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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.|
|Electronic Publication Date:||9-Nov-2017|
|Citation:||Sengupta, Soumyadip, Amir, Tal, Galun, Meirav, Goldstein, Tom, Jacobs, David W, Singer, Amit, Basri, Ronen. (2017). A New Rank Constraint on Multi-view Fundamental Matrices, and its Application to Camera Location Recovery. 30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017), 2413 - 2421. doi:10.1109/CVPR.2017.259|
|Pages:||2413 - 2421|
|Type of Material:||Conference Article|
|Journal/Proceeding Title:||30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017)|
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