MRF Labeling for Multi-view Range Image Integration

  • Ran Song
  • Yonghuai Liu
  • Ralph R. Martin
  • Paul L. Rosin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6493)


Multi-view range image integration focuses on producing a single reasonable 3D point cloud from multiple 2.5D range images for the reconstruction of a watertight manifold surface. However, registration errors and scanning noise usually lead to a poor integration and, as a result, the reconstructed surface cannot have topology and geometry consistent with the data source. This paper proposes a novel method cast in the framework of Markov random fields (MRF) to address the problem. We define a probabilistic description of a MRF labeling based on all input range images and then employ loopy belief propagation to solve this MRF, leading to a globally optimised integration with accurate local details. Experiments show the advantages and superiority of our MRF-based approach over existing methods.


Point Cloud Markov Random Field Range Image Registration Error Markov Random Field Model 
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

  • Ran Song
    • 1
  • Yonghuai Liu
    • 1
  • Ralph R. Martin
    • 2
  • Paul L. Rosin
    • 2
  1. 1.Department of Computer ScienceAberystwyth UniversityUK
  2. 2.School of Computer Science & InformaticsCardiff UniversityUK

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