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A Generalized Projective Reconstruction Theorem and Depth Constraints for Projective Factorization

Seyed Behrooz Nasihatkton (Institutionen för signaler och system, Bildanalys och datorseende) ; R. Hartley ; J. Trumpf
International Journal of Computer Vision (0920-5691). Vol. 115 (2015), 2, p. 87-114.
[Artikel, refereegranskad vetenskaplig]

This paper presents a generalized version of the classic projective reconstruction theorem which helps to choose or assess depth constraints for projective depth estimation algorithms. The theorem shows that projective reconstruction is possible under a much weaker constraint than requiring all estimated projective depths to be nonzero. This result enables us to present classes of depth constraints under which any reconstruction of cameras and points projecting into given image points is projectively equivalent to the true camera-point configuration. It also completely specifies the possible wrong configurations allowed by other constraints. We demonstrate the application of the theorem by analysing several constraints used in the literature, as well as presenting new constraints with desirable properties. We mention some of the implications of our results on iterative depth estimation algorithms and projective reconstruction via rank minimization. Our theory is verified by running experiments on both synthetic and real data.

Nyckelord: Multiple view geometry, Projective reconstruction, Projective reconstruction theorem, Projective factorization, Projective depths, Constraints on projective depths

Denna post skapades 2015-10-23. Senast ändrad 2016-05-24.
CPL Pubid: 224725


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Institutioner (Chalmers)

Institutionen för signaler och system, Bildanalys och datorseende (2013-2017)



Chalmers infrastruktur