An Efficient RANSAC for 3D Object Recognition in Noisy and Occluded Scenes

  • Chavdar Papazov
  • Darius Burschka
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6492)


In this paper, we present an efficient algorithm for 3D object recognition in presence of clutter and occlusions in noisy, sparse and unsegmented range data. The method uses a robust geometric descriptor, a hashing technique and an efficient RANSAC-like sampling strategy. We assume that each object is represented by a model consisting of a set of points with corresponding surface normals. Our method recognizes multiple model instances and estimates their position and orientation in the scene. The algorithm scales well with the number of models and its main procedure runs in linear time in the number of scene points. Moreover, the approach is conceptually simple and easy to implement. Tests on a variety of real data sets show that the proposed method performs well on noisy and cluttered scenes in which only small parts of the objects are visible.


Object Recognition Recognition Rate Hash Table Range Image Spin Image 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Chavdar Papazov
    • 1
  • Darius Burschka
    • 1
  1. 1.Technische Universität München (TUM)Germany

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