MADCrowner: Margin Aware Dental Crown Design with Template Deformation and Refinement
#MADCrowner #dental crown design #margin aware #template deformation #refinement #digital dentistry #CAD/CAM
📌 Key Takeaways
- MADCrowner is a new method for designing dental crowns with focus on margin accuracy.
- It uses template deformation and refinement to improve crown fit and aesthetics.
- The approach aims to enhance digital dentistry workflows for better patient outcomes.
- Research highlights potential for more efficient and precise dental restorations.
📖 Full Retelling
arXiv:2603.04771v1 Announce Type: cross
Abstract: Dental crown restoration is one of the most common treatment modalities for tooth defect, where personalized dental crown design is critical. While computer-aided design (CAD) systems have notably enhanced the efficiency of dental crown design, extensive manual adjustments are still required in the clinic workflow. Recent studies have explored the application of learning-based methods for the automated generation of restorative dental crowns. Ne
🏷️ Themes
Dental Technology, CAD/CAM
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Original Source
--> Computer Science > Computer Vision and Pattern Recognition arXiv:2603.04771 [Submitted on 5 Mar 2026] Title: MADCrowner: Margin Aware Dental Crown Design with Template Deformation and Refinement Authors: Linda Wei , Chang Liu , Wenran Zhang , Yuxuan Hu , Ruiyang Li , Feng Qi , Changyao Tian , Ke Wang , Yuanyuan Wang , Shaoting Zhang , Dimitris Metaxas , Hongsheng Li View a PDF of the paper titled MADCrowner: Margin Aware Dental Crown Design with Template Deformation and Refinement, by Linda Wei and 11 other authors View PDF HTML Abstract: Dental crown restoration is one of the most common treatment modalities for tooth defect, where personalized dental crown design is critical. While computer-aided design systems have notably enhanced the efficiency of dental crown design, extensive manual adjustments are still required in the clinic workflow. Recent studies have explored the application of learning-based methods for the automated generation of restorative dental crowns. Nevertheless, these approaches were challenged by inadequate spatial resolution, noisy outputs, and overextension of surface reconstruction. To address these limitations, we propose \totalframework, a margin-aware mesh generation framework comprising CrownDeformR and CrownSegger. Inspired by the clinic manual workflow of dental crown design, we designed CrownDeformR to deform an initial template to the target crown based on anatomical context, which is extracted by a multi-scale intraoral scan encoder. Additionally, we introduced \marginseg, a novel margin segmentation network, to extract the cervical margin of the target tooth. The performance of CrownDeformR improved with the cervical margin as an extra constraint. And it was also utilized as the boundary condition for the tailored postprocessing method, which removed the overextended area of the reconstructed surface. We constructed a large-scale intraoral scan dataset and performed extensive experiments. The proposed method significantly out...
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