CrownGen: Clinically Applicable Dental Crown Generation via Physical-Reward Generative Modeling

We present CrownGen, a fully automatic framework for clinically applicable single-crown generation from raw intraoral scans. Dental crown synthesis requires both high-fidelity anatomical morphology and precise functional relations to neighboring and opposing teeth, which are difficult to capture with reconstruction-driven generative objectives alone. CrownGen combines margin-aware latent diffusion, ambiguity-preserving morphology refinement, and reward-guided clinical applicability adaptation. The morphology generation stage produces anatomically coherent crowns with high-frequency dental structures, while the adaptation stage uses non-differentiable physical rewards from proximal contact and occlusal clearance to improve functional validity. Experiments and professional user studies demonstrate superior geometric fidelity, anatomical realism, functional validity, and clinical acceptability over existing learning-based baselines.