Improving 3D Keypoint Detection from Noisy Data Using Growing Neural Gas

  • José Garcia-Rodriguez
  • Miguel Cazorla
  • Sergio Orts-Escolano
  • Vicente Morell
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7903)


3D sensors provides valuable information for mobile robotic tasks like scene classification or object recognition, but these sensors often produce noisy data that makes impossible applying classical keypoint detection and feature extraction techniques. Therefore, noise removal and downsampling have become essential steps in 3D data processing. In this work, we propose the use of a 3D filtering and down-sampling technique based on a Growing Neural Gas (GNG) network. GNG method is able to deal with outliers presents in the input data. These features allows to represent 3D spaces, obtaining an induced Delaunay Triangulation of the input space. Experiments show how the state-of-the-art keypoint detectors improve their performance using GNG output representation as input data. Descriptors extracted on improved keypoints perform better matching in robotics applications as 3D scene registration.


GNG Noisy Point Cloud Visual Features Keypoint Detection Filtering 3D Scene Registration 


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  1. 1.
    Anne, M.: Treisman and Garry Gelade. A feature-integration theory of attention. Cognitive Psychology 12(1), 97–136 (1980)CrossRefGoogle Scholar
  2. 2.
    Szummer, M., Picard, R.W.: Indoor-outdoor image classification. In: Proceedings of the 1998 IEEE International Workshop Content-Based Access of Image and Video Database, pp. 42–51 (January 1998)Google Scholar
  3. 3.
    Nuchter, A., Surmann, H., Lingemann, K., Hertzberg, J., Thrun, S.: 6d slam with an application in autonomous mine mapping. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 1998–2003 (2004)Google Scholar
  4. 4.
    Connolly, C.: Cumulative generation of octree models from range data. In: Proceedings of the 1984 IEEE International Conference on Robotics and Automation, vol. 1, pp. 25–32 (March 1984)Google Scholar
  5. 5.
    Fritzke, B.: A Growing Neural Gas Network Learns Topologies, vol. 7, pp. 625–632. MIT Press (1995)Google Scholar
  6. 6.
    Holdstein, Y., Fischer, A.: Three-dimensional surface reconstruction using meshing growing neural gas (mgng). Vis. Comput. 24(4), 295–302 (2008)CrossRefGoogle Scholar
  7. 7.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60, 91–110 (2004)CrossRefGoogle Scholar
  8. 8.
    Mikolajczyk, K., Schmid, C.: An affine invariant interest point detector. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002, Part I. LNCS, vol. 2350, pp. 128–142. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  9. 9.
    Rusu, R.B., Blodow, N., Beetz, M.: Fast point feature histograms (fpfh) for 3d registration. In: IEEE International Conference on Robotics and Automation, ICRA 2009, pp. 3212–3217 (May 2009)Google Scholar
  10. 10.
    Tombari, F., Salti, S., Di Stefano, L.: A combined texture-shape descriptor for enhanced 3d feature matching. In: 2011 18th IEEE International Conference on Image Processing, ICIP, pp. 809–812 (September 2011)Google Scholar
  11. 11.
    Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Rusu, R.B., Cousins, S.: 3D is here: Point Cloud Library (PCL). In: Proceedings of the IEEE International Conference on Robotics and Automation, ICRA, Shanghai, China, May 9-13 (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • José Garcia-Rodriguez
    • 1
  • Miguel Cazorla
    • 2
  • Sergio Orts-Escolano
    • 1
  • Vicente Morell
    • 2
  1. 1.Department of Computing TechnologyUniversity of AlicanteSpain
  2. 2.Instituto de Investigación en InformáticaUniversity of AlicanteSpain

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