  This paper presents a novel preconditioning strategy for the classic 8-point
algorithm (8-PA) for estimating an essential matrix from 360-FoV images (i.e.,
equirectangular images) in spherical projection. To alleviate the effect of
uneven key-feature distributions and outlier correspondences, which can
potentially decrease the accuracy of an essential matrix, our method optimizes
a non-rigid transformation to deform a spherical camera into a new spatial
domain, defining a new constraint and a more robust and accurate solution for
an essential matrix. Through several experiments using random synthetic points,
360-FoV, and fish-eye images, we demonstrate that our normalization can
increase the camera pose accuracy by about 20% without significantly overhead
the computation time. In addition, we present further benefits of our method
through both a constant weighted least-square optimization that improves
further the well known Gold Standard Method (GSM) (i.e., the non-linear
optimization by using epipolar errors); and a relaxation of the number of
RANSAC iterations, both showing that our normalization outcomes a more
reliable, robust, and accurate solution.
