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“Bundle Adjustment Constrained Smoothing For Multi-View PointCloud Data”
Kun Liu and Rhaleb Zayer
8th International Symposium on Visual Computing (ISVC), 2012

Abstract: 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.

BibTex reference

@INPROCEEDINGS{BACS-isvc2012,
   AUTHOR     = "Liu, Kun and Zayer, Rhaleb",
   TITLE      = "Bundle Adjustment Constrained Smoothing For Multi-View PointCloud Dat
                   a",
   BOOKTITLE  = "8th International Symposium on Visual Computing (ISVC)",
   YEAR       = "2012",
   ADDRESS    = "Rethymnon, Crete, Greece",
   MONTH      = "July",
   PUBLISHER  = "Springer",
   SERIES     = "Lecture Notes in Computer Science",
}