Automated geometric digital twinning of bridges from segmented point clouds by parametric prototype models

Mafipour, M. Saeed; Vilgertshofer, Simon; Borrmann, André

doi:10.1016/j.autcon.2023.105101

Cited by 11 publications

(1 citation statement)

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“…Recent research has shown a growing interest in automating the Scan2BIM process [16]. Some studies have focused solely on automatically generating 3D geometric models of bridge datasets, overlooking the automation of semantic segmentation, which affect the quality of the generated 3D models [26][27][28]. Alternatively, other studies have approached semantic segmentation as a detection problem: detecting primitives and then labeling them semantically.…”

Section: Related Workmentioning

confidence: 99%

Hierarchical SVM for Semantic Segmentation of 3D Point Clouds for Infrastructure Scenes

Mansour,

Martens,

Blankenbach

2024

Infrastructures

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The incorporation of building information modeling (BIM) has brought about significant advancements in civil engineering, enhancing efficiency and sustainability across project life cycles. The utilization of advanced 3D point cloud technologies such as laser scanning extends the application of BIM, particularly in operations and maintenance, prompting the exploration of automated solutions for labor-intensive point cloud modeling. This paper presents a demonstration of supervised machine learning—specifically, a support vector machine—for the analysis and segmentation of 3D point clouds, which is a pivotal step in 3D modeling. The point cloud semantic segmentation workflow is extensively reviewed to encompass critical elements such as neighborhood selection, feature extraction, and feature selection, leading to the development of an optimized methodology for this process. Diverse strategies are implemented at each phase to enhance the overall workflow and ensure resilient results. The methodology is then evaluated using diverse datasets from infrastructure scenes of bridges and compared with state-of-the-art deep learning models. The findings highlight the effectiveness of supervised machine learning techniques at accurately segmenting 3D point clouds, outperforming deep learning models such as PointNet and PointNet++ with smaller training datasets. Through the implementation of advanced segmentation techniques, there is a partial reduction in the time required for 3D modeling of point clouds, thereby further enhancing the efficiency and effectiveness of the BIM process.

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