Abstract. 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 that can be easily interpreted in terms of camera poses. While our main focus is on theoretical aspects, we include applications to optimization problems in computer vision.
Recent work explored the properties of the manifold of essential matrices REF .
The Space of Essential Matrices as a Riemannian Quotient Manifold
SIAM J. Imaging Sciences
SIAM J. Imaging Sciences
Mathematics Computer Science