Prototype pipeline modelling using interval scanning point clouds

Abstract

With the aid of computer aided design (CAD) and building information modelling (BIM), as-built to as-designed comparison has seen many developments in improving the workflow of manufacturing and construction tasks. Recently, evolution has been centred around automation of scene interpretation from three-dimensional (3D) scan data. The scope of this paper is to assess assemblies as the installation process progresses and inferring if arising deviations are problematic (complex task). The adequacy of utilising unorganised point clouds to compliance check are trialled with a real life down-scaled prototype model in conjunction with a synthetic dataset. This work aims to highlight areas where large rework could be avoided, in part by the detection of potential clashes of components early in the pipeline installation process. With the help of an extracted model in the form of a point cloud generated from a scanned physical model and a 3D CAD model representing the nominal geometry, an operator can be made visually aware of potential deviations and component clashes during a pipeline assembly process.

Keywords

Point clouds / Modelling / Computer aided design (CAD) / Digital manufacturing / Interval scanning

References

[1.] Wang Q, Kim MK. Applications of 3D point cloud data in the construction industry: a fifteen-year review from 2004 to 2018. Adv Eng Inform, 2019, 39: 306-319.

[2.] Kardos C, Váncza J. Application of generic CAD models for supporting feature based assembly process planning. Proc CIRP, 2018, 67: 446-451.

[3.] Bosché F. Automated recognition of 3D CAD model objects in laser scans and calculation of as-built dimensions for dimensional compliance control in construction. Adv Eng Inform, 2010, 24(1): 107-118.

[4.] Bosché F, Guillemet A, Turkan Y, et al. Tracking the built status of MEP works: assessing the value of a scan-vs-BIM system. J Comput Civ Eng, 2014, 28(4): 05014004.

[5.] Anil EB, Tang P, Akinci B et al (2011) Assessment of the quality of as-is building information models generated from point clouds using deviation analysis. In: Beraldin JA, Cheok GS, McCarthy MB et al (eds) Three-dimensional imaging, interaction, and measurement, vol. 7864. International Society for Optics and Photonics, SPIE, p 78640F. https://doi.org/10.1117/12.876554

[6.] Nahangi M, Haas CT. Automated 3D compliance checking in pipe spool fabrication. Adv Eng Inform, 2014, 28(4): 360-369.

[7.] Bosché F, Haas C. Automated retrieval of 3D CAD model objects in construction range images. Autom Constr, 2008, 17(4): 499-512.

[8.] Maalek R, Lichti DD, Walker R, et al. Extraction of pipes and flanges from point clouds for automated verification of pre-fabricated modules in oil and gas refinery projects. Autom Constr, 2019, 103: 150-167.

[9.] Kim Y, Nguyen CHP, Choi Y. Automatic pipe and elbow recognition from three-dimensional point cloud model of industrial plant piping system using convolutional neural network-based primitive classification. Autom Constr, 2020, 116.

[10.] Sharif M, Nahangi M, Haas C, et al. Automated model-based finding of 3D objects in cluttered construction point cloud models. Comput Aided Civ Infrastruct Eng, 2017, 32(11): 893-908.

[11.] Papazov C, Burschka D. Kimmel R, Klette R, Sugimoto A. An efficient RANSAC for 3D object recognition in noisy and occluded scenes. Computer vision—ACCV 2010, 2011, Berlin Heidelberg: Springer 135-148.

[12.] Kawashima K, Kanai S, Date H. As-built modeling of piping system from terrestrial laser-scanned point clouds using normal-based region growing. J Comput Des Eng, 2014, 1(1): 13-26.

[13.] Czerniawski T, Nahangi M, Haas C, et al. Pipe spool recognition in cluttered point clouds using a curvature-based shape descriptor. Autom Constr, 2016, 71: 346-358.

[14.] Nguyen CHP, Choi Y. Comparison of point cloud data and 3D CAD data for on-site dimensional inspection of industrial plant piping systems. Autom Constr, 2018, 91: 44-52.

[15.] Anil EB, Tang P, Akinci B, et al. Deviation analysis method for the assessment of the quality of the as-is building information models generated from point cloud data. Autom Constr, 2013, 35: 507-516.

[16.] Safa M, Shahi A, Nahangi M, et al. Automating measurement process to improve quality management for piping fabrication. Structures, 2015, 3: 71-80.

[17.] Cheng L, Wei Z, Sun M, et al. DeepPipes: learning 3D pipelines reconstruction from point clouds. Graph Models, 2020, 111.

[18.] Kim MK, Wang Q, Park JW, et al. Automated dimensional quality assurance of full-scale precast concrete elements using laser scanning and BIM. Autom Constr, 2016, 72: 102-114.

[19.] Fraga-Lamas P, Noceda-Davila D, Fernández-Caramés T, et al. Smart pipe system for a Shipyard 4.0. Sensors, 2016, 16(12): 2186.

[20.] Qi CR, Su H, Mo K et al (2016) PointNet: deep learning on point sets for 3D classification and segmentation. Comput Vis Pattern Recognit. https://doi.org/10.48550/arXiv.1612.00593

[21.] Qi CR, Yi L, Su H et al (2017) PointNet++: deep hierarchical feature learning on point sets in a metric space. Comput Vis Pattern Recognit. https://doi.org/10.48550/arXiv.1706.02413

[22.] Phan AV, Nguyen ML, Nguyen YLH, et al. DGCNN: a convolutional neural network over large-scale labeled graphs. Neural Netw, 2018, 108: 533-543.

[23.] Guo M, Cai J, Liu Z, et al. PCT: point cloud transformer. Comput Vis Media, 2021, 7(2): 187-199.

[24.] Wu Z, Song S, Khosla A et al (2014) 3D shapeNets: a deep representation for volumetric shapes. Comput Vis Pattern Recognit. https://doi.org/10.48550/arXiv.1406.5670

[25.] Mo K, Zhu S, Chang AX et al (2019) Partnet: a large-scale benchmark for fine-grained and hierarchical part-level 3d object understanding. In: IEEE/CVF conference on computer vision and pattern recognition (CVPR), Long Beach, CA, USA, 15–20 June 2019

[26.] Uy MA, Pham QH, Hua BS et al (2019) Revisiting point cloud classification: a new benchmark dataset and classification model on real-world data. In: IEEE/CVF international conference on computer vision (ICCV), 27 October– 02 November, Seoul, Korea, pp 1588–1597. https://doi.org/10.1109/ICCV.2019.00167

[27.] Yeo C, Kim S, Kim H, et al. Deep learning applications in an industrial process plant: repository of segmented point clouds for pipework components. JMST Adv, 2020, 2(1): 15-24.

[28.] Yin C, Cheng JC, Wang B, et al. Automated classification of piping components from 3D LiDAR point clouds using SE-PseudoGrid. Autom Constr, 2022, 139.

[29.] Yin C, Wang B, Gan VJL, et al. Automated semantic segmentation of industrial point clouds using ResPointNet++. Autom Constr, 2021, 130.

[30.] Agapaki E, Brilakis I. CLOI-NET: class segmentation of industrial facilities’ point cloud datasets. Adv Eng Inform, 2020, 45.

[31.] Korus K, Czerniawski T, Salamak M. Visual programming simulator for producing realistic labeled point clouds from digital infrastructure models. Autom Constr, 2023, 156.

[32.] Li X, Li Z, Wu G. Modular and offsite construction of piping: current barriers and route. Appl Sci, 2017, 7(6): 547.

[33.] Pečur T, Yu N, Sherlock A et al (2023) Robust coordinate system alignment using high density point clouds from laser line probe. In: Proceedings of laser metrology and machine performance XV, Edinburgh, United Kingdom, pp 103–113

[34.] Schnabel R, Wahl R, Klein R. Efficient RANSAC for point-cloud shape detection. Comput Graph Forum, 2007, 26(2): 214-226.

[35.] Ester M, Kriegel HP, Sander J et al (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: ACM SIGKDD conference on knowledge discovery and data mining, Portland, 2–4 Aug, pp 226–231

[36.] EN 545 (2010) Ductile iron pipes, fittings, accessories and their joints for water pipelines—requirements and test methods

[37.] EN 593 (2017) Industrial valves—metallic butterfly valves for general purposes

[38.] EN 1092-2 (1997) Flanges and their joints—circular flanges for pipes, valves, fittings and accessories, PN designated Part 2. Cast iron flanges

[39.] Winiwarter L, Esmorís Pena AM, Weiser H, et al. Virtual laser scanning with HELIOS++: a novel take on ray tracing-based simulation of topographic full-waveform 3D laser scanning. Remote Sens Environ, 2022, 269.

[40.] Wang H, Liu Q, Yue X et al (2021) Unsupervised point cloud pre-training via occlusion completion. In: Proceedings of the IEEE/CVF international conference on computer vision (ICCV), Montreal, 10–17 Oct, pp 9782–9792

[41.] Huang J, You S (2013) Detecting objects in scene point cloud: a combinational approach. In: International conference on 3D vision, IEEE, Seattle, WA, USA, pp 175–182. https://doi.org/10.1109/3DV.2013.31

[42.] Sun J, Zhang Q, Kailkhura B et al (2022) Benchmarking robustness of 3d point cloud recognition against common corruptions. arXiv 2201.12296. https://doi.org/10.48550/arXiv.2201.12296

[43.] Lee D, Lee J, Lee J et al (2021) Regularization strategy for point cloud via rigidly mixed sample. arXiv 2102.01929. https://doi.org/10.48550/arXiv.2102.01929

[44.] Zhou QY, Park J, Koltun V (2016) Fast global registration. In: Leibe B, Matas J, Sebe N et al (eds.) Computer vision—ECCV 2016. Lecture Notes in Computer Science, vol 9906. Springer International Publishing, Cham. https://doi.org/10.1007/978-3-319-46475-6_47

[45.] Rusu RB, Blodow N, Beetz M (2009) Fast point feature histograms (FPFH) for 3D registration. In: IEEE international conference on robotics and automation, Kobe, 12−17 May, pp 3212–3217. https://doi.org/10.1109/ROBOT.2009.5152473

[46.] Park JH, Storch RL. Pipe-routing algorithm development: case study of a ship engine room design. Expert Syst Appl, 2002, 23(3): 299-309.

[47.] Dong Z, Bian X. Ship pipe route design using improved a* algorithm and genetic algorithm. IEEE Access, 2020, 8: 153273-153296.

[48.] Kim SH, Ruy WS, Jang BS. The development of a practical pipe auto-routing system in a shipbuilding cad environment using network optimization. Int J Naval Archit Ocean Eng, 2013, 5(3): 468-477.

[49.] Kim Y, Lee K, Nam B, et al. Application of reinforcement learning based on curriculum learning for the pipe auto-routing of ships. J Comput Des Eng, 2023, 10(1): 318-328.

Funding

Scottish Research Partnership in Engineering(SFC-NMIS-IDP/020/); State Key Laboratory of robotics and system(SKLRS/2022/KF/01)