A New Rank Constraint on Multi-view Fundamental Matrices, and its Application to Camera Location Recovery
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)(2017)
摘要
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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关键词
multi-view settings,camera matrices,multi-view fundamental matrices,iterative re-weighted least squares,alternate direction method of multiplier,L1-cost function,motion algorithms,novel rank constraint,camera location recovery,rank 3 matrix,proper scale factors
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