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arXiv · 2104.10900

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

Bolivar Solarte·Chin-Hsuan Wu·Kuan-Wei Lu·Min Sun·Wei-Chen Chiu·Yi-Hsuan Tsai
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Abstract

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.

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Bolivar Solarte, Chin-Hsuan Wu, Kuan-Wei Lu, Min Sun, Wei-Chen Chiu, Yi-Hsuan Tsai. 2021-04-22. Robust 360-8PA: Redesigning The Normalized 8-point Algorithm for 360-FoV Images. https://arxiv.org/abs/2104.10900

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