Neural Processing Letters

, Volume 43, Issue 2, pp 401–423 | Cite as

3D Surface Reconstruction of Noisy Point Clouds Using Growing Neural Gas: 3D Object/Scene Reconstruction

  • Sergio Orts-Escolano
  • Jose Garcia-Rodriguez
  • Vicente Morell
  • Miguel Cazorla
  • Jose Antonio Serra Perez
  • Alberto Garcia-Garcia


With the advent of low-cost 3D sensors and 3D printers, scene and object 3D surface reconstruction has become an important research topic in the last years. In this work, we propose an automatic (unsupervised) method for 3D surface reconstruction from raw unorganized point clouds acquired using low-cost 3D sensors. We have modified the growing neural gas network, which is a suitable model because of its flexibility, rapid adaptation and excellent quality of representation, to perform 3D surface reconstruction of different real-world objects and scenes. Some improvements have been made on the original algorithm considering colour and surface normal information of input data during the learning stage and creating complete triangular meshes instead of basic wire-frame representations. The proposed method is able to successfully create 3D faces online, whereas existing 3D reconstruction methods based on self-organizing maps required post-processing steps to close gaps and holes produced during the 3D reconstruction process. A set of quantitative and qualitative experiments were carried out to validate the proposed method. The method has been implemented and tested on real data, and has been found to be effective at reconstructing noisy point clouds obtained using low-cost 3D sensors.


GNG 3D reconstruction Low-cost 3D sensor Scene reconstruction  Object reconstruction 



This work was partially funded by the Spanish Government DPI2013-40534-R Grant.


  1. 1.
    Hoppe H, DeRose T, Duchamp T, McDonald J, Stuetzle W (1992) Surface reconstruction from unorganized points. SIGGRAPH Comput Graph 26(2):71–78, ISSN 0097–8930, doi: 10.1145/142920.134011
  2. 2.
    Amenta N, Choi S, Kolluri RK (2001) The power crust. In: Proceedings of the sixth ACM symposium on solid modeling and applications, SMA ’01, ACM, New York, NY, USA, pp 249–266, ISBN 1-58113-366-9, doi: 10.1145/376957.376986
  3. 3.
    Berger M, Tagliasacchi A, Seversky LM, Alliez P, Levine JA, Sharf A, Silva C (2014) State of the art in surface reconstruction from point clouds. In: Proceedings of eurographics state-of-the-art reports (EG’14), Springer, New YorkGoogle Scholar
  4. 4.
    Yu Y (1999) Surface reconstruction from unorganized points using self-organizing neural networks. In: Yu Y (ed) Proceedings of the IEEE visualization 99 conference, San Francisco, pp 61–64Google Scholar
  5. 5.
    Junior A, Neto ADD, de Melo J (2004) Surface reconstruction using neural networks and adaptive geometry meshes. In: Proceedings of the international joint conference on neural networks, vol 1–807, Budapest, ISSN 1098–7576, doi: 10.1109/IJCNN.2004.1380023
  6. 6.
    Fritzke B (1993) Growing cell structures—a self-organizing network for unsupervised and supervised learning. Neural Netw 7:1441–1460CrossRefGoogle Scholar
  7. 7.
    Ivrissimtzis I, Jeong WK, Seidel HP (2003) Using growing cell structures for surface reconstruction. In: Proceedings of the shape modeling international, IEEE Computer Society, Seoul, pp 78–86Google Scholar
  8. 8.
    Martinetz T, Schulten K (1994) Topology representing networks. Neural Netw 7(3):507–522CrossRefGoogle Scholar
  9. 9.
    Barhak J (2002) Freeform objects with arbitrary topology from multirange images. Ph.D. thesis, Israel Institute of Technology, Haifa, IsraelGoogle Scholar
  10. 10.
    Fritzke B (1995) A growing neural gas network learns topologies, vol 7. MIT Press, CambridgeGoogle Scholar
  11. 11.
    Cretu AM, Petriu EM, Payeur P (2008) Evaluation of growing neural gas networks for selective 3D scanning. In: Proceedings of the international workshop robotic and sensors environments ROSE 2008, Vancouver, pp 108–113Google Scholar
  12. 12.
    Holdstein Y, Fischer A (2008) Three-dimensional surface reconstruction using meshing growing neural gas (MGNG). Vis Comput 24:295–302CrossRefGoogle Scholar
  13. 13.
    Do Rego RLME, Araujo AFR, De Lima Neto FB (2010) Growing self-reconstruction maps. Trans Neural Netw 21(2):211–223, ISSN 1045–9227, doi: 10.1109/TNN.2009.2035312
  14. 14.
    Orts-Escolano S, Garcia-Rodriguez J, Morell V, Cazorla M, Garcia-Chamizo JM (2014) 3D colour object reconstruction based on growing neural gas. In: Proceedings of 2014 international joint conference on neural networks, IJCNN 2014, Beijing, China, July 6–11, 2014, pp 1474–1481, doi: 10.1109/IJCNN.2014.6889546
  15. 15.
    Kazhdan M, Bolitho M, Hoppe H (2006) Poisson surface reconstruction. In: Proceedings of the fourth eurographics symposium on geometry processing, SGP ’06, Eurographics Association, Aire-la-Ville, Switzerland, 61–70, ISBN 3-905673-36-3Google Scholar
  16. 16.
    Rusu RB, Blodow N, Beetz M (2009) Fast point feature histograms (FPFH) for 3D registration. In: Proceedings of IEEE international conference on robotics and automation 2009, ICRA ’09, pp 3212–3217, ISSN 1050–4729, doi: 10.1109/ROBOT.2009.5152473
  17. 17.
    Tombari F, Salti S (2011) A combined texture-shape descriptor for enhanced 3D feature matching. In: Proceedings of 18th IEEE international conference on image processing (ICIP), Brussels, pp 809–812, ISSN 1522–4880, doi: 10.1109/ICIP.2011.6116679
  18. 18.
    Mian AS, Bennamoun M, Owens RA (2006) A novel representation and feature matching algorithm for automatic pairwise registration of range images. Int J Comput Vis 66(1):19–40, ISSN 0920–5691, doi: 10.1007/s11263-005-3221-0
  19. 19.
    Orts-Escolano S, Morell V, Garcia-Rodriguez J, Cazorla M (2013) Point cloud data filtering and downsampling using growing neural gas. In: Proceedings of the the 2013 international joint conference on neural networks, IJCNN 2013, Dallas, TX, USA, August 4–9, 2013, pp 1–8, doi: 10.1109/IJCNN.2013.6706719
  20. 20.
    Jolliffe I (1986) Principal component analysis. Springer Verlag, New YorkCrossRefMATHGoogle Scholar
  21. 21.
    Mole VLD, Araújo AFR (2010) Growing self-organizing surface map: learning a surface topology from a point cloud. Neural Comput 22(3):689–729, ISSN 0899–7667, doi: 10.1162/neco.2009.08-08-842
  22. 22.
    Cignoni P, Rocchini C, Scopigno R (1996) Metro: Measuring Error on Simplified Surfaces. Tech. rep, Paris, France, FranceGoogle Scholar
  23. 23.
    Rusu RB, Cousins S (2011) 3D is here: point cloud library (PCL). In: Proceedings of the IEEE international conference on robotics and automation (ICRA), Shanghai, ChinaGoogle Scholar
  24. 24.
    Tombari F, Salti S, Di Stefano L (2010) Unique signatures of histograms for local surface description. In: Proceedings of the 11th European conference on computer vision conference on computer vision: Part III, ECCV’10, Springer-Verlag, Berlin, Heidelberg, pp 356–369, ISBN 978–3-642-15557-X-642-15557-4Google Scholar
  25. 25.
    Mian AS, Bennamoun M, Owens R (2006) Three-dimensional model-based object recognition and segmentation in cluttered scenes. IEEE Trans Pattern Anal Mach Intell 28(10):1584–1601, ISSN 0162–8828, doi: 10.1109/TPAMI.2006.213
  26. 26.
    Gray A (1996) Modern differential geometry of curves and surfaces with mathematica, 1st edn. CRC Press Inc, Boca Raton, ISBN 0849371643Google Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Sergio Orts-Escolano
    • 1
  • Jose Garcia-Rodriguez
    • 1
  • Vicente Morell
    • 2
  • Miguel Cazorla
    • 2
  • Jose Antonio Serra Perez
    • 1
  • Alberto Garcia-Garcia
    • 1
  1. 1.Department of Computer TechnologyUniversity of AlicanteAlicanteSpain
  2. 2.Department of Computer Science and Artificial IntelligenceUniversity of AlicanteAlicanteSpain

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