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Abstract

In this paper, we present 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 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 about 20% without significant 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., a 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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Robust 360-8PA: Redesigning The Normalized 8-point Algorithm for 360-FoV Images

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


Paper Code 
                @misc{solarte2021robust,
                    title={Robust 360-8PA: Redesigning The Normalized 8-point Algorithm for 360-FoV Images}, 
                    author={Bolivar Solarte and Chin-Hsuan Wu and Kuan-Wei Lu and Min Sun and Wei-Chen Chiu and Yi-Hsuan Tsai},
                    year={2021},
                    eprint={2104.10900},
                    archivePrefix={arXiv},
                    primaryClass={cs.CV}
              }