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
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017)
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.
BibTeX:@inproceedings{sengupta2017new,
title={A New Rank Constraint on Multi-View Fundamental Matrices, and its Application to Camera Location Recovery},
author={Sengupta, Soumyadip and Amir, Tal and Galun, Meirav and Goldstein, Tom and Jacobs, David W and Singer, Amit and Basri, Ronen},
booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
pages={4798--4806},
year={2017}
}