CRFT: Consistent-Recurrent Feature Flow Transformer for Cross-Modal Image Registration

• URL: http://arxiv.org/abs/2604.05689v1
• Date: Tue, 07 Apr 2026 10:40:27 GMT
• Title: CRFT: Consistent-Recurrent Feature Flow Transformer for Cross-Modal Image Registration
• Abstract summary: Consistent-Recurrent Feature Flow Transformer (CRFT) is a unified coarse-to-fine framework based on feature flow learning for robust cross-modal image registration.<n>CRFT learns a modality-independent feature flow representation within a transformer-based architecture that jointly performs feature alignment and flow estimation.
• Score: 6.361810913107242
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present Consistent-Recurrent Feature Flow Transformer (CRFT), a unified coarse-to-fine framework based on feature flow learning for robust cross-modal image registration. CRFT learns a modality-independent feature flow representation within a transformer-based architecture that jointly performs feature alignment and flow estimation. The coarse stage establishes global correspondences through multi-scale feature correlation, while the fine stage refines local details via hierarchical feature fusion and adaptive spatial reasoning. To enhance geometric adaptability, an iterative discrepancy-guided attention mechanism with a Spatial Geometric Transform (SGT) recurrently refines the flow field, progressively capturing subtle spatial inconsistencies and enforcing feature-level consistency. This design enables accurate alignment under large affine and scale variations while maintaining structural coherence across modalities. Extensive experiments on diverse cross-modal datasets demonstrate that CRFT consistently outperforms state-of-the-art registration methods in both accuracy and robustness. Beyond registration, CRFT provides a generalizable paradigm for multimodal spatial correspondence, offering broad applicability to remote sensing, autonomous navigation, and medical imaging. Code and datasets are publicly available at https://github.com/NEU-Liuxuecong/CRFT.

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