Set Distance Functions for 3D Object Recognition

  • Luís A. Alexandre
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8258)


One of the key steps in 3D object recognition is the matching between an input cloud and a cloud in a database of known objects. This is usually done using a distance function between sets of descriptors. In this paper we propose to study how several distance functions (some already available and other new proposals) behave experimentally using a large freely available household object database containing 1421 point clouds from 48 objects and 10 categories. We present experiments illustrating the accuracy of the distances both for object and category recognition and find that simple distances give competitive results both in terms of accuracy and speed.


Distance Function Point Cloud Object Recognition Local Reference Frame Simple Distance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Grauman, K., Darrell, T.: The pyramid match kernel: Efficient learning with sets of features. Journal of Machine Learning Research 8, 725–760 (2007)zbMATHGoogle Scholar
  2. 2.
    Rusu, R., Cousins, S.: 3D is here: Point Cloud Library (PCL). In: IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China (2011)Google Scholar
  3. 3.
    Alexandre, L.A.: 3D descriptors for object and category recognition: a comparative evaluation. In: Workshop on Color-Depth Camera Fusion in Robotics at the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vilamoura, Portugal (2012)Google Scholar
  4. 4.
    Rusu, R., Blodow, N., Marton, Z., Beetz, M.: Aligning point cloud views using persistent feature histograms. In: International Conference on Intelligent Robots and Systems (IROS), Nice, France (2008)Google Scholar
  5. 5.
    Tombari, F., Salti, S., Di Stefano, L.: A combined texture-shape descriptor for enhanced 3D feature matching. In: IEEE International Conference on Image Processing (2011)Google Scholar
  6. 6.
    Tombari, F., Salti, S., Di Stefano, L.: Unique signatures of histograms for local surface description. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part III. LNCS, vol. 6313, pp. 356–369. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  7. 7.
    Lai, K., Bo, L., Ren, X., Fox, D.: A Large-Scale hierarchical Multi-View RGB-D object dataset. In: Proc. of the IEEE International Conference on Robotics & Automation, ICRA (2011)Google Scholar
  8. 8.
    Lee, J.J.: Libpmk: A pyramid match toolkit. Technical Report MIT-CSAIL-TR-2008-17, MIT Computer Science and Artificial Intelligence Laboratory (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Luís A. Alexandre
    • 1
  1. 1.Instituto de TelecomunicaçõesUniv. Beira InteriorCovilhãPortugal

Personalised recommendations