Automated digital modeling of existing buildings: A review of visual object recognition methods

Czerniawski, Thomas; Leite, Fernanda

doi:10.1016/j.autcon.2020.103131

Cited by 87 publications

(26 citation statements)

References 81 publications

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“…From the literature, the BIM information extraction requirements for these classes are established. Table 2 depicts the expected Level of Accuracy and Level of Development that are commonly reported for the structure classes [2,3,49].…”

Section: Point Cloud Suitabilitymentioning

confidence: 99%

Point Cloud Validation: On the Impact of Laser Scanning Technologies on the Semantic Segmentation for BIM Modeling and Evaluation

et al. 2022

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Building Information models created from laser scanning inputs are becoming increasingly commonplace, but the automation of the modeling and evaluation is still a subject of ongoing research. Current advancements mainly target the data interpretation steps, i.e., the instance and semantic segmentation by developing advanced deep learning models. However, these steps are highly influenced by the characteristics of the laser scanning technologies themselves, which also impact the reconstruction/evaluation potential. In this work, the impact of different data acquisition techniques and technologies on these procedures is studied. More specifically, we quantify the capacity of static, trolley, backpack, and head-worn mapping solutions and their semantic segmentation results such as for BIM modeling and analyses procedures. For the analysis, international standards and specifications are used wherever possible. From the experiments, the suitability of each platform is established, along with the pros and cons of each system. Overall, this work provides a much needed update on point cloud validation that is needed to further fuel BIM automation.

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Section: Point Cloud Suitabilitymentioning

confidence: 99%

Point Cloud Validation: On the Impact of Laser Scanning Technologies on the Semantic Segmentation for BIM Modeling and Evaluation

et al. 2022

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“…The automation of the digital modelling process of buildings, whether existing (as-is) or under construction (as-built), has represented especially in recent times a challenging issue that has been addressed with several different approaches depending on the building typology and the modelling process. In this regard, most of the efforts have been spent in the geometric modelling side of the process [65] and on the implementation of various visual recognition methods for shapes and objects from point clouds or laser scanning data [66] [67].…”

Section: Acquisition and Modelling Of As-built Datamentioning

confidence: 99%

On-demand generation of as-built infrastructure information models for mechanised Tunnelling from TBM data: A computational design approach

Getuli

Capone

Bruttini

et al. 2021

Automation in Construction

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When dealing with complex curved geometries and massive datasets linked to linear infrastructures, manual generation and maintenance of related multidisciplinary BIM models and documentation are yet to be fully automated. This research focused on the integration of BIM and computational design into an intuitive approach for the generation of as-built models of mechanised tunnelling projects, leveraging the use of the real-time data collected by TBM. A preliminary study of the parameters was conducted to describe the curved geometry of the tunnel, then their mutual alignment was followed by the identification of additional relevant information. To automate the tunnel's BIM-based modelling process, a system of four coupled algorithms was developed and tested with a sample TBM dataset. The results demonstrate how the adoption of computational design methods drastically enhances the modelling process for infrastructure projects, allowing for the on-demand generation of as-built BIM models, reducing time and errors.

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“…From this point on, the process runs automatically apart of training which is not mandatory for each data set [19]. Training is necessary in a sufficient extent to provide the test base for the later testing [20]. Mesh reconstruction and point cloud segmentation are implemented as separate utilities for the object recognition framework.…”

Section: Process Definitionmentioning

confidence: 99%

Automated Generation of a Digital Twin of a Process Plant by Using 3D Scan and Artificial Intelligence

Grau¹,

Korol²,

Lützenberger³

et al. 2021

Advances in Transdisciplinary Engineering

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The simulation of production processes using a Digital Twin is a promising tool for predictive planning, analysis of existing systems or process-parallel monitoring. In the process industry, the concept of Digital Twin provides significant support for process optimization. The generation of the Digital Twin of an already existing plant is a major challenge – in particular for small and medium-sized enterprises. In this sense, the twinning of the existing physical environment has got a particular importance due to high effort. Shape segmentation from unstructured (e.g. point cloud data) is a core step of the digital twinning process for industrial facilities. This is an inherent issue of Product Lifecycle Management how to acquire data of existing goods. The practice of Digital Twin is described based on object recognition by using methods of Machine Learning. The exploration of the pipeline semantics presents a particular challenge. The highly automated procedure for the generation of Digital Twin is described based on a use case of a biogas plant. Commercial deployment, pitfalls, drawbacks and potential for further developments are further explored.

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Automated digital modeling of existing buildings: A review of visual object recognition methods

Cited by 87 publications

References 81 publications

Point Cloud Validation: On the Impact of Laser Scanning Technologies on the Semantic Segmentation for BIM Modeling and Evaluation

Point Cloud Validation: On the Impact of Laser Scanning Technologies on the Semantic Segmentation for BIM Modeling and Evaluation

On-demand generation of as-built infrastructure information models for mechanised Tunnelling from TBM data: A computational design approach

Automated Generation of a Digital Twin of a Process Plant by Using 3D Scan and Artificial Intelligence

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