PanoREG: From Pixel Matching to View Reasoning for Robust Panoramic Registration

Conventional panoramic registration typically follows a match-then-RANSAC paradigm, establishing pixel-level association followed by RANSAC-style pose estimation. However, under challenging conditions such as low overlap and visual ambiguity, high-quality correspondences often become scarce, leading to limited robustness. This paper proposes PanoREG, a novel viewpose-then-Bayesian paradigm for robust, training-free panoramic registration. Unlike matching-dependent paradigm, our key idea is to recover the relative pose between two panoramas by associating and reasoning over pose estimates from their decomposed sub-views. Specifically, PanoREG comprises two core steps: calibrated view-pose association and robust Bayesian pose estimation. Calibrated view-pose association constructs multiple panorama-level pose hypotheses from sub-view pose predictions. To this end, we decompose each panorama into calibrated perspective views and employ a 3D foundation model to estimate per-view poses. Cross-panorama view-pose pairs are then associated through the predefined view-to-panorama extrinsics, yielding multiple noisy hypotheses of the panorama-level relative pose. Given these hypotheses, robust Bayesian pose estimation defines an outlier-robust Cauchy likelihood over them, and derives the posterior of the panorama pose via Bayesian inference, enabling robust maximum a posteriori (MAP) pose inference by suppressing highly inconsistent hypotheses. In particular, beyond pose estimation, we further exploit the posterior uncertainty to quantify the confidence of the MAP pose estimate, enabling confidence-controlled adaptive verification, in which reliable MAP estimates are directly accepted, whereas low-confidence cases trigger additional point-level geometric refinement. Extensive experiments on challenging panoramic benchmarks demonstrate that PanoREG substantially outperforms existing state-of-the-art methods, with particularly strong robustness under low-overlap conditions.