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.


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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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