3D Research

, 8:30 | Cite as

Fast and Easy 3D Reconstruction with the Help of Geometric Constraints and Genetic Algorithms

  • Afafe Annich
  • Abdellatif El Abderrahmani
  • Khalid Satori
3DR Express


The purpose of the work presented in this paper is to describe new method of 3D reconstruction from one or more uncalibrated images. This method is based on two important concepts: geometric constraints and genetic algorithms (GAs). At first, we are going to discuss the combination between bundle adjustment and GAs that we have proposed in order to improve 3D reconstruction efficiency and success. We used GAs in order to improve fitness quality of initial values that are used in the optimization problem. It will increase surely convergence rate. Extracted geometric constraints are used first to obtain an estimated value of focal length that helps us in the initialization step. Matching homologous points and constraints is used to estimate the 3D model. In fact, our new method gives us a lot of advantages: reducing the estimated parameter number in optimization step, decreasing used image number, winning time and stabilizing good quality of 3D results. At the end, without any prior information about our 3D scene, we obtain an accurate calibration of the cameras, and a realistic 3D model that strictly respects the geometric constraints defined before in an easy way. Various data and examples will be used to highlight the efficiency and competitiveness of our present approach.

Graphical Abstract


3D reconstruction Genetic algorithms (GAs) Vanishing points Geometric constraints Bundle adjustment Structured scenes 


  1. 1.
    Andrew, A. M. (2001). Multiple view geometry in computer vision, by Richard Hartley and Andrew Zisserman, Cambridge University Press, Cambridge, 2000, xvi + 607 pp., ISBN 0–521–62304–9 (hardback, £60.00). Robotica. doi: 10.1017/s0263574700223217.Google Scholar
  2. 2.
  3. 3.
  4. 4.
    Chen, C.-S., Yu, C.-K., & Hung, Y.-P. (1999). New calibration-free approach for augmented reality based on parameterized cuboid structure. In Proceedings of the seventh IEEE international conference on computer vision. doi: 10.1109/iccv.1999.791194.
  5. 5.
    Debevec, P. E., Taylor, C. J. & Malik, J. (1996). Modelling and rendering architecture from photographs. In Proceedings of the 23rd annual conference on Computer graphics and interactive techniques - SIGGRAPH ’96. doi: 10.1145/237170.237191.
  6. 6.
    Dick, A. R. et al. (2001) Combining single view recognition and multiple view stereo for architectural scenes. In Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001. doi: 10.1109/iccv.2001.937528.
  7. 7.
    Navab, N., & Appel, M. (2006). Canonical representation and multi-view geometry of cylinders. International Journal of Computer Vision, 70(2), 133–149. doi: 10.1007/s11263-006-7935-4.CrossRefGoogle Scholar
  8. 8.
    Wilczkowiak, M., Boyer, E., & Sturm, P. (2001) Camera calibration and 3D reconstruction from single images using parallelepipeds. In Proceedings eighth IEEE international conference on computer vision. ICCV 2001. doi: 10.1109/iccv.2001.937510.
  9. 9.
    Lhuillier, M., & Quan, L. (2003). Image-based rendering by joint view triangulation. IEEE Transactions on Circuits and Systems for Video Technology, 13(11), 1051–1063. doi: 10.1109/tcsvt.2003.817355.CrossRefGoogle Scholar
  10. 10.
    Mahamud, S., & Hebert, M. (2000). Iterative projective reconstruction from multiple views. In Proceedings IEEE conference on computer vision and pattern recognition. CVPR 2000 (Cat. No.PR00662). doi: 10.1109/cvpr.2000.854872.
  11. 11.
    Pollefeys, M. et al. (1998). Metric 3D surface reconstruction from uncalibrated image sequences. In Lecture notes in computer science (pp. 139–154). doi: 10.1007/3-540-49437-5_10.
  12. 12.
    Wilczkowiak, M., Sturm, P., & Boyer, E. (2003). The analysis of ambiguous solutions in linear systems and its application to computer vision. In Proceedings of the British machine vision conference 2003. doi: 10.5244/c.17.9.
  13. 13.
    Bartoli, A. (2003). Reconstruction et alignement en vision 3D : points, droites, plans, caméras. Phd thesis, Institut national polytechnique de grenoble, septembre 2003.,d.ZWU.
  14. 14.
    Werner, T., & Zisserman, A. (2002). Model selection for automated reconstruction from multiple views. In Proceedings of the British machine vision conference 2002. doi: 10.5244/c.16.3.
  15. 15.
    Werner, T., & Zisserman, A. (2002). New techniques for automated architectural reconstruction from photographs. In Lecture notes in computer science (pp. 541–555). doi: 10.1007/3-540-47967-8_36.
  16. 16.
    Habbecke, M., & Kobbelt, L. (2012). Linear analysis of nonlinear constraints for interactive geometric modeling. Computer Graphics Forum, 31(2pt3), 641–650. doi: 10.1111/j.1467-8659.2012.03043.CrossRefGoogle Scholar
  17. 17.
    Vouzounaras, G., et al. (2010). 3D reconstruction of indoor and outdoor building scenes from a single image. In Proceedings of the 2010 ACM workshop on Surreal media and virtual cloning—SMVC ’10. doi: 10.1145/1878083.1878100.
  18. 18.
    Bondyfalat, D., & Bougnoux, S. (1998). Imposing euclidean constraints during self-calibration processes. Lecture Notes in Computer Science. doi: 10.1007/3-540-49437-5_15.Google Scholar
  19. 19.
    Sparr, G. (1998). Euclidean and affine structure/motion for uncalibrated cameras from affine shape and subsidiary information. In Lecture notes in computer science (pp. 187–207). doi: 10.1007/3-540-49437-5_13.
  20. 20.
    Szeliski, R., & Torr, P. H. S. (1998). Geometrically constrained structure from motion: Points on planes. In Lecture notes in computer science (pp. 171–186). doi: 10.1007/3-540-49437-5_12.
  21. 21.
    Wilczkowiak, M., Boyer, E., & Sturm, P. (2002). 3D modelling using geometric constraints: A parallelepiped based approach. In Lecture notes in computer science (pp. 221–236). doi: 10.1007/3-540-47979-1_15.
  22. 22.
    Wilczkowiak, M., Sturm, P., & Boyer, E. (2005). Using geometric constraints through parallelepipeds for calibration and 3D modeling. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(2), 194–207. doi: 10.1109/tpami.2005.40.CrossRefGoogle Scholar
  23. 23.
    Zou, C., et al. (2015). Sketch-based 3-D modelling for piecewise planar objects in single images. Computers and Graphics, 46, 130–137. doi: 10.1016/j.cag.2014.09.031.CrossRefGoogle Scholar
  24. 24.
    Annich, A., Elabderrahmani, A., & Satori, K. (2013). Reconstruction euclidienne des scènes 3D À l’aide des contraintes géométriques. In Proceedings of 4th workshop on codes, cryptography and communication systems (WCCCS13), EST-Meknès 7–8 November 2013.Google Scholar
  25. 25.
    Annich, A., El Abderrahmani, A., & Satori, K. (2015). Enhancement of 3D reconstruction process in terms of beautification and efficiency using geometric constraints. In 2015 Intelligent Systems and Computer Vision (ISCV). doi: 10.1109/isacv.2015.7106180.
  26. 26.
    Cornou, S., Dhome, M., & Sayd, P. (2004). 3D modelling of a building from a set of uncalibrated images with help of constrains. In 2004—ISPRS workshop on vision techniques applied to the rehabilitation of city centres, Lisbon, Portugal, October.
  27. 27.
    Coley, D. A. (1999). An introduction to genetic algorithms for scientists and engineers. Singapore: World Scientific Publishing Co Inc. doi: 10.1142/3904.CrossRefGoogle Scholar
  28. 28.
    Fogel, D. (1997). An Introduction to Genetic Algorithms Melanie Mitchell. MIT Press, Cambridge MA, 1996. $30.00 (cloth), 270 Pp. Bulletin of Mathematical Biology, 59(1), 199–204. doi: 10.1016/s0092-8240(96)00095-x.CrossRefGoogle Scholar
  29. 29.
    O’Neill, M. (2008). Riccardo Poli, William B. Langdon, Nicholas F. McPhee: A field guide to genetic programming. Genetic Programming and Evolvable Machines, 10(2), 229–230. doi: 10.1007/s10710-008-9073-y.CrossRefGoogle Scholar
  30. 30.
    Wright, A. H. (1991). Genetic algorithms for real parameter optimization. Foundations of Genetic Algorithms. doi: 10.1016/b978-0-08-050684-5.50016-1.Google Scholar
  31. 31.
    Cornou, S., Dhome, M., & Sayd, P. (2002). Bundle adjustment: A fast method with weak initialisation. In Proceedings of the British machine vision conference 2002. doi: 10.5244/c.16.20.
  32. 32.
    Triggs, B., et al. (2000). Bundle adjustment—A modern synthesis. In Lecture notes in computer science (pp. 298–372). doi: 10.1007/3-540-44480-7_21.
  33. 33.
    El Akkad, N., et al. (2016). Reconstruction of 3D scenes by camera self-calibration and using genetic algorithms. 3D Research. doi: 10.1007/s13319-016-0082-y.Google Scholar
  34. 34.
    David, P., et al. (2004). SoftPOSIT: Simultaneous pose and correspondence determination. International Journal of Computer Vision, 59(3), 259–284. doi: 10.1023/b:visi.0000025800.10423.1f.CrossRefGoogle Scholar
  35. 35.
    Dementhon, D. F., & Davis, L. S. (1995). Model-based object pose in 25 lines of code. International Journal of Computer Vision, 15(1–2), 123–141. doi: 10.1007/bf01450852.CrossRefGoogle Scholar
  36. 36.
    Annich, A., Elabderrahmani, A., & Satori, K. (2014) New simple approach of vanishing points detection in architectural environments. In Proceedings of 7th international symposium on signal, image, video and communications (ISIVC 2014) ENSA-Marrakesh, Morocco 19–21 November 2014.Google Scholar
  37. 37.
    Annich, A., Abderrahmani, A. E., & Satori, K. (2016). Important approach to vanishing points detection based on simple image geometry and new accumulation space. International Journal of Imaging and Robotics™, 16(1), 64–81.Google Scholar
  38. 38.
    Spears, W. M. (1993). Crossover or mutation? Foundations of Genetic Algorithms. doi: 10.1016/b978-0-08-094832-4.50020-9.Google Scholar
  39. 39.
    De Jong, K. A. (1975). An analysis of the behavior of a class of genetic adaptive systems. Ph.D. Thesis, University of Michigan, Ann Arbor.Google Scholar
  40. 40.
    Grefenstette, J. J. (1986). Optimization of control parameters for genetic algorithms. IEEE Transactions on Systems, Man, and Cybernetics, 16(1), 122–128.CrossRefGoogle Scholar
  41. 41.
    Goldberg, D. E. (1989). Sizing populations for serial and parallel genetic algorithms. In J. D. Schaffer (Ed.), Proceedings of the third international conference on genetic algorithms. Morgan Kaufmann.Google Scholar
  42. 42.

Copyright information

© 3D Research Center, Kwangwoon University and Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Afafe Annich
    • 1
  • Abdellatif El Abderrahmani
    • 1
    • 2
  • Khalid Satori
    • 1
  1. 1.LIIAN, Department of Computer Sciences Dhar-Mahraz Sciences SchoolUniversity Sidi Mohammed Ben AbdellahAtlas-FezMorocco
  2. 2.Larache Poly Disciplinary SchoolLaracheMorocco

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