A subspace method for projective reconstruction from multiple images with missing data

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.