SPPReg: Structure-Aware Partial-to-Complete Point Cloud Registration in Computer-Assisted Orthopedic Surgery

Accurate alignment between partial intraoperative and complete preoperative bone surfaces is essential for navigation in computer-assisted orthopedic surgery. However, this task remains challenging due to low surface overlap, significant initial pose discrepancies, and noise inherent in intraoperative data, which often compromise the effectiveness of existing registration methods. To address these challenges, we propose a structure-aware partial-to-complete point cloud registration framework, named SPPReg, for accurate intraoperative-to-preoperative alignment, featuring a two-stage coarse-to-fine design. In the coarse alignment stage, a point completion network reconstructs missing structures in partial scans and leverages global geometric features to facilitate initial alignment under large pose variations. For the fine registration stage, we adopt a self-attention-based feature matching strategy that constructs a feature similarity matrix to establish accurate point correspondences. To reduce uncertainty interference, we design an overlap estimation block that learns point-wise overlap scores to select representative and reliable correspondences within overlapping regions, thereby improving the accuracy of fine registration. Comparative and ablation studies on a public bone point cloud dataset demonstrate that our method outperforms existing approaches in both accuracy and robustness, highlighting its effectiveness and potential for clinical application.