SETSUNET: Shared Consensus Geometry for Few-Step Recovery

SETSUNET is a shared learned optimizer for few-step geometric and visual recovery. It learns search directions from local gradient and curvature geometry, projects them to complement a measured base solve, and coordinates their contribution across observation views. This construction allows the same graph network to operate on image variables, transformations and graph states. We establish whitening equivariance for the learned directions and numerical solve, and use it to accelerate the complete solver with Cholesky whitening while preserving physical updates and trained weights. Experiments show better recovery than HSLM, Ceres LM, Dogbox and VeLO on multiframe alignment, stereo and biased-graph estimation, with competitive results on pairwise alignment and image restoration. Learned directions also improve recovery over equal-rank random completion. Together, these results demonstrate that learned search geometry can serve as a reusable component for accurate recovery under small update budgets.