Element-Wise Factorization for N-View Projective Reconstruction View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2010

AUTHORS

David Hutchison , Takeo Kanade , Josef Kittler , Jon M. Kleinberg , Friedemann Mattern , John C. Mitchell , Moni Naor , Oscar Nierstrasz , C. Pandu Rangan , Bernhard Steffen , Madhu Sudan , Demetri Terzopoulos , Doug Tygar , Moshe Y. Vardi , Gerhard Weikum , Yuchao Dai , Hongdong Li , Mingyi He

ABSTRACT

Sturm-Triggs iteration is a standard method for solving the projective factorization problem. Like other iterative algorithms, this method suffers from some common drawbacks such as requiring a good initialization, the iteration may not converge or only converge to a local minimum, etc. None of the published works can offer any sort of global optimality guarantee to the problem. In this paper, an optimal solution to projective factorization for structure and motion is presented, based on the same principle of low-rank factorization. Instead of formulating the problem as matrix factorization, we recast it as element-wise factorization, leading to a convenient and efficient semi-definite program formulation. Our method is thus global, where no initial point is needed, and a globally-optimal solution can be found (up to some relaxation gap). Unlike traditional projective factorization, our method can handle real-world difficult cases like missing data or outliers easily, and all in a unified manner. Extensive experiments on both synthetic and real image data show comparable or superior results compared with existing methods. More... »

PAGES

396-409

Book

TITLE

Computer Vision – ECCV 2010

ISBN

978-3-642-15560-4
978-3-642-15561-1

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-15561-1_29

DOI

http://dx.doi.org/10.1007/978-3-642-15561-1_29

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1044096350


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