Bundle Adjustment Constrained Smoothing for Multi-view Point Cloud Data

Kun Liu 1, * Rhaleb Zayer 1
* Auteur correspondant
1 ALICE - Geometry and Lighting
Inria Nancy - Grand Est, LORIA - ALGO - Department of Algorithms, Computation, Image and Geometry
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.
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Communication dans un congrès
SVC 2012, 8th International Symposium on Visual Computing, Jul 2012, Rethymnon, Crete, Greece. 7431, pp.126-137, 2012, Lecture Notes in Computer Science (LNCS). 〈10.1007/978-3-642-33179-4_13〉
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Dernière modification le : jeudi 11 janvier 2018 - 06:25:23
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Kun Liu, Rhaleb Zayer. Bundle Adjustment Constrained Smoothing for Multi-view Point Cloud Data. SVC 2012, 8th International Symposium on Visual Computing, Jul 2012, Rethymnon, Crete, Greece. 7431, pp.126-137, 2012, Lecture Notes in Computer Science (LNCS). 〈10.1007/978-3-642-33179-4_13〉. 〈hal-00763442〉

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