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.
- Je Hyeong Hong (jhh37@cantab.net)
- Christopher Zach (christopher.m.zach@gmail.com)
The main paper file is included in the ''Documents'' folder. (The corresponding supplementary document will be uploaded soon.)
URL: https://youtu.be/1Ef441Ki4sI
This video shows iterative visualizations of the bundle adjustment strategy using pOSE from arbitrary cameras and 3D points. (3 datasets from Carl Olsson shown here.) For this strategy to work, sufficiently cleaned 2D point tracks (can have some outliers) are required as well as camera intrinsics.
- We thank Roberto Cipolla for the travel support.
- The conference travel was supported by Roberto Cipolla, Toshiba Research Europe and Christ's College.