Abstract
Bundle adjustment is a nonlinear refinement method for
camera poses and 3D structure requiring sufficiently good
initialization. In recent years, it was experimentally observed that useful minima can be reached even from arbitrary initialization for affine bundle adjustment problems
(and fixed-rank matrix factorization instances in general).
The key success factor lies in the use of the variable projection (VarPro) method, which is known to have a wide basin
of convergence for such problems. In this paper, we propose
the Pseudo Object Space Error (pOSE), which is an objective with cameras represented as a hybrid between the affine
and projective models. This formulation allows us to obtain
3D reconstructions that are close to the true projective reconstructions while retaining a bilinear problem structure
suitable for the VarPro method. Experimental results show
that using pOSE has a high success rate to yield faithful 3D
reconstructions from random initializations, taking one step
towards initialization-free structure from motion