Factorization with missing data for 3D structure recovery

Matrix factorization methods are now widely used to recover 3D structure from 2D projections [C. Tomasi and T. Kanade. International Journal of Computer Vision, 9(2), 1992] . In this practice, the observation matrix to be factored out has missing data, due to the limited field of view and the occlusion that occur in real video sequences. In opposition to the optimality of the SVD to factor out matrices without missing entries, the optimal solution for the missing data case is not known. In R.F.C. Guerreiro and P.M.Q. Aguiar [IEEE ICIP, New York, USA, September 2002] we introduced suboptimal algorithms that proved to be more efficient than previous approaches to the factorization of matrices with missing data. In this paper we make an experimental analysis of the algorithms of R.F.C. Guerreiro and P.M.Q. Aguiar [IEEE ICIP, New York, USA, September 2002] and demonstrate their performance in virtual reality and video compression applications. We conclude that these algorithms are adequate to the amount of missing entries that may occur when processing real videos; robust to the typical level of noise in practical applications; and computationally as simple as the factorization of matrices without missing entries.