud Data ◽

Cultural Relics

The digital documentation of cultural relics plays an important role in archiving, protection, and management. In the field of cultural heritage, three-dimensional (3D) point cloud data is effective at expressing complex geometric structures and geometric details on the surface of cultural relics, but lacks semantic information. To elaborate the geometric information of cultural relics and add meaningful semantic information, we propose a modeling and processing method of smart point clouds of cultural relics with complex geometries. An information modeling framework for complex geometric cultural relics was designed based on the concept of smart point clouds, in which 3D point cloud data are organized through the time dimension and different spatial scales indicating different geometric details. The proposed model allows smart point clouds or a subset to be linked with semantic information or related documents. As such, this novel information modeling framework can be used to describe rich semantic information and high-level details of geometry. The proposed information model not only expresses the complex geometric structure of the cultural relics and the geometric details on the surface, but also has rich semantic information, and can even be associated with documents. A case study of the Dazu Thousand-Hand Bodhisattva Statue, which is characterized by a variety of complex geometries, reveals that our proposed framework is capable of modeling and processing the statue with excellent applicability and expansibility. This work provides insights into the sustainable development of cultural heritage protection globally.

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Calculation Method of Complex Tank Capacity Based on Laser Point Cloud and Building Information Modeling Technology

Laser & Optoelectronics Progress ◽

10.3788/lop56.052802 ◽

2019 ◽

Vol 56 (5) ◽

pp. 052802

Author(s):

邬镇伦 Wu Zhenlun ◽

程效军 Cheng Xiaojun ◽

辛佩康 Xin Peikang ◽

张立朔 Zhang Lishuo ◽

胡敏捷 Hu Minjie

Keyword(s):

Calculation Method ◽

Point Cloud ◽

Building Information Modeling ◽

Information Modeling ◽

Modeling Technology ◽

Building Information ◽

Tank Capacity

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From Point Cloud Data to Building Information Modelling: An Automatic Parametric Workflow for Heritage

Remote Sensing ◽

10.3390/rs12071094 ◽

2020 ◽

Vol 12 (7) ◽

pp. 1094 ◽

Cited By ~ 4

Author(s):

Mesrop Andriasyan ◽

Juan Moyano ◽

Juan Enrique Nieto-Julián ◽

Daniel Antón

Keyword(s):

Point Cloud ◽

Laser Scanning ◽

Accuracy Assessment ◽

Parametric Modelling ◽

Point Cloud Data ◽

Building Information Modelling ◽

Historical Interpretation ◽

Information Modelling ◽

Cloud Data ◽

Building Information

Building Information Modelling (BIM) is a globally adapted methodology by government organisations and builders who conceive the integration of the organisation, planning, development and the digital construction model into a single project. In the case of a heritage building, the Historic Building Information Modelling (HBIM) approach is able to cover the comprehensive restoration of the building. In contrast to BIM applied to new buildings, HBIM can address different models which represent either periods of historical interpretation, restoration phases or records of heritage assets over time. Great efforts are currently being made to automatically reconstitute the geometry of cultural heritage elements from data acquisition techniques such as Terrestrial Laser Scanning (TLS) or Structure From Motion (SfM) into BIM (Scan-to-BIM). Hence, this work advances on the parametric modelling from remote sensing point cloud data, which is carried out under the Rhino+Grasshopper-ArchiCAD combination. This workflow enables the automatic conversion of TLS and SFM point cloud data into textured 3D meshes and thus BIM objects to be included in the HBIM project. The accuracy assessment of this workflow yields a standard deviation value of 68.28 pixels, which is lower than other author’s precision but suffices for the automatic HBIM of the case study in this research.

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