Bundle Adjustment Constrained Smoothing for Multi-view Point Cloud Data

  • Kun Liu
  • Rhaleb Zayer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7431)


Direct use of denoising and mesh reconstruction algorithms on point clouds originating from multi-view images is often oblivious to the reprojection error. This can be a severe limitation in applications which require accurate point tracking, e.g., metrology.

In this paper, we propose a method for improving the quality of such data without forfeiting the original matches. We formulate the problem as a robust smoothness cost function constrained by a bounded reprojection error. The arising optimization problem is addressed as a sequence of unconstrained optimization problems by virtue of the barrier method. Substantiated experiments on synthetic and acquired data compare our approach to alternative techniques.


Point Cloud Point Cloud Data Bundle Adjustment Visual Hull Reprojection Error 
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 2012

Authors and Affiliations

  • Kun Liu
    • 1
  • Rhaleb Zayer
    • 1
  1. 1.InriaVillers-lès-NancyFrance

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