An adaptive approach for the reconstruction and modeling of as-built 3D pipelines from point clouds

Patil, Ashok Kumar; Holi, Pavitra; Lee, Sang Keun; Chai, Young Ho

doi:10.1016/j.autcon.2016.12.002

Cited by 93 publications

(47 citation statements)

References 11 publications

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“…As this method employed a 5D Hough transform to estimate the cylinder shape, it was computationally inefficient and so was generally computed separately in 2D and 3D. In [7], the data were first extracted and removed vertically and horizontally from the ground, and then the Hough transform was applied to project a plane on the normal vector followed by a cylinder shape estimation through circle fitting [14,15]. Huang and You [16] proposed a method to classify 3D point clouds using fast point feature histogram (FPFH) and support vector machine (SVM) and then segmented it using a classical flood-fill algorithm.…”

Section: Related Workmentioning

confidence: 99%

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Automatic Detection and Modeling of Underground Pipes Using a Portable 3D LiDAR System

Aijazi

Laurent

Trassoudaine

et al. 2019

Sensors

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Automatic and accurate mapping and modeling of underground infrastructure has become indispensable for several important tasks ranging from urban planning and construction to safety and hazard mitigation. However, this offers several technical and operational challenges. The aim of this work is to develop a portable automated mapping solution for the 3D mapping and modeling of underground pipe networks during renovation and installation work when the infrastructure is being laid down in open trenches. The system is used to scan the trench and then the 3D scans obtained from the system are registered together to form a 3D point cloud of the trench containing the pipe network using a modified global ICP (iterative closest point) method. In the 3D point cloud, pipe-like structures are segmented using fuzzy C-means clustering and then modeled using a nested MSAC (M-estimator SAmpling Consensus) algorithm. The proposed method is evaluated on real data pertaining to three different sites, containing several different types of pipes. We report an overall registration error of less than 7 % , an overall segmentation accuracy of 85 % and an overall modeling error of less than 5 % . The evaluated results not only demonstrate the efficacy but also the suitability of the proposed solution.

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Section: Related Workmentioning

confidence: 99%

“…Use MSAC to fit circle 13 begin 14 Select 3 points w c , w d and w e such that w c = w d and w c = w e 15 Use these 3 points employing Kasa method to parameterize a circle with center c ∈ R 2 and radius r…”

Section: Segment_length L Nmentioning

confidence: 99%

Automatic Detection and Modeling of Underground Pipes Using a Portable 3D LiDAR System

Aijazi

Laurent

Trassoudaine

et al. 2019

Sensors

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“…State-of-the-art research work on pipe detection has partially solved the problem and not to a greater extent compared to commercially available software like Edge-Wise [14,15]. For instance, [14] only detect pipes in orthogonal directions.…”

Section: State-of-researchmentioning

confidence: 99%

“…For instance, [14] only detect pipes in orthogonal directions. A recent study completed by [15] is dependent on threshold values for radius and normal estimation. The pipe radius range is 0.0254 m -0.762 m and the normal deviation is 5°.…”

Section: State-of-researchmentioning

confidence: 99%

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State-of-Practice on As-Is Modelling of Industrial Facilities

Agapaki

Brilakis

2018

Advanced Computing Strategies for Engineering

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90% of the time needed for the conversion from point clouds to 3D models of industrial facilities is spent on geometric modelling due to the sheer number of Industrial Objects (IOs) of each plant. Hence, cost reduction is only possible by automating modelling. Our previous work has successfully identified the most frequent industrial objects which are in descending order: electrical conduit, straight pipes, circular hollow sections, elbows, channels, solid bars, I-beams, angles, flanges and valves. We modelled those on a state-of-theart software, EdgeWise and then evaluated the performance of this software for pipeline and structural modelling. The modelling of pipelines is summarized in three basic steps: (a) automated extraction of cylinders, (b) their semantic classification and (c) manual extraction and editing of pipes. The results showed that cylinders are modelled with 75 % recall and 62 % precision on average. We discovered that pipes, electrical conduit and circular hollow sections require 80 % of the Total Modelling Hours (TMH) of the 10 most frequent IOs to build the plant model. TMH was then compared to modelling hours in Revit and showed that 67 % of pipe modelling time is saved by EdgeWise. This paper is the first to evaluate state-of-the-art industrial modelling software. These findings help in better understanding the problem and serve as the foundation for researchers who are interested in solving it.

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Real‐time reconstruction of pipes using RGB‐D cameras

Kim,

Ahn,

Kim

et al. 2023

Computer Animation & Virtual

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This paper presents a novel method that automatically reconstructs pipes in real time using an RGB‐D camera. The input image is decomposed into superpixels, and a pipe element, which is a 3D circle lying on the pipe surface, is generated for each superpixel. Over frames, the pipe elements are grouped into sequences and are finally modeled in parametric equations. The method is tested in daily settings, where the pipes may be curved and their radii may vary along the axes. The test results show that the method is able to reconstruct pipes efficiently, precisely and robustly.

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An adaptive approach for the reconstruction and modeling of as-built 3D pipelines from point clouds

Cited by 93 publications

References 11 publications

Automatic Detection and Modeling of Underground Pipes Using a Portable 3D LiDAR System

Automatic Detection and Modeling of Underground Pipes Using a Portable 3D LiDAR System

State-of-Practice on As-Is Modelling of Industrial Facilities

Real‐time reconstruction of pipes using RGB‐D cameras

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