This work contributes an efficient algorithm to compute the relative pose problem (RPp) between calibrated cameras and certify the optimality of the solution, given a set of pair-wise feature correspondences affected ...
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This work contributes an efficient algorithm to compute the relative pose problem (RPp) between calibrated cameras and certify the optimality of the solution, given a set of pair-wise feature correspondences affected by noise and probably corrupted by wrong matches. We propose a family of certifiers that is shown to increase the ratio of detected optimal solutions. This set of certifiers is incorporated into a fast essential matrix estimation pipeline that, given any initial guess for the RPp, refines it iteratively on the product space of 3D rotations and 2-sphere. In addition, this fast certifiable pipeline is integrated into a robust framework that combines graduated non-convexity and the Black-Rangarajan duality between robust functions and line processes. We proved through extensive experiments on synthetic and real data that the proposed framework provides a fast and robust relative pose estimation. We make the code publicly available https://***/mergarsal/***.
In this paper we present the first fast optimality certifier for the non-minimal version of the Relative Pose prob-lem for calibrated cameras from epipolar constraints. The proposed certifier is based on Lagrangian du...
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In this paper we present the first fast optimality certifier for the non-minimal version of the Relative Pose prob-lem for calibrated cameras from epipolar constraints. The proposed certifier is based on Lagrangian duality and relies on a novel closed-form expression for dual points. We also leverage an efficient solver that performs local optimization on the manifold of the original problem's non-convex domain. The optimality of the solution is then checked via our novel fast certifier. The extensive conducted experiments demonstrate that, despite its simplicity, this certifiable solver performs excellently on synthetic data, repeatedly attaining the (certified a posteriori) optimal solution and shows a satisfactory performance on real data. (c) 2021 Elsevier B.V. All rights reserved.
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