A study on scan data matching for reverse engineering of pipes in plant construction

The domestic plant construction market is in a prosperous condition, due to the uprising demand for industry facility and infrastructure, triggered by the oil price rise in Middle East Asia. It is difficult, however, to survive in competition with advanced foreign construction companies, because domestic companies rely heavily on labor-intensive construction conventions in AEC(Architectural, Engineering and Construction industry). In particular, the piping work in a plant construction project accounts for many portions of the critical paths, from the viewpoint of process control. Although plant construction projects need to deal with complex three-dimensional piping information, most domestic companies rely on two-dimensional CAD drawings for construction and quality control, which frequently leads to significant problems, such as construction delay, and productivity decrease. For such reasons, it is problematic, in that the build ability, workability, and productivity of projects are dependent on the workers’ abilities to understand drawings and their technical skill. In other words, as plant construction projects are getting more complicated in details, and more increased in size, it is urgently needed to develop an efficient information management methodology, in order to figure out heterogeneous 3D piping information in a real-time manner. Reverse engineering for obtaining 3D shape information using a 3D laser scanner is used, in order to extract drawing information from constructed objects. In particular, as piping works control the Critical Path (CP) in the plant construction project, in this study, a reverse engineering algorithm is proposed, which can measure the preciseness of pipes, by comparing the 3D CAD model and the 3D shape information obtained from a laser scanner. Applying the algorithm can contribute to re-construction prevention and quality control in the construction process.

[1]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Robert B. Fisher,et al.  Object reconstruction by incorporating geometric constraints in reverse engineering , 1999, Comput. Aided Des..

[3]  Marc Levoy,et al.  Efficient variants of the ICP algorithm , 2001, Proceedings Third International Conference on 3-D Digital Imaging and Modeling.

[4]  Dar-Yuan Chang,et al.  A Freeform Surface Modelling System Based on Laser Scan Data for Reverse Engineering , 2002 .

[5]  Linda G. Shapiro,et al.  A new paradigm for recognizing 3-D objects from range data , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[6]  Soon-Wook Kwon,et al.  Fitting range data to primitives for rapid local 3D modeling using sparse range point clouds , 2004 .

[7]  A. Grün,et al.  LEAST SQUARES 3D SURFACE MATCHING , 2004 .

[8]  Choi Jong-Soo,et al.  3D Model Retrieval Using Geometric Information , 2005 .

[9]  Zhengyou Zhang,et al.  Iterative point matching for registration of free-form curves and surfaces , 1994, International Journal of Computer Vision.

[10]  S S Han,et al.  Application of Reverse Engineering on Sheet Metal Forming Analysis , 2005 .

[11]  Burcu Akinci,et al.  Automating the task of tracking the delivery and receipt of fabricated pipe spools in industrial projects , 2006 .

[12]  Armin Gruen,et al.  FAST CORRESPONDENCE SEARCH FOR 3D SURFACE MATCHING , 2006 .

[13]  X J Cheng,et al.  Study on Reverse Engineering of Historical Architecture Based on 3D Laser Scanner , 2006 .

[14]  Choi Jin Won,et al.  Construction Management System Using the Mobile Augmented Reality Techniques , 2006 .

[15]  Myung Sagong,et al.  A detection algorithm for the installations and damages on a tunnel liner using the laser scanning data , 2007 .

[16]  Jaeone Lee,et al.  Tunnel Convergence and Crown Settlement Using 3D Laser Scanning , 2007 .

[17]  Sen Wang,et al.  Conformal Geometry and Its Applications on 3D Shape Matching, Recognition, and Stitching , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  김봉근,et al.  Recognition and Localization of Steel Frame Material Using Scan Matching Method for Automation of Steel Construction , 2009 .

[19]  Guzide Atasoy,et al.  A comparative study on the use of laser scanners for construction quality control and progress monitoring purposes , 2009 .

[20]  Burcu Akinci,et al.  Automatic Reconstruction of As-Built Building Information Models from Laser-Scanned Point Clouds: A Review of Related Techniques | NIST , 2010 .

[21]  Jae-Chon Lee,et al.  On Improving the Reverse Engineering Process by Effectively Utilizing the Functional Models , 2010 .

[22]  Adam Finkelstein,et al.  Patchmatch: a fast randomized matching algorithm with application to image and video , 2011 .

[23]  Soon-Wook Kwon,et al.  Development of Optimized Point Clouds Merging Method for Rapid Processing to Generate Earthwork Site Model , 2011 .

[24]  Do-Hoon Kim,et al.  The construction management of tunnel using 3D laser scanner , 2011 .

[25]  Carl T. Haas,et al.  Automatic Detection of Cylindrical Objects in Built Facilities , 2014, J. Comput. Civ. Eng..