Gate3R: Training-Free Prior Gating for Feed-Forward 3D Reconstruction

Feed-forward reconstruction models such as DUSt3R and its successors recover geometry from unposed images in an end-to-end manner. Some of these models are prior-conditioned, taking optional geometric priors such as camera intrinsics, relative pose, and depth to further improve reconstruction. In practice, however, such priors are often inaccurate or corrupted, and conditioning on them can produce reconstructions worse than those from images alone. We propose Gate3R, a training-free method to discard unreliable priors at test time and fall back to image-only reconstruction. It combines the residual between the prior and the prediction with the frozen model's attention entropy to decide whether each prior is usable. On two prior-conditioned models, Pow3R and MapAnything, Gate3R outperforms both always keeping and always discarding priors in most single-prior settings and in all multi-prior settings across six datasets.