RBE-Flow:Recurrent Bayesian Estimation on Feature Manifolds for Cross-Modal Registration

Cross-modal image registration is essential for multi-sensorperception but remains fundamentally challenging due to severe non-linear radiometric discrepancies and geometric distortions. Existing de-terministic matching methods lack uncertainty awareness, struggling tonavigate the resulting highly non-convex optimization landscape and fre-quently accumulating errors in ambiguous regions. In this paper, wepropose RBE-Flow, a novel framework that reformulates dense cross-modal flow estimation as a closed-loop recurrent Bayesian estimationproblem on learned feature manifolds. Diverging from standard feed-forward regression, RBE-Flow establishes a robust self-correcting mech-anism by deeply coupling feature-metric non-linear optimization withprobabilistic state updates. Specifically, a Recurrent Manifold Optimiza-tion (RMO) block iteratively generates flow observations and their as-sociated uncertainties, which are then optimally assimilated into theprior state via an Uncertainty-Adaptive Probabilistic Update (UAPU)using deterministic sigma-point projection. Crucially, the resulting cal-ibrated posterior covariance is fed back to adaptively regularize thedamping of subsequent optimization steps, allowing the system to mod-ulate its convergence based on predictive confidence. To ensure stableprobabilistic training, we introduce a hybrid supervision scheme featur-ing a geometry-aware rectified NLL loss that structurally prevents vari-ance collapse. Extensive experiments on challenging OSdataset, WHU-OPT-SAR, and RoadScene benchmarks demonstrate that RBE-Flowconsistently achieves state-of-the-art performance, outperforming exist-ing methods by a significant margin, particularly under strict sub-pixelcriteria. Project page: https://github.com/NEU-Liuxuecong/RBE-Flow