A new cost effective 3D measurement audit and model comparison system for verification tasks

  • Karthikeyan VaiapuryEmail author
  • Anil Aksay
  • Xinyu Lin
  • Ebroul Izquierdo
  • Christopher Papadopoulos


A new unified system application for the production audit in an aerospace industry is presented in this paper which comprises two key application tools such as (a) 3D PAMT (production audit measurement tool) and (b) 3D PACT (production audit compare tool). In spite of the facts that above functionalities are modular wise independent, commonly they are related in terms of assisting the production audit task. 3D PAMT facilitates the verification of manufactured parts to be within a pre-defined threshold range using a calibrated stereo camera with the safety test engineer interaction in order to select the matching disparity points. The distance between datum points with or without reference to a planar reference surface model can be obtained. We describe the system flow, plus validate the technique via a number of experimental datasets. 3D PACT allows the identification of discrepancies between a computed 3D point cloud model and the corresponding digital mock-up point cloud model. Usually, the computer aided geometry model is built before an actual installation. This knowledge about the components of an installation assembly is available as semantic information in an extendable markup language (XML) format of the CATIA model. We have provided an use case study of a sample assembly with components such as cube, pyramid, rectangular prism and triangular prism. The proposed cost-effective and robust framework for 3D measurement audit and model comparison is based on the input available from a digital camera and the semantic metadata knowledge available from geometry models which can be used for verification tasks.


3D measurement verification Installation production audit Model matching Industrial safety analysis 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Attene M., Falcidieno B., Spagnuolo M. (2006) Hierarchical mesh segmentation based on fitting primitives. The Visual Computer 22(3): 181–193CrossRefGoogle Scholar
  2. Bay H., Ess A., Tuytelaars T., Van Gool L. (2008) Speeded-up robust features (surf). Computer Vision and Image Understanding 110(3): 346–359CrossRefGoogle Scholar
  3. Bespalov, D., Regli, W. C., & Shokoufandeh, A. (2003). Reeb graph based shape retrieval for cad. In Proceedings of ASME design engineering technical conferences.Google Scholar
  4. Borrmann, D., Elseberg, J., Lingemann, K., & Nüchter, A. (2010). A data structure for the 3D hough transform for plane detection. In Proceedings of the 5th IFAC symposium on intelligent autonomous vehicles (IAV ’10), Lecce, Italy.Google Scholar
  5. Bradski, A. (2008). Learning openCV (1st ed.). Beijing: O’Reilly Media.Google Scholar
  6. Bretar F., Chehata N. (2010) Terrain modeling from lidar range data in natural landscapes: A predictive and bayesian framework. IEEE Transactions on Geoscience and Remote Sensing 48(3): 1568–1578CrossRefGoogle Scholar
  7. Chang, C.-C., & Lin, C.-J. (2011). LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2(3), 27:1–27:27.Google Scholar
  8. Faugeras O. (1993) Three-dimensional computer vision: A geometric viewpoint. MIT Press, Cambridge, MAGoogle Scholar
  9. Furukawa Y., Ponce J. (2010) Accurate, dense, and robust multiview stereopsis. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(8): 1362–1376CrossRefGoogle Scholar
  10. Georgel, P., Schroeder, P., Benhimane, S., Hinterstoisser, S., Appel, M., & Navab, N. (2007). An industrial augmented reality solution for discrepancy check. In Proceedings of the 2007 6th IEEE and ACM international symposium on mixed and augmented reality, ISMAR ’07, Washington, DC, USA, IEEE Computer Society, pp. 1–4.Google Scholar
  11. Heikkila, J., & Silven, O. (1997). A four-step camera calibration procedure with implicit image correction. In Proceedings of the 1997 conference on computer vision and pattern recognition (CVPR ’97), CVPR ’97, Washington, DC, USA, IEEE Computer Society, p. 1106.Google Scholar
  12. Ip C. Y., Regli W. C. (2006) A 3D object classifier for discriminating manufacturing processes. Computer & Graphics 30(6): 903–916CrossRefGoogle Scholar
  13. Izquierdo E., Ohm J.-R. (2000) Image-based rendering and 3D modeling: A complete framework. Signal Processing: Image Communication 15(10): 817–858CrossRefGoogle Scholar
  14. Lowe D. G. (2004) Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2): 91–110CrossRefGoogle Scholar
  15. Nagatomo, S., Hayashi, J., & Hata, S. (2009). Proposal of calibration error-tolerant 3d measurement method using stereo vision for robot vision. In International conference on mechatronics and automation, 2009. ICMA 2009, pp. 3263–3268.Google Scholar
  16. Nan, L., Ni, L., Haitao, L., & Chen, Y. (2010). A study on 3d measurement technology for apparels & accessories. International conference on networking and distributed computing, pp. 261–264.Google Scholar
  17. Osada R., Funkhouser T., Chazelle B., Dobkin D. (2002) Shape distributions. ACM Transactions on Graphics 21(4): 807–832CrossRefGoogle Scholar
  18. Rabbani, T., & van den Heuvel, F. A. (2005). Efficient hough transform for automatic detection of cylinders in point clouds. In Laser05.Google Scholar
  19. Schnabel, R., Wahl, R., & Klein, R. (2007). Efficient RANSAC for point-cloud shape detection. Computer Graphics Forum, 214–226.Google Scholar
  20. Shi, Y., Li, Z., & Wang, C. (2009). Real time complex 3d measurement. In International conference on computer modeling and simulation.Google Scholar
  21. Tarsha-kurdi, F., & Grussenmeyer, P. (2007). Hough-transform and extended ransac algorithms for automatic detection of 3D building roof planes from lidar data. In Part 3/W52.Google Scholar
  22. Tsai R. Y. (1987) A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses. IEEE Journal of Robotics and Automation 3(4): 323–344CrossRefGoogle Scholar
  23. Vaiapury K., Aksay A., Lin X., Izquierdo E. (2011) Model based 3D vision and analysis for production audit purposes. Infocommunications Journal 3(3): 1–8Google Scholar
  24. Vaiapury K., Aksay A., Lin X., Izquierdo E., Papadopoulos C. (2011) A vision based audit method and tool that compares a systems installation on a production aircraft to the original digital mock-up. SAE International Journal of Aerospace 4(2): 880–892Google Scholar
  25. Zhang Z. (2000) A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(11): 1330–1334CrossRefGoogle Scholar
  26. Zuliani, M. (2008). RANSAC for dummies.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Karthikeyan Vaiapury
    • 1
    Email author
  • Anil Aksay
    • 1
  • Xinyu Lin
    • 1
  • Ebroul Izquierdo
    • 1
  • Christopher Papadopoulos
    • 2
  1. 1.Multimedia and Vision Research Group, School of Electronic Engineering and Computer ScienceQueen Mary University of LondonLondonUK
  2. 2.Airbus Operations Ltd.FiltonUK

Personalised recommendations