I am stitching together multiple images with arbitrary 3D views of a planar surface. I have some estimation of which images overlap and a coarse estimate of each pairwise homography between pairs of overlapping images. However, I need to refine my homographies by minimizing the global error across all images.
I have read a few different papers with various methods for doing this, and I think the best way would be to use a non-linear optimization such as Levenberg–Marquardt, ideally in a fast way that is sparse and/or parallel.
Ideally I would like to use an existing library such as sba or pba, but I am really confused as to how to limit the calculation to just estimating the eight parameters of the homography rather than the full 3 dimensions for both camera pose and object position. I also found this handy explanation by Szeliski (see section 5.1 on page 50) but again, the math is all for a rotating camera rather than a flat surface.
How do I use L-M to minimize the global error for a set of homographies? Is there a speedy way to do this with existing bundle adjustment libraries?
Note: I cannot use methods that rely on rotation-only camera motion (such as in openCV) because those cannot accurately estimate camera poses, and I also cannot use full 3D reconstruction methods (such as SfM) because those have too many parameters which results in non-planar point clouds. I definitely need something specific to a full 8 parameter homography. Camera intrinsics don't really matter because I am already correcting those in an earlier step.
Thanks for your help!
