Linearly converging quasi branch and bound algorithms for global rigid registration

Published

Journal Article

© 2019 IEEE. In recent years, several branch-and-bound (BnB) algorithms have been proposed to globally optimize rigid registration problems. In this paper, we suggest a general framework to improve upon the BnB approach, which we name emph{Quasi BnB}. Quasi BnB replaces the linear lower bounds used in BnB algorithms with quadratic quasi-lower bounds which are based on the quadratic behavior of the energy in the vicinity of the global minimum. While quasi-lower bounds are not truly lower bounds, the Quasi-BnB algorithm is globally optimal. In fact we prove that it exhibits linear convergence - it achieves epsilon accuracy in O(log(1/epsilon)) time while the time complexity of other rigid registration BnB algorithms is polynomial in 1/epsilon. Our experiments verify that Quasi-BnB is significantly more efficient than state-of-the-art BnB algorithms, especially for problems where high accuracy is desired.

Full Text

Duke Authors

Cited Authors

  • Dym, N; Kovalsky, S

Published Date

  • October 1, 2019

Published In

Volume / Issue

  • 2019-October /

Start / End Page

  • 1628 - 1636

International Standard Serial Number (ISSN)

  • 1550-5499

Digital Object Identifier (DOI)

  • 10.1109/ICCV.2019.00171

Citation Source

  • Scopus