Optimal Essential Matrix Estimation via Inlier-Set Maximization

In this paper, we extend the globally optimal “rotation space search” method [11] to essential matrix estimation in the presence of feature mismatches or outliers. The problem is formulated as inlier-set cardinality maximization, and solved via branch-and-bound global optimization which searches the entire essential manifold formed by all essential matrices. Our main contributions include an explicit, geometrically meaningful essential manifold parametrization using a 5D direct product space of a solid 2D disk and a solid 3D ball, as well as efficient closed-form bounding functions. Experiments on both synthetic data and real images have confirmed the efficacy of our method. The method is mostly suitable for applications where robustness and accuracy are paramount. It can also be used as a benchmark for method evaluation.

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