Efficient Iterative Solution to M-View Projective Reconstruction Problem

We propose an efficient solution to the general M-view projective reconstruction problem, using matrix factorization and iterative least squares. The method can accept input with missing data, meaning that not all points are necessarily visible in all views. It runs much faster than the often-used non-linear minimization method, while preserving the accuracy of the latter. The key idea is to convert the minimization problem into a series of weighted least squares sub-problems with drastically reduced matrix sizes. Additionally, we show that good initial values can always be obtained. Experimental results on both synthetic and real data are presented. Potential applications are also demonstrated.