Landmark-Guided Coarse-to-Fine Registration of Intraoral Scans and Cone-Beam CT

Accurate registration of Intraoral Scans (IOS) with Cone-Beam Computed Tomography (CBCT) enables integration of dental crown surfaces with the tooth roots and the surrounding alveolar bone. However, IOS-CBCT registration remains challenging because of limited anatomical overlap, cross-modal differences, and unreliable correspondences. Task 2 of the MICCAI STS 2026 Challenge formulates this setting as a semi-supervised rigid registration problem, in which IOS meshes must be aligned with their corresponding CBCT volumes. For this task, we propose a landmark-driven coarse-to-fine framework in which modality-specific networks predict corresponding dental landmarks from the IOS mesh and CBCT volume. A confidence-weighted RANSAC-Kabsch procedure estimates a robust initial transformation, later refined using point-to-plane Iterative Closest Point. On the challenge validation set, the method achieved a Mean Translation Error of 4.988 mm and a Mean Rotation Error of 1.421°, ranking first on the registration leaderboard at the time of evaluation. The code is available on GitHub: https://github.com/AImageLab-zip/L2L-Registration