The space of essential matrices as a Riemannian quotient manifold
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Citation (published version)R Tron, K Daniilidis. (2017) "The Space of Essential Matrices as a Riemannian Quotient Manifold." SIAM Journal on Imaging Sciences, 10(3), DOI: 10.1137/16M1091332
The essential matrix, which encodes the epipolar constraint between points in two projective views, is a cornerstone of modern computer vision. Previous works have proposed different characterizations of the space of essential matrices as a Riemannian manifold. However, they either do not consider the symmetric role played by the two views, or do not fully take into account the geometric peculiarities of the epipolar constraint. We address these limitations with a characterization as a quotient manifold which can be easily interpreted in terms of camera poses. While our main focus in on theoretical aspects, we include applications to optimization problems in computer vision.
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