Optimize overlapping chunks

[tkwasnitschka]

I have 25 chunks in a grid that overlap to each of their neighbors, i.e. they partly contain the same cameras. Even though I ran them all with the same intrinsic parameters, overlapping areas dont perfectly match since the calibration is imperfect and cannot be done any better. Thus, the residual misfit was pushed into the extrinsics.

So, how do I
- optimize chunks relative to each other so that overlapping areas actually overlap
- merge chunks so that there are no double images (-> do I really have to manually pick the cameras or find them with a script?)

-> I cannot just run all chunks in one optimization step as each chunk already has 10k images, and there are 25 chunks.
Thanks!
Tom

[tkwasnitschka]

Bump...
Am I really the only one who needs to optimize several very large chunks relative to each other?

[Alexey Pasumansky]

Hello Tom,

If it is not possible to align all the images in the same chunk and optimize it as a single model, I can suggest the following approach:
create markers in common points related to the overlapping areas in different chunks (use the same marker label for the point related to the same real world point, even if the point is marked in different chunks), then acquire estimated values for each marker from every chunk and average the coordinate information. After that load the same averaged coordinates for all the markers to each chunk and optimize each chunk individually. Such operation may be performed in a few iterations to achieve better accuracy.

Removing the duplicated cameras is possible using Python, providing that the cameras are created from the same source image file. However, note that chunk alignment and chunk merging operations (unless Merge Tie Points option is enabled) do not create the correspondences for the images between different chunks. It means that if you are building the depth maps in the merged chunk, they will be basing on the image pairs from the same chunk and some artifacts in the overlapping areas may be still observed.