Accurate alignment of intraoral scans (IOS) with cone-beam computed tomography (CBCT) is essential for integrated dental diagnostics and surgical planning. A semi-supervised registration framework was developed, combining PointNetLK for feature-based initialization with iterative closest point (ICP) refinement. Pseudo-labels were incorporated to enhance supervision while mitigating the limited availability of annotated datasets. Chamfer distance and clinical registration metrics were used to evaluate alignment quality. Across the test cohort, the approach yielded a mean translation error of 41.67 mm and a mean rotation error of 33.96 \(^{\circ }\) , highlighting the challenge of partial-arch fusion. Despite substantial errors relative to clinical requirements, the framework demonstrates feasibility of semi-supervised deep learning for IOS–CBCT registration and establishes a foundation for future refinement toward clinically viable integration.