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arXiv · 1702.03023

A New Rank Constraint on Multi-view Fundamental Matrices, and its Application to Camera Location Recovery

Soumyadip Sengupta·Tal Amir·Meirav Galun·Tom Goldstein·David W. Jacobs·Amit Singer·Ronen Basri
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Abstract

Accurate estimation of camera matrices is an important step in structure from motion algorithms. In this paper we introduce a novel rank constraint on collections of fundamental matrices in multi-view settings. We show that in general, with the selection of proper scale factors, a matrix formed by stacking fundamental matrices between pairs of images has rank 6. Moreover, this matrix forms the symmetric part of a rank 3 matrix whose factors relate directly to the corresponding camera matrices. We use this new characterization to produce better estimations of fundamental matrices by optimizing an L1-cost function using Iterative Re-weighted Least Squares and Alternate Direction Method of Multiplier. We further show that this procedure can improve the recovery of camera locations, particularly in multi-view settings in which fewer images are available.

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Soumyadip Sengupta, Tal Amir, Meirav Galun, Tom Goldstein, David W. Jacobs, Amit Singer, Ronen Basri. 2017-02-10. A New Rank Constraint on Multi-view Fundamental Matrices, and its Application to Camera Location Recovery. https://arxiv.org/abs/1702.03023

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