XPos3R: Cross-Modal Transformer for Intraoperative 2D/3D Registration

Intraoperative 2D/3D registration, which aligns live X-rayimages with preoperative volumes, is essential for image-guided interven-tions. Previous regression-based methods suffer from limited generaliza-tion, thus requiring time-consuming patient-specific retraining. Inspiredby recent geometry foundation models such as DUSt3R, we proposeXPos3R, a generalizable pose regression method that eliminates preop-erative preparation. Unlike existing geometry models designed for homo-geneous inputs, XPos3R extends this paradigm to multi-modal inputs,namely 2D X-rays and 3D volumes. Specifically, we introduce an asym-metric encoder–decoder architecture that improves cross-modal featurealignment while maintaining computational efficiency. To scale trainingunder limited medical data, we adopt an anatomy-specific data cura-tion strategy and construct million-scale synthetic datasets. Evaluatedon real-world benchmarks, a single pretrained XPos3R surpasses patient-specific methods in both accuracy and robustness. With test-time opti-mization completed in seconds, it further reduces the 3D error to <4 mmand the reprojection error to <1 mm. The strong generalization, accu-racy, and efficiency of XPos3R highlight its clinical potential, while itsasymmetric framework may inspire broader cross-modal vision geometrytasks.