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Excellent work, thanks for the detailed paper and prompt model release!
I think that the method systematically underestimates focal length on ultrawide images. For example, here is an iPhone 13 Pro ultrawide image:
MoGe estimates a focal length of 1767.9873 pixels, or about 97 degrees hFOV. But the GT hFOV for this device is about 108 degrees. In testing I have done, there seems to be a pretty consistent bias to underestimate on these.
I don't know of a nice public dataset of images of this kind to show this to you. But I can produce a related behavior by taking a public dataset with known intrinsics (Cambridge Landmarks Dataset) and cropping in the vertical. As the crop gets more extreme, MoGe's estimate gets biased towards being big:
BTW, it's not reproduced here, but taking a cambridge image and cropping the per-pixel pointcloud, rather than the image itself, does not reproduce this behavior. So, I think it is not a problem with the LM solver step.
I wonder if you have any comment or ideas for improving the estimation accuracy here? Is it just that these weird crops/FOVs are well outside the training distribution?
The text was updated successfully, but these errors were encountered:
RichardBowenGM
changed the title
Intrinsics estimation on
Intrinsics estimation on ultrawide FOV
Dec 19, 2024
Hi,
Excellent work, thanks for the detailed paper and prompt model release!
I think that the method systematically underestimates focal length on ultrawide images. For example, here is an iPhone 13 Pro ultrawide image:
MoGe estimates a focal length of 1767.9873 pixels, or about 97 degrees hFOV. But the GT hFOV for this device is about 108 degrees. In testing I have done, there seems to be a pretty consistent bias to underestimate on these.
I don't know of a nice public dataset of images of this kind to show this to you. But I can produce a related behavior by taking a public dataset with known intrinsics (Cambridge Landmarks Dataset) and cropping in the vertical. As the crop gets more extreme, MoGe's estimate gets biased towards being big:
(I attached a jupyter notebook you can use to reproduce these plots: cambridge intrinsics.ipynb.zip).
BTW, it's not reproduced here, but taking a cambridge image and cropping the per-pixel pointcloud, rather than the image itself, does not reproduce this behavior. So, I think it is not a problem with the LM solver step.
I wonder if you have any comment or ideas for improving the estimation accuracy here? Is it just that these weird crops/FOVs are well outside the training distribution?
The text was updated successfully, but these errors were encountered: