Automatic reconstruction of as-built building information models from laser-scanned point clouds: A review of related techniques

doi:10.1016/J.AUTCON.2010.06.007

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Automatic reconstruction of as-built building information models from laser-scanned point clouds: A review of related techniques

Journal Article•DOI•

Automatic reconstruction of as-built building information models from laser-scanned point clouds: A review of related techniques

Pingbo Tang¹, Daniel Huber², Burcu Akinci², Robert R. Lipman³, Alan M. Lytle³ - Show less +1 more•Institutions (3)

Western Michigan University¹, Carnegie Mellon University², National Institute of Standards and Technology³

01 Nov 2010-Automation in Construction (Elsevier)-Vol. 19, Iss: 7, pp 829-843

TL;DR: This article surveys techniques developed in civil engineering and computer science that can be utilized to automate the process of creating as-built BIMs and outlines the main methods used by these algorithms for representing knowledge about shape, identity, and relationships.

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About: This article is published in Automation in Construction.The article was published on 2010-11-01. It has received 789 citations till now. The article focuses on the topics: Information model & Computer Aided Design.

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Journal Article•DOI•

Applications of 3D point cloud data in the construction industry: A fifteen-year review from 2004 to 2018

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Qian Wang¹, Minkoo Kim²•Institutions (2)

National University of Singapore¹, Hong Kong Polytechnic University²

01 Jan 2019-Advanced Engineering Informatics

TL;DR: A thorough review on the applications of 3D point cloud data in the construction industry and to provide recommendations on future research directions in this area is provided.

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203 citations

Journal Article•DOI•

Productive modeling for development of as-built BIM of existing indoor structures

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Jaehoon Jung¹, Sungchul Hong¹, Seongsu Jeong², Sangmin Kim¹, Hyoungsig Cho¹, Seunghwan Hong¹, Joon Heo¹ - Show less +3 more•Institutions (2)

Yonsei University¹, Ohio State University²

01 Jun 2014-Automation in Construction

TL;DR: In this paper, a semi-automatic methodology for improved productivity of as-built building information model (BIM) creation with respect to large and complex indoor environments is proposed, which produces 3D geometric drawings through three steps: segmentation for plane extraction, refinement for removal of noisy points, and boundary tracing for outline extraction.

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198 citations

Cites methods from "Automatic reconstruction of as-buil..."

...The performancemetrics in the present studywere not evaluated, simply because no standard evaluation metrics have yet been established for as-built BIM creation [4,41]....
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...As-built BIM creation for existing structures involves three steps: first, geometrical modeling of components, second, attribution of categories and material properties to the components, and third, establishment of the relations between them [28,41]....
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...scanners; 2) data preprocessing, in which the point cloud data are filtered and registered as a single point cloud in a common coordinate system; 3) geometric modeling, in which the 3D building components are reconstructed as a simplified representative 3D shape, and 4) creation of the BIM, in which the low-level surface model is transformed into a semantically rich BIM by assigning an object category,material properties, and topological relationships between objects [41]....
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Journal Article•DOI•

Mapping the knowledge domains of Building Information Modeling (BIM): A bibliometric approach

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Xiao Li¹, Peng Wu², Geoffrey Qiping Shen¹, Xiangyu Wang², Yue Teng³ - Show less +1 more•Institutions (3)

Hong Kong Polytechnic University¹, Curtin University², University of Hong Kong³

01 Dec 2017-Automation in Construction

TL;DR: An objective and accurate summary of BIM knowledge is provided using 1874 published BIM-related papers and shows that 60 key research areas, such as information systems, 3D modeling, design and sustainability and 10 key research clusters are extremely important for the development of B IM knowledge.

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197 citations

Cites background from "Automatic reconstruction of as-buil..."

...In order to uncover the hidden connections of scientific literature, many studies have been conducted to review the past development and propose new research trend of BIM (see Jung and Joo, 2011; Cerovsek, 2011; Tang et al. 2010)....
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...Tang et al. (2010) conducted reviews to survey the adoption of laser-scanned point clouds for BIMs creation and found that filling the gaps among existing promising techniques and algorithms could become a foundamental burst for automated as-built BIM creation....
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...…safety (Zhang and Hu, 2011; Feng et al., 2015), space (Isikdag et al., 2008), workflow (Sacks et al., 2009), risk (Chien et al., 2014), model (Tang et al., 2010), facility (Kang and Hong, 2015), supply chain (Irizarry et al., 2013), investment (Giel and Issa, 2011) and stakeholders (Succar,…...
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Journal Article•DOI•

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

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H. Macher, Tania Landes, Pierre Grussenmeyer

12 Oct 2017-Applied Sciences

TL;DR: In this paper, a semi-automatic approach is presented for the 3D reconstruction of indoor of existing buildings from point clouds, where several segmentations are performed so that point clouds corresponding to grounds, ceilings and walls are extracted.

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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.

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193 citations

Cites background or methods from "Automatic reconstruction of as-buil..."

...[9], research about recognition of BIM specific components (walls, windows, doors, etc....
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...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]....
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...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....
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...The chosen approach depends on the considered elements which are either parametric representations or non-parametric representations [9]....
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Journal Article•DOI•

BIM-enabled facilities operation and maintenance: A review

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Xinghua Gao¹, Pardis Pishdad-Bozorgi¹•Institutions (1)

Georgia Institute of Technology¹

01 Jan 2019-Advanced Engineering Informatics

TL;DR: The content analysis results show that research on BIM for O&M is still in its early stage and most of the current research has focused on energy management, and adopting the National Institute of Standards and Technology's Cyber-Physical Systems (CPS) Framework is a potential starting point to address this issue.

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183 citations

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Abstract: A new paradigm, Random Sample Consensus (RANSAC), for fitting a model to experimental data is introduced. RANSAC is capable of interpreting/smoothing data containing a significant percentage of gross errors, and is thus ideally suited for applications in automated image analysis where interpretation is based on the data provided by error-prone feature detectors. A major portion of this paper describes the application of RANSAC to the Location Determination Problem (LDP): Given an image depicting a set of landmarks with known locations, determine that point in space from which the image was obtained. In response to a RANSAC requirement, new results are derived on the minimum number of landmarks needed to obtain a solution, and algorithms are presented for computing these minimum-landmark solutions in closed form. These results provide the basis for an automatic system that can solve the LDP under difficult viewing

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09 Dec 2001-International Journal of Computer Vision

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Abstract: Stereo matching is one of the most active research areas in computer vision. While a large number of algorithms for stereo correspondence have been developed, relatively little work has been done on characterizing their performance. In this paper, we present a taxonomy of dense, two-frame stereo methods designed to assess the different components and design decisions made in individual stereo algorithms. Using this taxonomy, we compare existing stereo methods and present experiments evaluating the performance of many different variants. In order to establish a common software platform and a collection of data sets for easy evaluation, we have designed a stand-alone, flexible C++ implementation that enables the evaluation of individual components and that can be easily extended to include new algorithms. We have also produced several new multiframe stereo data sets with ground truth, and are making both the code and data sets available on the Web.

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"Automatic reconstruction of as-buil..." refers background in this paper

...In other fields, such as computer vision, standard test sets and performance metrics have been established [72,83], but no standard evaluation metrics have been established for as-built BIM creation as yet....
[...]

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Irving Biederman

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TL;DR: Recognition-by-components (RBC) provides a principled account of the heretofore undecided relation between the classic principles of perceptual organization and pattern recognition.

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Abstract: The perceptual recognition of objects is conceptualized to be a process in which the image of the input is segmented at regions of deep concavity into an arrangement of simple geometric components, such as blocks, cylinders, wedges, and cones. The fundamental assumption of the proposed theory, recognition-by-components (RBC), is that a modest set of generalized-cone components, called geons (N £ 36), can be derived from contrasts of five readily detectable properties of edges in a two-dimensiona l image: curvature, collinearity, symmetry, parallelism, and cotermination. The detection of these properties is generally invariant over viewing position an$ image quality and consequently allows robust object perception when the image is projected from a novel viewpoint or is degraded. RBC thus provides a principled account of the heretofore undecided relation between the classic principles of perceptual organization and pattern recognition: The constraints toward regularization (Pragnanz) characterize not the complete object but the object's components. Representational power derives from an allowance of free combinations of the geons. A Principle of Componential Recovery can account for the major phenomena of object recognition: If an arrangement of two or three geons can be recovered from the input, objects can be quickly recognized even when they are occluded, novel, rotated in depth, or extensively degraded. The results from experiments on the perception of briefly presented pictures by human observers provide empirical support for the theory. Any single object can project an infinity of image configurations to the retina. The orientation of the object to the viewer can vary continuously, each giving rise to a different two-dimensional projection. The object can be occluded by other objects or texture fields, as when viewed behind foliage. The object need not be presented as a full-colored textured image but instead can be a simplified line drawing. Moreover, the object can even be missing some of its parts or be a novel exemplar of its particular category. But it is only with rare exceptions that an image fails to be rapidly and readily classified, either as an instance of a familiar object category or as an instance that cannot be so classified (itself a form of classification).

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"Automatic reconstruction of as-buil..." refers background in this paper

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The FERET evaluation methodology for face-recognition algorithms

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P.J. Phillips, Hyeonjoon Moon, Syed A. Rizvi¹, Patrick J. Rauss²•Institutions (2)

College of Staten Island¹, United States Army Research Laboratory²

01 Oct 2000-IEEE Transactions on Pattern Analysis and Machine Intelligence

TL;DR: Two of the most critical requirements in support of producing reliable face-recognition systems are a large database of facial images and a testing procedure to evaluate systems.

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Abstract: Two of the most critical requirements in support of producing reliable face-recognition systems are a large database of facial images and a testing procedure to evaluate systems. The Face Recognition Technology (FERET) program has addressed both issues through the FERET database of facial images and the establishment of the FERET tests. To date, 14,126 images from 1,199 individuals are included in the FERET database, which is divided into development and sequestered portions of the database. In September 1996, the FERET program administered the third in a series of FERET face-recognition tests. The primary objectives of the third test were to 1) assess the state of the art, 2) identify future areas of research, and 3) measure algorithm performance.

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4,816 citations

Proceedings Article•DOI•

A volumetric method for building complex models from range images

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Brian Curless¹, Marc Levoy¹•Institutions (1)

Stanford University¹

01 Aug 1996

TL;DR: This paper presents a volumetric method for integrating range images that is able to integrate a large number of range images yielding seamless, high-detail models of up to 2.6 million triangles.

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Abstract: A number of techniques have been developed for reconstructing surfaces by integrating groups of aligned range images. A desirable set of properties for such algorithms includes: incremental updating, representation of directional uncertainty, the ability to fill gaps in the reconstruction, and robustness in the presence of outliers. Prior algorithms possess subsets of these properties. In this paper, we present a volumetric method for integrating range images that possesses all of these properties. Our volumetric representation consists of a cumulative weighted signed distance function. Working with one range image at a time, we first scan-convert it to a distance function, then combine this with the data already acquired using a simple additive scheme. To achieve space efficiency, we employ a run-length encoding of the volume. To achieve time efficiency, we resample the range image to align with the voxel grid and traverse the range and voxel scanlines synchronously. We generate the final manifold by extracting an isosurface from the volumetric grid. We show that under certain assumptions, this isosurface is optimal in the least squares sense. To fill gaps in the model, we tessellate over the boundaries between regions seen to be empty and regions never observed. Using this method, we are able to integrate a large number of range images (as many as 70) yielding seamless, high-detail models of up to 2.6 million triangles.

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3,282 citations