SOAR: Stochastic optimization for affine global point set registration

Marco Agus, E. Gobbetti, A. Jaspe Villanueva, C. Mura, R. Pajarola

Research output: Contribution to conferencePaperpeer-review

3 Scopus citations

Abstract

We introduce a stochastic algorithm for pairwise affine registration of partially overlapping 3D point clouds with unknown point correspondences. The algorithm recovers the globally optimal scale, rotation, and translation alignment parameters and is applicable in a variety of difficult settings, including very sparse, noisy, and outlierridden datasets that do not permit the computation of local descriptors. The technique is based on a stochastic approach for the global optimization of an alignment error function robust to noise and resistant to outliers. At each optimization step, it alternates between stochastically visiting a generalized BSP-tree representation of the current solution landscape to select a promising transformation, finding point-to-point correspondences using a GPU-accelerated technique, and incorporating new error values in the BSP tree. In contrast to previous work, instead of simply constructing the tree by guided random sampling, we exploit the problem structure through a low-cost local minimization process based on analytically solving absolute orientation problems using the current correspondences. We demonstrate the quality and performance of our method on a variety of large point sets with different scales, resolutions, and noise characteristics.

Original languageEnglish (US)
Pages103-110
Number of pages8
DOIs
StatePublished - Jan 1 2014
Event19th International Workshop on Vision, Modeling and Visualization, VMV 2014 - Darmstadt, Germany
Duration: Oct 8 2014Oct 10 2014

Other

Other19th International Workshop on Vision, Modeling and Visualization, VMV 2014
CountryGermany
CityDarmstadt
Period10/8/1410/10/14

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Modeling and Simulation

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