Hello, thank you for your wonderful ideas!
I wonder to know why the homography adaptation algorithm works. Personally, I think the reason may be that the types of point-line features contained in the synthetic shapes dataset are mainly T-shaped, L-shaped and other links, while the corners of rooms and furniture in the real image are mostly of this shape, so the features contained in the synthetic shapes are a subset of the real world, and the models trained on it can also detect some features of the real world.
But how to explain it be more specific? Or are there any papers about it? Thanks again.