A family of globally optimal branch-and-bound algorithms for 2D–3D correspondence-free registration

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

A family of globally optimal branch-and-bound algorithms for 2D–3D correspondence-free registration

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

Exploiting Structural Constraints in Image Pairs

Ph.DDOCTOR OF PHILOSOPH

Two View Geometry Estimation with Outliers

We study the relative orientation problem for two calibrated cameras with outliers from the feature matching. In recent years there has been a growing interest in optimal algorithms for computer vision. Most people agree that to get accurate solutions to multiview geometry problems, an appropriate norm of the reprojection errors should be minimized. To this end local as well as global optimization methods have been employed. To handle outliers though, heuristic methods still dominate the field. In this paper we address the problem of estimating relative orientation from uncertain feature correspondences. We formulate this task as an optimization problem and propose a branchand-bound algorithm to find the optimal set of correspondences as well as the optimal relative orientation. The approach is based on geometric constraints for pairs of correspondences. The experimental results are promising, especially for omnidirectional cameras. An implementation of the algorithm is also made publicly available to facilitate further research