A family of globally optimal branch-and-bound algorithms for 2D–3D correspondence-free registration
Article
Brown, M., Windridge, D. and Guillemaut, J. 2019. A family of globally optimal branch-and-bound algorithms for 2D–3D correspondence-free registration. Pattern Recognition. 93, pp. 36-54. https://doi.org/10.1016/j.patcog.2019.04.002
Type | Article |
---|---|
Title | A family of globally optimal branch-and-bound algorithms for 2D–3D correspondence-free registration |
Authors | Brown, M., Windridge, D. and Guillemaut, J. |
Abstract | We present a family of methods for 2D–3D registration spanning both deterministic and non-deterministic branch-and-bound approaches. Critically, the methods exhibit invariance to the underlying scene primitives, enabling e.g. points and lines to be treated on an equivalent basis, potentially enabling a broader range of problems to be tackled while maximising available scene information, all scene primitives being simultaneously considered. Being a branch-and-bound based approach, the method furthermore enjoys intrinsic guarantees of global optimality; while branch-and-bound approaches have been employed in a number of computer vision contexts, the proposed method represents the first time that this strategy has been applied to the 2D–3D correspondence-free registration problem from points and lines. Within the proposed procedure, deterministic and probabilistic procedures serve to speed up the nested branch-and-bound search while maintaining optimality. Experimental evaluation with synthetic and real data indicates that the proposed approach significantly increases both accuracy and robustness compared to the state of the art. |
Keywords | 2D-3D registration; Multi-modal registration; Branch-and-bound; Global optimisation |
Publisher | Elsevier |
Journal | Pattern Recognition |
ISSN | 0031-3203 |
Electronic | 1873-5142 |
Publication dates | |
Online | 04 Apr 2019 |
Sep 2019 | |
Publication process dates | |
Deposited | 20 May 2020 |
Accepted | 04 Apr 2019 |
Submitted | 17 Aug 2018 |
Output status | Published |
Publisher's version | License File Access Level Open |
Copyright Statement | © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license. (http://creativecommons.org/licenses/by/4.0/) |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.patcog.2019.04.002 |
Web of Science identifier | WOS:000472697800004 |
Language | English |
https://repository.mdx.ac.uk/item/88z00
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