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This paper presents a novel preconditioning strategy for the classic 8-pointalgorithm (8-PA) for estimating an essential matrix from 360-FoV images (i.e.,equirectangular images) in spherical projection. To alleviate the effect ofuneven key-feature distributions and outlier correspondences, which canpotentially decrease the accuracy of an essential matrix, our method optimizesa non-rigid transformation to deform a spherical camera into a new spatialdomain, defining a new constraint and a more robust and accurate solution foran essential matrix. Through several experiments using random synthetic points,360-FoV, and fish-eye images, we demonstrate that our normalization canincrease the camera pose accuracy by about 20% without significantly overheadthe computation time. In addition, we present further benefits of our methodthrough both a constant weighted least-square optimization that improvesfurther the well known Gold Standard Method (GSM) (i.e., the non-linearoptimization by using epipolar errors); and a relaxation of the number ofRANSAC iterations, both showing that our normalization outcomes a morereliable, robust, and accurate solution.

Robust 360-8PA: Redesigning The Normalized 8-point Algorithm for 360-FoV Images