Here’s a screengrab of a test alignment of 2 image sets shot a couple of weeks apart (different colour adjustment for me to identify each set) There are 2 problem areas in this set which were interesting.
When aligning just a subset of images in region 1 everything aligns OK, but when there are a larger number of images the distribution of the tie points seems to change a bit resulting in a drifting of the alignment. The two problem areas do have something in common relating to the positioning of cameras and this seems to be contributing to the alignment issue.
Region 1 is a short section of images containing a single camera path that travels along the footpath and then returns back to the start. The alignment drifting can be fixed in this instance by renumbering the images so that the misaligned images are listed just after the overlapping images from the other end of the camera path, but in practice I’ll adjust my shooting strategy to avoid this.
Region 2 has a similar problem so at least I know that one method of going around a corner works and one doesn’t (work well). A second alignment will join the other small component which consists of a small diversion of the camera path and the return path.
Masking the area outside the circular image does seem to make a difference, although that is already a part of my pre-processing workflow along with masking the white sky. I was masking inside of the circular image to remove the crappy outer area with lots of chromatic aberration and I’ve added a crop to just outside the image circle now, which reduce the fov of the image file to about 170°… again, seems to help but need to run a few more to verify.