A New Rank Constraint on Multi-view Fundamental Matrices, and its Application to Camera Location Recovery
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.
Keywords
Cite
@article{arxiv.1702.03023,
title = {A New Rank Constraint on Multi-view Fundamental Matrices, and its Application to Camera Location Recovery},
author = {Soumyadip Sengupta and Tal Amir and Meirav Galun and Tom Goldstein and David W. Jacobs and Amit Singer and Ronen Basri},
journal= {arXiv preprint arXiv:1702.03023},
year = {2017}
}