As-built modeling of piping system from terrestrial laser-scanned point clouds using normal-based region growing

Journal of Computational Design and Engineering

Volumn 1, Issue 1, 2014, Pages 13-26

  Kawashima, Kazuaki ^a   Kanai, Satoshi ^a   Date, Hiroaki ^a  

^a HOKKAIDO UNIVERSITY   (Japan)

Author keywords

As built model; Laser scanning; Object recognition; Piping system; Point clouds

Indexed keywords

3D MODELING; CHEMICAL PLANTS; OBJECT RECOGNITION; SURFACE FITTING;

CYLINDER SURFACE; HIGH NOISE LEVELS; LARGE SCALE LASERS; LASER SCANNING; PLANT EQUIPMENTS; POINT CLOUD; RECOGNITION ALGORITHM; REGION GROWING;

PIPING SYSTEMS;

EID: 85011609914     PISSN: 22884300     EISSN: 22885048     Source Type: Journal    
DOI: 10.7315/JCDE.2014.002     Document Type: Article

References (17)

1. Masuda, H., Tanaka, I., Enomoto, M., Reliable surface extraction from point-clouds using scanner-dependent parameters. Computer-Aided Design and Applications 10:2 (2012), 265–277.
2. Rabbani, T., Heuvel, F., 3d industrial reconstruction by fitting CSG models to a combination of images and point clouds. Proceeings of ISPRS The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences 35:B3 (2004), 7–12 July 12-23; IST, Turkey.
3. Aurelien, B., Chaine, R., Marc, R., Thibault, G., Akkouche, S., Reconstruction of consistent 3d CAD models from point cloud data using a priori CAD models. ISPRS Workshop Laser Scanning 2011, 2011 Aug 29-31; Calgary, Canada; submission-26.
4. Johnson, A., Hoffman, R., Osborn, J., Hebert, M., A system for semi-automatic modeling of complex environments. Proceedings of IEEE International Conference on Recent Advances in 3-D Digital Imaging and Modeling, 1997, 213–220 May 12-15; Ottawa, Canada.
5. Namatame, T., Mizoguti, T., Kobayashi, Y., Shirai, K., As-build modeling using Manhattan-world assumption(in Japanese). Proceedings of the 2011 Japan Society for Precision Engineering Autumn meeting, 2011 2011 Sep 20-22; Kanazawa, Japan; F06.
6. Lee, J., Kim, C., Son, H., Kim, C., Skeleton-based 3d reconstruction of as-built pipelines from laser-scanned data. Proceedings of the 2012 ASCE International Conference on Computing in Civil Engineering, 2012, 245–252 Jun 17-20; Clearwater Beach, FL.
7. Belton, D., Lichti, D., Classification and segmentation of terrestrial laserscanner point clouds using local variance information. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 36:5 (2006), 44–49.
8. Rabbani, T., Heuvel, F., Vosselman, G., Segmentation of point clouds using smoothness constraint. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 36 (2006), 248–253.
9. Vosselman, G., Gorte, B.G.H., Sithole, G., Rabbani, T., Recognising structure in laser scanner point clouds. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences 46 (2004), 33–38.
10. El-Halawany, S., Lichti, D., Detection of road poles from mobile terrestrial laser scanner point cloud. C2011 International Workshop on Multi-Platform/MultiSensor Remote Sensing and Mapping (M2RSM), 2011, 1–6 Jan 10-12; Xiamen, China.
11. Marshall, A.D., Lukacs, G., Martin, R.R., Robust segmentation of primitives from range data in the presence of geometric degeneracy. IEEE Transactions on Pattern Analysis and Machine Intelligence 23:3 (2001), 304–314.
12. Kawashima, K., Kanai, S., Date, H., Automatic recognition and modeling of piping system from large-scale terrestrial laser scanned point cloud. 2012 Asian Conference on Design and Digital Engineering, 2012, 100022 Dec 6-8; Niseko, Japan.
13. Maronna, R.A., Martin, D.R., Yohai, V.J., Robust statistics: theory and methods., 2006, John Wiley & Sons, Ltd., (UK), 436.
14. Shakarji, M.C., Least-squares fitting algorithms of the NIST algorithm testing system. Journal of Research of the National Institute of Standards and Technology 103:6 (1998), 633–641.
15. Bauer, U., Polthier, K., Generating parametric models of tubes from laser scans. Computer-Aided Design 41:10 (2009), 719–729.
16. Rusu, R.B., Martin, Z.C., Beetz, M., Learning informative point classes for the acquisition of object model maps. Proceedings of International Conference on Control, Automation, Robotics and Vision, 2008, 643–650 Dec 17-20; Hanoi, Vietnam.
17. Biegelbauer, G., Vincze, M., Efficient 3d object detection by fitting superquadrics to range image data for robot's object manipulation. Proceedings of IEEE International Conference on Robotics and Automation, 2007, 1086–1091 Apr 10-14; Roma, Italy.