3D structural component recognition and modeling method using color and 3D data for construction progress monitoring

Son, Hyojoo; Kim, Chang-Wan

doi:10.1016/j.autcon.2010.03.003

Cited by 122 publications

(52 citation statements)

References 26 publications

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“…In contrast to a point cloud received from a laser scan a scale has to be introduced to a photogrammetric point cloud. A possibility is to use a stereo-camera system, as done in (Son and Kim, 2010), or . (Rashidi et al, 2014) propose to use a colored cube with known size as target, which can be automatically measured to determine the scale.…”

Section: Related Workmentioning

confidence: 99%

Validation of Bim Components by Photogrammetric Point Clouds for Construction Site Monitoring

Tuttas¹,

Braun²,

Borrmann³

et al. 2015

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Construction progress monitoring is a primarily manual and time consuming process which is usually based on 2D plans and therefore has a need for an increased automation. In this paper an approach is introduced for comparing a planned state of a building (as-planned) derived from a Building Information Model (BIM) to a photogrammetric point cloud (as-built). In order to accomplish the comparison a triangle-based representation of the building model is used. The approach has two main processing steps. First, visibility checks are performed to determine whether or not elements of the building are potentially built. The remaining parts can be either categorized as free areas, which are definitely not built, or as unknown areas, which are not visible. In the second step it is determined if the potentially built parts can be confirmed by the surrounding points. This process begins by splitting each triangle into small raster cells. For each raster cell a measure is calculated using three criteria: the mean distance of the points, their standard deviation and the deviation from a local plane fit. A triangle is confirmed if a sufficient number of raster cells yield a high rating by the measure. The approach is tested based on a real case inner city scenario. Only triangles showing unambiguous results are labeled with their statuses, because it is intended to use these results to infer additional statements based on dependencies modeled in the BIM. It is shown that the label built is reliable and can be used for further analysis. As a drawback this comes with a high percentage of ambiguously classified elements, for which the acquired data is not sufficient (in terms of coverage and/or accuracy) for validation.

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Section: Related Workmentioning

confidence: 99%

Validation of Bim Components by Photogrammetric Point Clouds for Construction Site Monitoring

Tuttas¹,

Braun²,

Borrmann³

et al. 2015

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…For correct scaling of the point cloud, stereo-camera systems can be used, as done in [13], [14] and [15]. [16] proposes to use a colored cube with known size as target, which can be automatically measured to determine the scale.…”

Section: Monitoringmentioning

confidence: 99%

Automated Progress Monitoring Based on Photogrammetric Point Clouds and Precedence Relationship Graphs

Braun¹,

Tuttas²,

Borrmann³

et al. 2015

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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Construction progress monitoring is an essential but time-consuming work on all construction sites. This research introduces a method to facilitate the asplanned versus as-built comparison through image based monitoring. A dense point cloud is reconstructed from the images that is compared to an existing 4D building information model (BIM). However, due to the numerous obstructions found on a construction site, only a minority of building elements can be detected directly. In this paper, we discuss how the detection results are significantly refined and enriched by using additional spatial and temporal information gained from the 4D BIM. In this regard, a precedence relationship graph is derived which helps to identify occluded elements and enhance the detection algorithm.

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“…Few research studies have been conducted to propose methods for recognizing semantic elements in buildings during the construction phase [5][6][7][8][9][10][11] as well as during occupancy [1,2,4,12,13]. These research studies performed as-built data acquisition using spatial survey technologies that are based on photogrammetry [8] and laser scanning [5,6,1,7,9,2,4,12,13].…”

Section: Introductionmentioning

confidence: 99%

“…These research studies performed as-built data acquisition using spatial survey technologies that are based on photogrammetry [8] and laser scanning [5,6,1,7,9,2,4,12,13]. From the acquired as-built data represented in the point cloud format, research studies that focused on the construction phase proposed methods to recognize and model the structural elements by utilizing the as-planned model [5][6][7][8][9]. However, such a scan-to-BIM approach can be generally not effective where discrepancies are observed in the as-built and as-planned conditions.…”

Section: Introductionmentioning

confidence: 99%

Semantic AsBuilt 3D Modeling of Buildings Under Construction from Laser-Scan Data Based on Local Convexity without an As-Planned Model

Son¹,

Na²,

Kim³

2015

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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As-built building information models (BIMs) need to be represented with the composing building elements having their own functional semantic information. In order to generate such as-built BIMs with composing building elements, it is necessary to recognize each of the semantic elements and separate them from each other. In addition, the as-built BIM needs to be generated and updated during or after each major construction activity because most of the structural elements and nonstructural elements (especially for mechanical, electrical, and plumbing (MEP) components) are covered by other objects as the construction phase progresses. This study aims to propose a method for generating a semantic as-built BIM from laser-scan data acquired during the construction phase, especially for structural works. A field experiment was performed to validate the proposed method by acquiring and processing laserscan data from the construction site. The experimental result shows that the proposed method can be used for semantic as-built BIMs without any prior information from as-planned models.

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3D structural component recognition and modeling method using color and 3D data for construction progress monitoring

Cited by 122 publications

References 26 publications

Validation of Bim Components by Photogrammetric Point Clouds for Construction Site Monitoring

Validation of Bim Components by Photogrammetric Point Clouds for Construction Site Monitoring

Automated Progress Monitoring Based on Photogrammetric Point Clouds and Precedence Relationship Graphs

Semantic AsBuilt 3D Modeling of Buildings Under Construction from Laser-Scan Data Based on Local Convexity without an As-Planned Model

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