Really Need Recursion? Revisiting One-pass Registration with Multi-level Multi-space Contextual Guidance

arXiv:2610.22423v1 Announce Type: cross Abstract: Deformable image registration is a fundamental challenge in medical image analysis. Recent deep learning-based methods typically adopt either one-pass architectures that complete registration in a single network iteration, or recursive architectures that refine registration through multiple passes. While recursive methods often improve accuracy, they incur substantial computational overhead and slower inference speed. In this paper, we challenge the necessity of recursion for high-performance deformable registration and propose M2CG, a one-pass

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