Comparative visualization of BIM geometry and corresponding point clouds

Stojanovic, Vladeta; Richter, Rico; Döllner, Jürgen; Trapp, Matthias

doi:10.2495/sdp-v13-n1-12-23

Cited by 13 publications

(9 citation statements)

References 20 publications

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“…Anil et al [42] were among the pioneers who discussed in depth the problem of geometric deviation. They suggested using minimum Euclidean distance and thresholding [43] as metrics to evaluate the fitting quality (CAD model against point clouds). The deviation analysis at macro level (for the whole structure) was performed using a commercial software application (i.e.…”

Section: Existing Research Methodsmentioning

confidence: 99%

Geometric Accuracy of Digital Twins for Structural Health Monitoring

Lu¹,

Rausch²,

Bolpagni³

et al. 2021

Structural Integrity and Failure

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We present an exploratory analysis of the geometric accuracy of digital twins generated for existing infrastructure using point clouds. The Level of Geometric Accuracy is a vital specification to measure the twinning quality of the resulting twins. However, there is a lack of a clear definition of the Level of Geometric Accuracy for twins generated in the operation and maintenance stage, especially for structural health monitoring purposes. We critically review existing industry applications and twinning methods. To highlight the technical challenges with creating high-fidelity digital replicas, we present a case study of twinning a bridge using real-world point clouds. We do not provide conclusive methods or results but envisage potential twinning strategies to achieve the desired geometry accuracy. This chapter aims to inform the future development of a geometric accuracy-based evaluation system for use in twinning and updating processes. Since a major barrier for a fully automated twinning workflow is the lack of rigorous interpretation of 'geometric accuracy' outside design environments, it is imperative to develop comprehensive standards to guide practitioners and researchers in order to achieve model certainty. As such, this chapter also aims to educate all stakeholders in order to minimise risk when drafting contracts and exchanging digital deliverables.

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Section: Existing Research Methodsmentioning

confidence: 99%

Geometric Accuracy of Digital Twins for Structural Health Monitoring

Lu¹,

Rausch²,

Bolpagni³

et al. 2021

Structural Integrity and Failure

View full text Add to dashboard Cite

show abstract

“…The micro scale considers individual BIM elements and allows a detailed comparison between the point cloud and the model. (Stojanovic et al, 2018) investigate a deviation analysis approach which enables visual assessment of differences between existing BIM geometry and point cloud data. The deviation analysis is, in this case, an updating process used to identify and document new features in a building.…”

Section: Related Workmentioning

confidence: 99%

Automating the Verification of Heritage Building Information Models Created From Point Cloud Data

Macher

Chow

Fai

2019

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

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The use of remote sensors to acquire metric information for building information modelling (BIM) of heritage buildings is now common. Problematically, the creation of models from that information is still largely a manual and non-quantifiable process. As a result, a key aspect of the scan-to-BIM process is verification of the accuracy of the model in relation to the metric information. The most common method to check a model element constructed from a point cloud seems to be the analysis of deviations between this element and the corresponding point cloud (Anil et al., 2013;Tang et al., 2011). It is comprised of three main steps: the computation, the visualisation and the analysis of deviations. The verification process is particularly onerous for large-scale buildings where it is necessary to ensure that all elements of a model are consistent with metric data that may come from diverse sources (Chow and Fai, 2017). In this paper, we discuss the development of a plug-in for Autodesk Revit that automates this verification process.

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“…Deviation analysis plays an important role in the approximation and visualization of spatial differences between as-designed, as-built and as-is digital built environment representations. This can include approximation of planar surfaces for measuring deviations between as-built and as-is BIM versus point cloud representations for core structural elements such as non-curved walls (Stojanovic et al, 2018). Anil et al used an existing commercial software tool to import corresponding as-is BIM and point cloud representations, in order to perform comparative deviation analysis -using a computationally expensive method based on minimum Euclidean distance comparison (Anil et al, 2013).…”

Section: Foundations and Related Workmentioning

confidence: 99%

“…The use of a voxelized mesh representation is similar to the style of mesh representation used for FEA. Our general deviation analysis approach approach is influenced by earlier deviation analysis methods focusing on visualizing spatial deviations between as-is point cloud and as-built BIM models of indoor spaces (Stojanovic et al, 2018).…”

Section: Foundations and Related Workmentioning

confidence: 99%

A Service-Oriented Indoor Point Cloud Processing Pipeline

Stojanovic

Trapp

Richter

et al. 2019

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

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Abstract. Visualization of point clouds plays an important role in understanding the context of the digital representation of the built environment. Modern commodity mobile devices (e.g., smartphones and tablets), are capable of capturing representations in the form of 3D point clouds, with their depth-sensing and photogrammetry capabilities. Points clouds enable the encoding of important spatial and physical features of the built environment they represent. However, once captured, point clouds need to be processed before they can be used for further semantic enrichment and decision making. An integrated pipeline for such processes is crucial for use in larger and more complex enterprise systems and data analysis platforms, especially within the realm of Facility Management (FM) and Real Estate 4.0. We present and discuss a prototypical implementation for a service-oriented point cloud processing pipeline. The presented processing features focus on detecting and visualizing spatial deviations between as-is versus as-designed representations. We discuss the design and implementation of these processing features, and present experimental results. The presented approach can be used as a lightweight software component for processing indoor point clouds captured using commodity mobile devices, as well as primary deviation analysis, and also provides a processing link for further semantic enrichment of base-data for Building Information Modeling (BIM) and Digital Twin (DT) applications.

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Comparative visualization of BIM geometry and corresponding point clouds

Cited by 13 publications

References 20 publications

Geometric Accuracy of Digital Twins for Structural Health Monitoring

Geometric Accuracy of Digital Twins for Structural Health Monitoring

Automating the Verification of Heritage Building Information Models Created From Point Cloud Data

A Service-Oriented Indoor Point Cloud Processing Pipeline

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