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Christian Rupprecht explains their interpretability research in 3D computer vision, testing if (and where in the model) multi-view transformers like VGGT, DepthAnything 3, and DUSt3R use point/patch correspondences to make sense of 3D scene geometry.

Chris Offner

4,745 subscribers

74,761 views • 6 months ago •via X (Twitter)

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9 Comments

Chris Offner6 months ago

Full talk:

Chris Offner6 months ago

Paper:

Chris Offner6 months ago

Q: Are these truly geometric correspondences or just the semantic correspondences we get from the DINO encoder, or even from diffusion models like Stable Diffusion? A: Early layers are good at semantic correspondences but bad at geometric ones, and vice versa for later layers.

Chris Offner6 months ago

Related:

Mazeyar6 months ago

Can we extract these correspondences from VGGT then run classic bundle adjustment?

Chris Offner6 months ago

Possibly, but why would you want to do that? I'd expect them to be much less precise than, e.g., RoMA 2, not least because they're only patch-level (not pixel-level) correspondences.

Mazeyar6 months ago

Im thinking they're less precise too, but maybe more robust

Kemajou Charlesquin6 months ago

Interesting!

Prateeth Rao6 months ago

That's a very good analysis to verify if models are truly able to learn epipolar geometry for monocular VSLAM. Every matching/depth estimation model that I have tested, produce few epipolar correspondences until explicitly trained to follow geometry.

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