Continuous Surface-Point Distributions for 3D Object Pose Estimation and Recognition

  • Renaud Detry
  • Justus Piater
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6494)


We present a 3D, probabilistic object-surface model, along with mechanisms for probabilistically integrating unregistered 2.5D views into the model, and for segmenting model instances in cluttered scenes. The object representation is a probabilistic expression of object parts through smooth surface-point distributions obtained by kernel density estimation on 3D point clouds. A multi-part, viewpoint-invariant model is learned incrementally from a set of roughly segmented, unregistered views, by sequentially registering and fusing the views with the incremental model. Registration is conducted by nonparametric inference of maximum-likelihood model parameters, using Metropolis–Hastings MCMC with simulated annealing. The learning of viewpoint-invariant models and the applicability of our method to pose estimation, object detection, and object recognition is demonstrated on 3D-scan data, providing qualitative, quantitative and comparative evaluations.


Simulated Annealing Object Recognition Object Detection Object Representation Kernel Density Estimation 
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

  • Renaud Detry
    • 1
  • Justus Piater
    • 1
  1. 1.University of LiègeBelgium

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