Autonomous Modeling of Pipes within Point Clouds

Ahmed, Mahmoud; Haas, Carl T.; Haas, Ralph

doi:10.22260/isarc2013/0120

Cited by 11 publications

(2 citation statements)

References 21 publications

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“…Hough transform finds shape parameters to best describe the provide data for object generation from PCD. Researchers have shown how this method could be applied to detect pipes and other cylindrical elements [37][38][39]. It can also be used to estimate shape parameters from a PCD with the prior shape knowledge from the corresponding DI [40].…”

Section: Classic Computer Vision Algorithmsmentioning

confidence: 99%

Construction and Maintenance of Building Geometric Digital Twins: State of the Art Review

Drobnyi

Fathy

et al. 2023

Sensors

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Most of the buildings that exist today were built based on 2D drawings. Building information models that represent design-stage product information have become prevalent in the second decade of the 21st century. Still, it will take many decades before such models become the norm for all existing buildings. In the meantime, the building industry lacks the tools to leverage the benefits of digital information management for construction, operation, and renovation. To this end, this paper reviews the state-of-the-art practice and research for constructing (generating) and maintaining (updating) geometric digital twins. This paper also highlights the key limitations preventing current research from being adopted in practice and derives a new geometry-based object class hierarchy that mainly focuses on the geometric properties of building objects, in contrast to widely used existing object categorisations that are mainly function-oriented. We argue that this new class hierarchy can serve as the main building block for prioritising the automation of the most frequently used object classes for geometric digital twin construction and maintenance. We also draw novel insights into the limitations of current methods and uncover further research directions to tackle these problems. Specifically, we believe that adapting deep learning methods can increase the robustness of object detection and segmentation of various types; involving design intents can achieve a high resolution of model construction and maintenance; using images as a complementary input can help to detect transparent and specular objects; and combining synthetic data for algorithm training can overcome the lack of real labelled datasets.

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Section: Classic Computer Vision Algorithmsmentioning

confidence: 99%

Construction and Maintenance of Building Geometric Digital Twins: State of the Art Review

Drobnyi

Fathy

et al. 2023

Sensors

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“…For industrial specific object recognition, some of the popular methods found in the literature are based on voxel connectivity [12][13][14], 2D primitive fitting [15][16][17][18], classifier training [19], and feature and shape descriptor matching [20][21][22][23]. Although a variety of approaches exist, all are negatively impacted by unnecessarily cluttered search spaces.…”

Section: Industrial Object Recognitionmentioning

confidence: 99%

Automated Removal of Planar Clutter from 3D Point Clouds for Improving Industrial Object Recognition

Czerniawski

Nahangi

Walbridge

et al. 2016

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

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The industrial construction industry makes use of prefabrication, preassembly, modularization and offsite fabrication (PPMOF) for project execution because they offer a superior level of control as compared to on-site operations. This control is enabled by systematic and thorough performance feedback loops. Improvement of the feedback systems within these facilities will require a transition away from suboptimal manual data collection to more reliable automated data collection and processing. Laser scanners are an effective tool for automatically gathering dimensional data but extraction of useful information from point clouds remains a challenge. The speed of 3D object recognition methods depends on the size of the search space. Methods for reducing this search space are needed in order to improve the performance of 3D object recognition and subsequent information extraction. Large planar objects (e.g. floors and walls) constitute a large portion of the search space in fabrication facilities, yet are rarely the objects of interest for analysis. In this paper, an automated framework for detecting and removing large planes in point clouds is presented to speed up object recognition. The raw point cloud is first Guassian mapped to normal vector space by calculating normal vectors at each point. The Gaussian sphere is clustered using a density-based clustering algorithm and major parallel planes are segmented from the rest of the point cloud. The major planes are removed and the remaining objects in the scene continue on to 3D object recognition. Results show the algorithm for automatic plane removal can reduce the search space for object recognition by as much as 60% or 70%.

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Automated Building Information Models Reconstruction Using 2D Mechanical Drawings

Cho

Liu

Akinci

2018

Advances in Informatics and Computing in Civil and Construction Engineering

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Autonomous Modeling of Pipes within Point Clouds

Cited by 11 publications

References 21 publications

Construction and Maintenance of Building Geometric Digital Twins: State of the Art Review

Construction and Maintenance of Building Geometric Digital Twins: State of the Art Review

Automated Removal of Planar Clutter from 3D Point Clouds for Improving Industrial Object Recognition

Automated Building Information Models Reconstruction Using 2D Mechanical Drawings

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