Xfeat versus SIFT - examples of when to use which?

[AruniRC]

Firstly, thank you for your work, @guipotje et al.

There are issues like https://github.com/verlab/accelerated_features/issues/59 that show SIFT performing better than vanilla XFeat for large geometric transformations, or when there is a large scale difference in objects being matched between images (https://github.com/verlab/accelerated_features/issues/58).

1. In this case, what are the cases where, in practice, one should prefer XFeat over SIFT? Can you share more examples of cases and some prescriptive use cases where XFeat surpasses SIFT? For example, maybe in cases of motion blur in videos, or lighting inconstancy, I am guessing XFeat may be better than SIFT - could you please share such analysis, if you have done this. I am particularly mentioning SIFT since it is a de facto standard for non-earned features.
2. Steerable-XFeat (https://github.com/verlab/accelerated_features/issues/32) - do you have any plans to integrate the Steerers work into the Xfeat codebase with pretrained networks?

Thank you.