An Experimental Study of Projective Structure From Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Proceedings of the 24th DAGM Symposium on Pattern Recognition
Euclidean Reconstruction from Uncalibrated Views
Proceedings of the Second Joint European - US Workshop on Applications of Invariance in Computer Vision
Projective Reconstruction from Multiple Views with Minimization of 2D Reprojection Error
International Journal of Computer Vision
A column-space approach to projective reconstruction
Computer Vision and Image Understanding
Element-wise factorization for N-View projective reconstruction
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
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In this paper, we consider the problem of projective reconstruction based on the subspace method. Unlike existing subspace methods which require that all the points are visible in all views, we propose an algorithm to estimate projective shape, projective depths and missing data iteratively. All these estimation problems are formulated within a subspace framework in terms of the minimization of a single consistent objective function, hence ensuring the convergence of the iterative solution. Experimental results using both synthetic data and real images are provided to illustrate the performance of the proposed method.