@inproceedings{dce2d2922c784514bc9b549cd5a1ff76,
title = "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.",
author = "Soumyadip Sengupta and Tal Amir and Meirav Galun and Tom Goldstein and Jacobs, {David W.} and Amit Singer and Ronen Basri",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 ; Conference date: 21-07-2017 Through 26-07-2017",
year = "2017",
month = nov,
day = "6",
doi = "10.1109/CVPR.2017.259",
language = "الإنجليزيّة",
isbn = "9781538604571",
series = "Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017",
pages = "2413--2421",
booktitle = "Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017",
}