GeoCM-Pose: geometry-aware monocular dental 2D/3D registration benchmarked against reference-assisted methods

Objective.To develop GeoCM-Pose, a geometry-aware monocular dental 2D/3D registration method that predicts metric model-to-camera 6DoF pose from one image under weak texture, repetitive anatomy and partial visibility.Approach.GeoCM-Pose comprises cross-modal adaptation (CMA) and geometry-aware pose regression (GAPR). CMA uses fixed-Gaussian frequency separation and a high-frequency structure-preservation module with multi-scale edge extraction, spatial-channel gating and cross-resolution refinement. GAPR uses a ResNet-50/U-Net to predict dense object-coordinate maps; hierarchical geometric feature aggregation and geometry-aware channel refinement feed decoupled unit-quaternion and translation heads. Joint 20-point pose-matching and dense-coordinate losses train the network. The study comprised 30in vitrotraining cases, fivein vivovalidation cases, fivein vivointernal-test cases, 10 fully held-out Teeth3DS+ dental surface models and a 200-frame benchmark generated from a single three-dimensional dental model.Main Results.GAPR/V0 generated pose estimates for all 200 query frames, with median reprojection, rotation and translation-vector errors of 2.156 px, 1.252° and 0.653 mm, respectively, and a <5 px pass rate of 91.5% across all attempts. It used no inference-time reference-image bank. At R01, scale-invariant feature transform and ORB generated 85/200 and 89/200 valid estimates, with <5 px all-attempt pass rates of 40.5% and 40.0%; at R20, each generated 197/200 valid estimates, with pass rates of 98.0% and 95.5%. Thus, conventional performance increased with the available reference bank, whereas GAPR/V0 provided complete query coverage from a single image without inference-time reference images. Median GAPR/V0 inference latency, including image I/O, was 11.459 ms.Significance.GeoCM-Pose integrates CMA, dense object-coordinate supervision and metric pose regression for monocular dental 2D/3D registration. Further case-level evaluation using intraoral endoscopic images acquired from independent patient cases and additional image domains, together with robot-integrated experiments, is required to assess generalisation and system-level performance.