Point cloud quality requirements for Scan-vs-BIM based automated construction progress monitoring

Rebolj, Danijel; Babič, Nenad Čuš; Bizjak, Marko; Mongus, Domen

doi:10.1016/j.autcon.2017.09.021

Cited by 140 publications

(67 citation statements)

References 27 publications

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“…After the identification of the required information, the required scan data quality that can fulfil these information requirements should be determined. The parameters for the scan data or point cloud data quality have been discussed in previous research and industry guidelines [35]. For example, the U.S. General Services Administration (GSA) BIM Guide for 3D Imaging defined four different levels of detail for point cloud data.…”

Section: Parameters For Scan Data Qualitymentioning

confidence: 99%

“…Here, the completeness was measured as the percentage of coverage of point cloud data, which was calculated as the ratio between the number of covered regions and the total number of regions. In addition, Rebolj et al [35] adopted the minimum local density, the minimum local accuracy, and level of scatter as parameters for measuring the quality of point cloud data.…”

Section: Parameters For Scan Data Qualitymentioning

confidence: 99%

“…In this case, the deviation of 11 mm, which actually exceeds the tolerance of 10 mm, has a 40% chance of being modelled as less than the tolerance 10 mm by mistake. One recent study also quantitatively investigated the relationship between point cloud quality (which is closely related to modelling accuracy) and the success of identifying building elements for construction progress tracking [35]. Three criteria were defined using a statistical method to measure point cloud quality and experiments showed that the defined quality criteria can be applied to decide the appropriate scanning methods for successful construction progress tracking.…”

Section: Information Requirements For Bim Applicationsmentioning

confidence: 99%

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An Application Oriented Scan-to-BIM Framework

2019

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Building information modelling (BIM) has been adopted in the construction industry. The success of BIM implementation relies on the accurate building information stored in BIM models. However, building information in BIM models can be inaccurate, out-of-date, or missing in real-world projects. 3D laser scanning has been leveraged to capture the accurate as-is conditions of buildings and create as-is BIM models of buildings; this is known as the scan-to-BIM process. Although industry practitioners and researchers have implemented and studied the scan-to-BIM process, there is no framework that systematically defines and discusses the key steps and considerations in the process. This study proposes an application-oriented framework for scan-to-BIM, which describes the four major steps of a scan-to-BIM process and their relationships. The framework is oriented towards the specific BIM application to be implemented using the created as-is BIM, and includes four steps: (1) identification of information requirements, (2) determination of required scan data quality, (3) scan data acquisition, and (4) as-is BIM reconstruction. Two illustrative examples are provided to demonstrate the feasibility of the proposed scan-to-BIM framework. Furthermore, future research directions within the scan-to-BIM framework are suggested.

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Section: Parameters For Scan Data Qualitymentioning

confidence: 99%

Section: Parameters For Scan Data Qualitymentioning

confidence: 99%

Section: Information Requirements For Bim Applicationsmentioning

confidence: 99%

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An Application Oriented Scan-to-BIM Framework

2019

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“…This includes relocating the objects and adjusting the dimensions (Gao et al, 2015). This procedure is also referred to as Scan-vs-BIM (Bosché et al, 2014, Rebolj et al, 2017. Careful attention should be given to the modeling specifications since deviations acquired from the quality control can only be determined up to the accuracy and density of the point cloud.…”

Section: Percentage Of Completion (Poc)mentioning

confidence: 99%

Percentage of Completion of in-Situ Cast Concrete Walls Using Point Cloud Data and Bim

Bassier

Vincke

Mattheuwsen

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Progress monitoring of construction sites is becoming increasingly popular in the construction industry. Especially with the integration of 4D BIM, the progression and quality of the construction process can be better quantified. A key aspect is the detection of the changes between consecutive epochs of measurements on the site. However, the development of automated procedures is challenging due to noise, occlusions and the associativity between different objects. Additionally, objects are built in stages and thus varying states have to be detected according to the Percentage of Completion.In this work, a framework is presented to derive work progress of construction sites based on point cloud data. More specifically, a methodology is constituted to compute the Percentage of Completion of in-situ cast concrete walls. In the literature study, existing methods are evaluated for their ability to track progress even in highly cluttered environments. In the practical study, we perform an empirical analysis on a set of periodic point clouds to establish the obstacles and feasibility of the methodology. This work leads to a better understanding of the progress monitoring paradigm which is still subject of ongoing research and will serve as the basis for the further development of a set of automated procedures.

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“…Planar features are important in both indoor and outdoor 3D scenes. For large-scale 3D scene reconstructions, complex objects can be reconstructed better by the segmentation and recognition of planar shapes [4][5][6]. Traditional plane segmentation algorithms are limited by the large amount of point cloud data and the search method of neighbor points, which result in slow computation efficiency.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Encoding Voxel-Based Segmentation (EVBS) Algorithm Based on Fast Adjacent Voxel Search for Point Cloud Plane Segmentation

2019

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Plane segmentation is a basic yet important process in light detection and ranging (LiDAR) point cloud processing. The traditional point cloud plane segmentation algorithm is typically affected by the number of point clouds and the noise data, which results in slow segmentation efficiency and poor segmentation effect. Hence, an efficient encoding voxel-based segmentation (EVBS) algorithm based on a fast adjacent voxel search is proposed in this study. First, a binary octree algorithm is proposed to construct the voxel as the segmentation object and code the voxel, which can compute voxel features quickly and accurately. Second, a voxel-based region growing algorithm is proposed to cluster the corresponding voxel to perform the initial point cloud segmentation, which can improve the rationality of seed selection. Finally, a refining point method is proposed to solve the problem of under-segmentation in unlabeled voxels by judging the relationship between the points and the segmented plane. Experimental results demonstrate that the proposed algorithm is better than the traditional algorithm in terms of computation time, extraction accuracy, and recall rate.

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Point cloud quality requirements for Scan-vs-BIM based automated construction progress monitoring

Cited by 140 publications

References 27 publications

An Application Oriented Scan-to-BIM Framework

An Application Oriented Scan-to-BIM Framework

Percentage of Completion of in-Situ Cast Concrete Walls Using Point Cloud Data and Bim

An Efficient Encoding Voxel-Based Segmentation (EVBS) Algorithm Based on Fast Adjacent Voxel Search for Point Cloud Plane Segmentation

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